From fc13650dd86eaddfd29096bd9bb978f1d9439807 Mon Sep 17 00:00:00 2001 From: Siddhartha Bagaria <1929612+siddharthab@users.noreply.github.com> Date: Tue, 3 Sep 2024 06:16:01 +0000 Subject: [PATCH 001/107] Add umicollapse as an alternative to umi-tools TODO: - [ ] Benchmark paired ends mode - [ ] Include in multiqc --- modules.json | 12 + modules/nf-core/umicollapse/environment.yml | 7 + modules/nf-core/umicollapse/main.nf | 73 +++++ modules/nf-core/umicollapse/meta.yml | 63 +++++ .../nf-core/umicollapse/tests/main.nf.test | 249 ++++++++++++++++++ .../umicollapse/tests/main.nf.test.snap | 124 +++++++++ .../nf-core/umicollapse/tests/nextflow.config | 8 + .../umicollapse/tests/nextflow_PE.config | 10 + .../umicollapse/tests/nextflow_SE.config | 10 + modules/nf-core/umicollapse/tests/tags.yml | 2 + nextflow.config | 1 + nextflow_schema.json | 7 + .../utils_nfcore_rnaseq_pipeline/main.nf | 4 + .../main.nf | 55 ++++ .../meta.yml | 59 +++++ .../tests/main.nf.test | 103 ++++++++ .../tests/main.nf.test.snap | 81 ++++++ .../tests/paired-end-umis.config | 10 + .../tests/tags.yml | 2 + workflows/rnaseq/main.nf | 98 ++++--- workflows/rnaseq/nextflow.config | 49 ++-- 21 files changed, 979 insertions(+), 48 deletions(-) create mode 100644 modules/nf-core/umicollapse/environment.yml create mode 100644 modules/nf-core/umicollapse/main.nf create mode 100644 modules/nf-core/umicollapse/meta.yml create mode 100644 modules/nf-core/umicollapse/tests/main.nf.test create mode 100644 modules/nf-core/umicollapse/tests/main.nf.test.snap create mode 100644 modules/nf-core/umicollapse/tests/nextflow.config create mode 100644 modules/nf-core/umicollapse/tests/nextflow_PE.config create mode 100644 modules/nf-core/umicollapse/tests/nextflow_SE.config create mode 100644 modules/nf-core/umicollapse/tests/tags.yml create mode 100644 subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/main.nf create mode 100644 subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/meta.yml create mode 100644 subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/main.nf.test create mode 100644 subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/main.nf.test.snap create mode 100644 subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/paired-end-umis.config create mode 100644 subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/tags.yml diff --git a/modules.json b/modules.json index 3b8875999..c9ddf78df 100644 --- a/modules.json +++ b/modules.json @@ -185,6 +185,7 @@ "branch": "master", "git_sha": "46eca555142d6e597729fcb682adcc791796f514", "installed_by": [ + "bam_dedup_stats_samtools_umicollapse", "bam_dedup_stats_samtools_umitools", "bam_markduplicates_picard", "bam_sort_stats_samtools" @@ -250,6 +251,11 @@ "git_sha": "46eca555142d6e597729fcb682adcc791796f514", "installed_by": ["bedgraph_bedclip_bedgraphtobigwig"] }, + "umicollapse": { + "branch": "master", + "git_sha": "18e452a645bd56d1a539f53c7656e3d2915246f7", + "installed_by": ["bam_dedup_stats_samtools_umicollapse"] + }, "umitools/dedup": { "branch": "master", "git_sha": "0eacd714effe5aac1c1de26593873960b3346cab", @@ -274,6 +280,11 @@ }, "subworkflows": { "nf-core": { + "bam_dedup_stats_samtools_umicollapse": { + "branch": "master", + "git_sha": "ab80a04707104a4baf39341581dfbced5da05479", + "installed_by": ["subworkflows"] + }, "bam_dedup_stats_samtools_umitools": { "branch": "master", "git_sha": "46eca555142d6e597729fcb682adcc791796f514", @@ -298,6 +309,7 @@ "branch": "master", "git_sha": "0eacd714effe5aac1c1de26593873960b3346cab", "installed_by": [ + "bam_dedup_stats_samtools_umicollapse", "bam_dedup_stats_samtools_umitools", "bam_markduplicates_picard", "bam_sort_stats_samtools" diff --git a/modules/nf-core/umicollapse/environment.yml b/modules/nf-core/umicollapse/environment.yml new file mode 100644 index 000000000..8dbc65dc4 --- /dev/null +++ b/modules/nf-core/umicollapse/environment.yml @@ -0,0 +1,7 @@ +name: umicollapse +channels: + - conda-forge + - bioconda + - defaults +dependencies: + - bioconda::umicollapse=1.0.0 diff --git a/modules/nf-core/umicollapse/main.nf b/modules/nf-core/umicollapse/main.nf new file mode 100644 index 000000000..dae290e6e --- /dev/null +++ b/modules/nf-core/umicollapse/main.nf @@ -0,0 +1,73 @@ +process UMICOLLAPSE { + tag "$meta.id" + label "process_high" + label "process_high_memory" + + conda "${moduleDir}/environment.yml" + container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container ? + 'https://depot.galaxyproject.org/singularity/umicollapse:1.0.0--hdfd78af_1' : + 'biocontainers/umicollapse:1.0.0--hdfd78af_1' }" + + input: + tuple val(meta), path(input), path(bai) + val(mode) + + output: + tuple val(meta), path("*.bam"), emit: bam, optional: true + tuple val(meta), path("*dedup*fastq.gz"), emit: fastq, optional: true + tuple val(meta), path("*_UMICollapse.log"), emit: log + path "versions.yml" , emit: versions + + when: + task.ext.when == null || task.ext.when + + script: + def args = task.ext.args ?: '' + def prefix = task.ext.prefix ?: "${meta.id}" + def VERSION = '1.0.0-1' // WARN: Version information not provided by tool on CLI. Please update this string when bumping container versions. + // Memory allocation: We need to make sure that both heap and stack size is sufficiently large for + // umicollapse. We set the stack size to 5% of the available memory, the heap size to 90% + // which leaves 5% for stuff happening outside of java without the scheduler killing the process. + def max_heap_size_mega = (task.memory.toMega() * 0.9).intValue() + def max_stack_size_mega = 999 //most java jdks will not allow Xss > 1GB, so fixing this to the allowed max + if ( mode !in [ 'fastq', 'bam' ] ) { + error "Mode must be one of 'fastq' or 'bam'." + } + extension = mode.contains("fastq") ? "fastq.gz" : "bam" + """ + # Getting the umicollapse jar file like this because `umicollapse` is a Python wrapper script generated + # by conda that allows to set the heap size (Xmx), but not the stack size (Xss). + # `which` allows us to get the directory that contains `umicollapse`, independent of whether we + # are in a container or conda environment. + UMICOLLAPSE_JAR=\$(dirname \$(which umicollapse))/../share/umicollapse-${VERSION}/umicollapse.jar + java \\ + -Xmx${max_heap_size_mega}M \\ + -Xss${max_stack_size_mega}M \\ + -jar \$UMICOLLAPSE_JAR \\ + $mode \\ + -i ${input} \\ + -o ${prefix}.${extension} \\ + $args | tee ${prefix}_UMICollapse.log + + cat <<-END_VERSIONS > versions.yml + "${task.process}": + umicollapse: $VERSION + END_VERSIONS + """ + + stub: + def prefix = task.ext.prefix ?: "${meta.id}" + def VERSION = '1.0.0-1' + if ( mode !in [ 'fastq', 'bam' ] ) { + error "Mode must be one of 'fastq' or 'bam'." + } + extension = mode.contains("fastq") ? "fastq.gz" : "bam" + """ + touch ${prefix}.dedup.${extension} + touch ${prefix}_UMICollapse.log + cat <<-END_VERSIONS > versions.yml + "${task.process}": + umicollapse: $VERSION + END_VERSIONS + """ +} diff --git a/modules/nf-core/umicollapse/meta.yml b/modules/nf-core/umicollapse/meta.yml new file mode 100644 index 000000000..c1361f9ad --- /dev/null +++ b/modules/nf-core/umicollapse/meta.yml @@ -0,0 +1,63 @@ +--- +name: "umicollapse" +description: Deduplicate reads based on the mapping co-ordinate and the UMI attached to the read. +keywords: + - umicollapse + - deduplication + - genomics +tools: + - "umicollapse": + description: "UMICollapse contains tools for dealing with Unique Molecular Identifiers (UMIs)/Random Molecular Tags (RMTs)." + homepage: "https://github.com/Daniel-Liu-c0deb0t/UMICollapse" + documentation: "https://github.com/Daniel-Liu-c0deb0t/UMICollapse" + tool_dev_url: "https://github.com/Daniel-Liu-c0deb0t/UMICollapse" + doi: "10.7717/peerj.8275" + licence: ["MIT"] +input: + - meta: + type: map + description: | + Groovy Map containing sample information + e.g. [ id:'test', single_end:false ] + - bam: + type: file + description: | + BAM file containing reads to be deduplicated via UMIs. + pattern: "*.{bam}" + - bai: + type: file + description: | + BAM index files corresponding to the input BAM file. Optionally can be skipped using [] when using FastQ input. + pattern: "*.{bai}" + - mode: + type: string + description: | + Selects the mode of Umicollapse - either fastq or bam need to be provided. + pattern: "{fastq,bam}" + +output: + - meta: + type: map + description: | + Groovy Map containing sample information + e.g. [ id:'test', single_end:false ] + - bam: + type: file + description: BAM file with deduplicated UMIs. + pattern: "*.{bam}" + - log: + type: file + description: A log file with the deduplication statistics. + pattern: "*_{UMICollapse.log}" + - versions: + type: file + description: File containing software versions + pattern: "versions.yml" +authors: + - "@CharlotteAnne" + - "@chris-cheshire" +maintainers: + - "@CharlotteAnne" + - "@chris-cheshire" + - "@apeltzer" + - "@MatthiasZepper" diff --git a/modules/nf-core/umicollapse/tests/main.nf.test b/modules/nf-core/umicollapse/tests/main.nf.test new file mode 100644 index 000000000..cc28359a6 --- /dev/null +++ b/modules/nf-core/umicollapse/tests/main.nf.test @@ -0,0 +1,249 @@ +nextflow_process { + + name "Test Process UMICOLLAPSE" + script "../main.nf" + process "UMICOLLAPSE" + + tag "modules" + tag "modules_nfcore" + tag "umicollapse" + tag "umitools/extract" + tag "samtools/index" + tag "bwa/index" + tag "bwa/mem" + + test("umicollapse single end test") { + setup{ + run("UMITOOLS_EXTRACT"){ + script "../../umitools/extract/main.nf" + config "./nextflow_SE.config" + process{ + """ + input[0] = [ + [ id:'test', single_end:true ], // meta map + [ + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_1.fastq.gz', checkIfExists: true), + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_2.fastq.gz', checkIfExists: true) + ] + ] + """ + } + } + + run("BWA_INDEX"){ + script "../../bwa/index/main.nf" + process{ + """ + input[0] = [[ id:'sarscov2'],file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)] + """ + } + } + run("BWA_MEM"){ + script "../../bwa/mem/main.nf" + process{ + """ + input[0] = UMITOOLS_EXTRACT.out.reads + input[1] = BWA_INDEX.out.index + input[2] = [[ id:'sarscov2'],file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)] + input[3] = true + """ + } + } + run("SAMTOOLS_INDEX"){ + script "../../samtools/index/main.nf" + process{ + """ + input[0] = BWA_MEM.out.bam + """ + } + } + } + + when { + config "./nextflow_SE.config" + process { + """ + input[0] = BWA_MEM.out.bam.join(SAMTOOLS_INDEX.out.bai, by: [0]) + input[1] = 'bam' + """ + } + } + + then { + assertAll( + { assert process.success }, + { assert snapshot( + process.out.bam, + process.out.versions).match() } + ) + } + + } + + test("umicollapse paired tests") { + setup{ + run("UMITOOLS_EXTRACT"){ + script "../../umitools/extract/main.nf" + config "./nextflow_PE.config" + process{ + """ + input[0] = [ + [ id:'test', single_end:false ], // meta map + [ + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_1.fastq.gz', checkIfExists: true), + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_2.fastq.gz', checkIfExists: true) + ] + ] + """ + } + } + + run("BWA_INDEX"){ + script "../../bwa/index/main.nf" + process{ + """ + input[0] = [ + [ id:'sarscov2'], + file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true) + ] + """ + } + } + run("BWA_MEM"){ + script "../../bwa/mem/main.nf" + process{ + """ + input[0] = UMITOOLS_EXTRACT.out.reads + input[1] = BWA_INDEX.out.index + input[2] = [[ id:'sarscov2'],file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)] + input[3] = true + """ + } + } + run("SAMTOOLS_INDEX"){ + script "../../samtools/index/main.nf" + process{ + """ + input[0] = BWA_MEM.out.bam + """ + } + } + } + + when { + config "./nextflow_PE.config" + process { + """ + input[0] = BWA_MEM.out.bam.join(SAMTOOLS_INDEX.out.bai, by: [0]) + input[1] = 'bam' + """ + } + } + + then { + assertAll( + { assert process.success }, + { assert snapshot( + process.out.bam, + process.out.versions).match() } + ) + } + + } + + test("umicollapse fastq tests") { + + when { + config "./nextflow_SE.config" + process { + """ + input[0] = [ + [ id:'test', single_end:true ], // meta map + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_1.fastq.gz', checkIfExists: true), + [] + ] + input[1] = 'fastq' + """ + } + } + + then { + assertAll( + { assert process.success }, + { assert snapshot( + process.out.fastq, + process.out.versions).match() } + ) + } + } + + test("umicollapse stub tests") { + options "-stub-run" + setup{ + run("UMITOOLS_EXTRACT"){ + script "../../umitools/extract/main.nf" + config "./nextflow_PE.config" + process{ + """ + input[0] = [ + [ id:'test', single_end:false ], // meta map + [ + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_1.fastq.gz', checkIfExists: true), + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_2.fastq.gz', checkIfExists: true) + ] + ] + """ + } + } + + run("BWA_INDEX"){ + script "../../bwa/index/main.nf" + process{ + """ + input[0] = [ + [ id:'sarscov2'], + file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true) + ] + """ + } + } + run("BWA_MEM"){ + script "../../bwa/mem/main.nf" + process{ + """ + input[0] = UMITOOLS_EXTRACT.out.reads + input[1] = BWA_INDEX.out.index + input[2] = [[ id:'sarscov2'],file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)] + input[3] = true + """ + } + } + run("SAMTOOLS_INDEX"){ + script "../../samtools/index/main.nf" + process{ + """ + input[0] = BWA_MEM.out.bam + """ + } + } + } + when { + config "./nextflow_PE.config" + process { + """ + input[0] = BWA_MEM.out.bam.join(SAMTOOLS_INDEX.out.bai, by: [0]) + input[1] = 'bam' + """ + } + } + + then { + assertAll( + { assert process.success }, + { assert snapshot(process.out).match() } + ) + } + + } + +} \ No newline at end of file diff --git a/modules/nf-core/umicollapse/tests/main.nf.test.snap b/modules/nf-core/umicollapse/tests/main.nf.test.snap new file mode 100644 index 000000000..3f393eac1 --- /dev/null +++ b/modules/nf-core/umicollapse/tests/main.nf.test.snap @@ -0,0 +1,124 @@ +{ + "umicollapse single end test": { + "content": [ + [ + [ + { + "id": "test", + "single_end": true + }, + "test.dedup.bam:md5,89e844724f73fae9e7100506d0be5775" + ] + ], + [ + "versions.yml:md5,c1e0275d81b1c97a9344d216f9154996" + ] + ], + "meta": { + "nf-test": "0.8.4", + "nextflow": "23.10.1" + }, + "timestamp": "2024-05-20T08:47:11.402203361" + }, + "umicollapse fastq tests": { + "content": [ + [ + [ + { + "id": "test", + "single_end": true + }, + "test.dedup.fastq.gz:md5,c9bac08c7fd8df3e0203e3eeafc73155" + ] + ], + [ + "versions.yml:md5,c1e0275d81b1c97a9344d216f9154996" + ] + ], + "meta": { + "nf-test": "0.8.4", + "nextflow": "23.10.1" + }, + "timestamp": "2024-01-30T10:45:56.053352008" + }, + "umicollapse stub tests": { + "content": [ + { + "0": [ + [ + { + "id": "test", + "single_end": false + }, + "test.dedup.dedup.bam:md5,d41d8cd98f00b204e9800998ecf8427e" + ] + ], + "1": [ + + ], + "2": [ + [ + { + "id": "test", + "single_end": false + }, + "test.dedup_UMICollapse.log:md5,d41d8cd98f00b204e9800998ecf8427e" + ] + ], + "3": [ + "versions.yml:md5,c1e0275d81b1c97a9344d216f9154996" + ], + "bam": [ + [ + { + "id": "test", + "single_end": false + }, + "test.dedup.dedup.bam:md5,d41d8cd98f00b204e9800998ecf8427e" + ] + ], + "fastq": [ + + ], + "log": [ + [ + { + "id": "test", + "single_end": false + }, + "test.dedup_UMICollapse.log:md5,d41d8cd98f00b204e9800998ecf8427e" + ] + ], + "versions": [ + "versions.yml:md5,c1e0275d81b1c97a9344d216f9154996" + ] + } + ], + "meta": { + "nf-test": "0.8.4", + "nextflow": "23.10.1" + }, + "timestamp": "2024-01-30T10:46:12.482697713" + }, + "umicollapse paired tests": { + "content": [ + [ + [ + { + "id": "test", + "single_end": false + }, + "test.dedup.bam:md5,3e2ae4701e3d2ca074ea878a314a3e4f" + ] + ], + [ + "versions.yml:md5,c1e0275d81b1c97a9344d216f9154996" + ] + ], + "meta": { + "nf-test": "0.8.4", + "nextflow": "23.10.1" + }, + "timestamp": "2024-05-20T08:47:30.028323337" + } +} \ No newline at end of file diff --git a/modules/nf-core/umicollapse/tests/nextflow.config b/modules/nf-core/umicollapse/tests/nextflow.config new file mode 100644 index 000000000..844edbdc6 --- /dev/null +++ b/modules/nf-core/umicollapse/tests/nextflow.config @@ -0,0 +1,8 @@ +process { + withName: UMITOOLS_EXTRACT { + ext.args = '--bc-pattern="NNNN"' + } + withName: UMICOLLAPSE { + ext.prefix = { "${meta.id}.dedup" } + } +} \ No newline at end of file diff --git a/modules/nf-core/umicollapse/tests/nextflow_PE.config b/modules/nf-core/umicollapse/tests/nextflow_PE.config new file mode 100644 index 000000000..ae4c96320 --- /dev/null +++ b/modules/nf-core/umicollapse/tests/nextflow_PE.config @@ -0,0 +1,10 @@ +process { + + withName: UMITOOLS_EXTRACT { + ext.args = '--bc-pattern="NNNN" --bc-pattern2="NNNN"' + } + + withName: UMICOLLAPSE { + ext.prefix = { "${meta.id}.dedup" } + } +} diff --git a/modules/nf-core/umicollapse/tests/nextflow_SE.config b/modules/nf-core/umicollapse/tests/nextflow_SE.config new file mode 100644 index 000000000..d4b944365 --- /dev/null +++ b/modules/nf-core/umicollapse/tests/nextflow_SE.config @@ -0,0 +1,10 @@ +process { + + withName: UMITOOLS_EXTRACT { + ext.args = '--bc-pattern="NNNN"' + } + + withName: UMICOLLAPSE { + ext.prefix = { "${meta.id}.dedup" } + } +} diff --git a/modules/nf-core/umicollapse/tests/tags.yml b/modules/nf-core/umicollapse/tests/tags.yml new file mode 100644 index 000000000..912879c4d --- /dev/null +++ b/modules/nf-core/umicollapse/tests/tags.yml @@ -0,0 +1,2 @@ +umicollapse: + - "modules/nf-core/umicollapse/**" diff --git a/nextflow.config b/nextflow.config index 1c46d7c2e..b56fa1e48 100644 --- a/nextflow.config +++ b/nextflow.config @@ -30,6 +30,7 @@ params { with_umi = false skip_umi_extract = false umitools_extract_method = 'string' + umi_dedup_tool = 'umicollapse' umitools_grouping_method = 'directional' umitools_dedup_stats = false umitools_bc_pattern = null diff --git a/nextflow_schema.json b/nextflow_schema.json index d89b6398e..28ac08976 100644 --- a/nextflow_schema.json +++ b/nextflow_schema.json @@ -305,6 +305,13 @@ "fa_icon": "fas fa-barcode", "description": "Enable UMI-based read deduplication." }, + "umi_dedup_tool": { + "type": "string", + "default": "umicollapse", + "description": "Specifies the tool to use for UMI deduplication - available options are 'umitools' and 'umicollapse'.", + "fa_icon": "fas fa-barcode", + "enum": ["umitools", "umicollapse"] + }, "umitools_extract_method": { "type": "string", "default": "string", diff --git a/subworkflows/local/utils_nfcore_rnaseq_pipeline/main.nf b/subworkflows/local/utils_nfcore_rnaseq_pipeline/main.nf index b2b890479..8385bf08a 100644 --- a/subworkflows/local/utils_nfcore_rnaseq_pipeline/main.nf +++ b/subworkflows/local/utils_nfcore_rnaseq_pipeline/main.nf @@ -216,6 +216,10 @@ def validateInputParameters() { } } + if (params.with_umi && params.umi_dedup_tool == "umicollapse" && params.umitools_grouping_method !in ['directional', 'adjacency', 'cluster']) { + error("UMI grouping method '${params.umitools_grouping_method}' unsupported for umicollapse, supported methods are 'cluster', 'adjacency' and 'directional'") + } + if (params.skip_alignment) { skipAlignmentWarn() } diff --git a/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/main.nf b/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/main.nf new file mode 100644 index 000000000..54c42b986 --- /dev/null +++ b/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/main.nf @@ -0,0 +1,55 @@ +// +// umicollapse, index BAM file and run samtools stats, flagstat and idxstats +// + +include { UMICOLLAPSE } from '../../../modules/nf-core/umicollapse/main' +include { SAMTOOLS_INDEX } from '../../../modules/nf-core/samtools/index/main' +include { BAM_STATS_SAMTOOLS } from '../bam_stats_samtools/main' + +workflow BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE { + take: + ch_bam_bai // channel: [ val(meta), path(bam), path(bai/csi) ] + + main: + + ch_versions = Channel.empty() + + // + // umicollapse in bam mode (thus hardcode mode input channel to 'bam') + // + UMICOLLAPSE ( ch_bam_bai, channel.value( 'bam' )) + ch_versions = ch_versions.mix(UMICOLLAPSE.out.versions.first()) + + // + // Index BAM file and run samtools stats, flagstat and idxstats + // + SAMTOOLS_INDEX ( UMICOLLAPSE.out.bam ) + ch_versions = ch_versions.mix(SAMTOOLS_INDEX.out.versions.first()) + + ch_bam_bai_dedup = UMICOLLAPSE.out.bam + .join(SAMTOOLS_INDEX.out.bai, by: [0], remainder: true) + .join(SAMTOOLS_INDEX.out.csi, by: [0], remainder: true) + .map { + meta, bam, bai, csi -> + if (bai) { + [ meta, bam, bai ] + } else { + [ meta, bam, csi ] + } + } + + BAM_STATS_SAMTOOLS ( ch_bam_bai_dedup, [ [:], [] ] ) + ch_versions = ch_versions.mix(BAM_STATS_SAMTOOLS.out.versions) + + emit: + bam = UMICOLLAPSE.out.bam // channel: [ val(meta), path(bam) ] + + bai = SAMTOOLS_INDEX.out.bai // channel: [ val(meta), path(bai) ] + csi = SAMTOOLS_INDEX.out.csi // channel: [ val(meta), path(csi) ] + dedup_stats = UMICOLLAPSE.out.log // channel: [ val(meta), path(stats) ] + stats = BAM_STATS_SAMTOOLS.out.stats // channel: [ val(meta), path(stats) ] + flagstat = BAM_STATS_SAMTOOLS.out.flagstat // channel: [ val(meta), path(flagstat) ] + idxstats = BAM_STATS_SAMTOOLS.out.idxstats // channel: [ val(meta), path(idxstats) ] + + versions = ch_versions // channel: [ path(versions.yml) ] +} diff --git a/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/meta.yml b/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/meta.yml new file mode 100644 index 000000000..a24e0448d --- /dev/null +++ b/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/meta.yml @@ -0,0 +1,59 @@ +# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/subworkflows/yaml-schema.json +name: "bam_dedup_stats_samtools_umicollapse" +description: umicollapse, index BAM file and run samtools stats, flagstat and idxstats +keywords: + - umi + - dedup + - index + - bam + - sam + - cram +components: + - umicollapse + - samtools/index + - samtools/stats + - samtools/idxstats + - samtools/flagstat + - bam_stats_samtools +input: + - ch_bam_bai: + description: | + input BAM file + Structure: [ val(meta), path(bam), path(bai) ] +output: + - bam: + description: | + Umi deduplicated BAM/SAM file + Structure: [ val(meta), path(bam) ] + - bai: + description: | + Umi deduplicated BAM/SAM samtools index + Structure: [ val(meta), path(bai) ] + - csi: + description: | + CSI samtools index + Structure: [ val(meta), path(csi) ] + - dedupstats: + description: | + File containing umicollapse deduplication stats + Structure: [ val(meta), path(stats) ] + - stats: + description: | + File containing samtools stats output + Structure: [ val(meta), path(stats) ] + - flagstat: + description: | + File containing samtools flagstat output + Structure: [ val(meta), path(flagstat) ] + - idxstats: + description: | + File containing samtools idxstats output + Structure: [ val(meta), path(idxstats) ] + - versions: + description: | + Files containing software versions + Structure: [ path(versions.yml) ] +authors: + - "@MatthiasZepper" +maintainers: + - "@MatthiasZepper" diff --git a/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/main.nf.test b/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/main.nf.test new file mode 100644 index 000000000..dd7f23718 --- /dev/null +++ b/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/main.nf.test @@ -0,0 +1,103 @@ +// nf-core subworkflows test bam_dedup_stats_samtools_umicollapse +nextflow_workflow { + + name "Test Subworkflow BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE" + script "../main.nf" + workflow "BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE" + + tag "subworkflows" + tag "subworkflows_nfcore" + tag "subworkflows/bam_dedup_stats_samtools_umicollapse" + tag "subworkflows/bam_stats_samtools" + tag "bam_stats_samtools" + tag "bwa/index" + tag "bwa/mem" + tag "samtools" + tag "samtools/index" + tag "samtools/stats" + tag "samtools/idxstats" + tag "samtools/flagstat" + tag "umicollapse" + tag "umitools/extract" + + test("sarscov2_bam_bai") { + + setup{ + run("UMITOOLS_EXTRACT"){ + script "../../../../modules/nf-core/umitools/extract/main.nf" + config "./paired-end-umis.config" + process{ + """ + input[0] = [ + [ id:'test', single_end:false ], // meta map + [ + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_1.fastq.gz', checkIfExists: true), + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_2.fastq.gz', checkIfExists: true) + ] + ] + """ + } + } + + run("BWA_INDEX"){ + script "../../../../modules/nf-core/bwa/index/main.nf" + process{ + """ + input[0] = [ + [ id:'sarscov2'], + file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true) + ] + """ + } + } + run("BWA_MEM"){ + script "../../../../modules/nf-core/bwa/mem/main.nf" + process{ + """ + input[0] = UMITOOLS_EXTRACT.out.reads + input[1] = BWA_INDEX.out.index + input[2] = [[ id:'sarscov2'],file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)] + input[3] = true + """ + } + } + run("SAMTOOLS_INDEX"){ + script "../../../../modules/nf-core/samtools/index/main.nf" + process{ + """ + input[0] = BWA_MEM.out.bam + """ + } + } + } + + when { + config "./paired-end-umis.config" + params { + outdir = "$outputDir" + } + workflow { + """ + + input[0] = BWA_MEM.out.bam.join(SAMTOOLS_INDEX.out.bai, by: [0]) + + """ + } + } + + then { + assertAll( + { assert workflow.success}, + { assert snapshot(workflow.out.bam, workflow.out.versions).match() }, + { assert workflow.out.bam.get(0).get(1) ==~ ".*.bam"}, + { assert workflow.out.bai.get(0).get(1) ==~ ".*.bai"}, + { assert workflow.out.dedup_stats.get(0).get(1) ==~ ".*_UMICollapse.log"}, + { assert snapshot(workflow.out.stats).match("test_bam_dedup_stats_samtools_umicollapse_stats") }, + { assert snapshot(workflow.out.flagstat).match("test_bam_dedup_stats_samtools_umicollapse_flagstats") }, + { assert snapshot(workflow.out.idxstats).match("test_bam_dedup_stats_samtools_umicollapse_idxstats") } + ) + } + +} +} + diff --git a/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/main.nf.test.snap b/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/main.nf.test.snap new file mode 100644 index 000000000..e0c32a77e --- /dev/null +++ b/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/main.nf.test.snap @@ -0,0 +1,81 @@ +{ + "test_bam_dedup_stats_samtools_umicollapse_stats": { + "content": [ + [ + [ + { + "id": "test", + "single_end": false + }, + "test.stats:md5,34e1f13bd6e6b78bb8a589560222e343" + ] + ] + ], + "meta": { + "nf-test": "0.8.4", + "nextflow": "23.10.1" + }, + "timestamp": "2024-05-29T07:42:00.089356713" + }, + "test_bam_dedup_stats_samtools_umicollapse_flagstats": { + "content": [ + [ + [ + { + "id": "test", + "single_end": false + }, + "test.flagstat:md5,18d602435a02a4d721b78d1812622159" + ] + ] + ], + "meta": { + "nf-test": "0.8.4", + "nextflow": "23.10.1" + }, + "timestamp": "2024-04-09T17:05:48.69612524" + }, + "sarscov2_bam_bai": { + "content": [ + [ + [ + { + "id": "test", + "single_end": false + }, + "test.dedup.bam:md5,3e2ae4701e3d2ca074ea878a314a3e4f" + ] + ], + [ + "versions.yml:md5,15f852de3d9e3fb11c786d15873c5d16", + "versions.yml:md5,268e43f34038d4c6146ed050630f95b4", + "versions.yml:md5,543112ba38e0fae7ae41be77cf1e0791", + "versions.yml:md5,7f6271d4e25dc8c739f28a15399787db", + "versions.yml:md5,d241a309a3e0eecfe1d3fe6efa65515c" + ] + ], + "meta": { + "nf-test": "0.8.4", + "nextflow": "23.10.1" + }, + "timestamp": "2024-05-29T07:42:00.053427512" + }, + "test_bam_dedup_stats_samtools_umicollapse_idxstats": { + "content": [ + [ + [ + { + "id": "test", + "single_end": false + }, + "test.idxstats:md5,85d20a901eef23ca50c323638a2eb602" + ] + ] + ], + "meta": { + "nf-test": "0.8.4", + "nextflow": "23.10.1" + }, + "timestamp": "2024-04-09T17:05:48.740441747" + } +} \ No newline at end of file diff --git a/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/paired-end-umis.config b/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/paired-end-umis.config new file mode 100644 index 000000000..602c026f0 --- /dev/null +++ b/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/paired-end-umis.config @@ -0,0 +1,10 @@ +process { + + withName: UMITOOLS_EXTRACT { + ext.args = '--bc-pattern="NNNN" --bc-pattern2="NNNN"' + } + + withName: UMICOLLAPSE { + ext.prefix = { "${meta.id}.dedup" } + } +} \ No newline at end of file diff --git a/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/tags.yml b/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/tags.yml new file mode 100644 index 000000000..a3ba5b726 --- /dev/null +++ b/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/tags.yml @@ -0,0 +1,2 @@ +subworkflows/bam_dedup_stats_samtools_umicollapse: + - subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/** diff --git a/workflows/rnaseq/main.nf b/workflows/rnaseq/main.nf index 0b702e641..b721f5d17 100755 --- a/workflows/rnaseq/main.nf +++ b/workflows/rnaseq/main.nf @@ -55,6 +55,8 @@ include { FASTQ_ALIGN_HISAT2 } from '../../subworkflows/nf-core/fa include { BAM_SORT_STATS_SAMTOOLS } from '../../subworkflows/nf-core/bam_sort_stats_samtools' include { BAM_MARKDUPLICATES_PICARD } from '../../subworkflows/nf-core/bam_markduplicates_picard' include { BAM_RSEQC } from '../../subworkflows/nf-core/bam_rseqc' +include { BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE as BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_GENOME } from '../../subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse' +include { BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE as BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_TRANSCRIPTOME } from '../../subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse' include { BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS as BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME } from '../../subworkflows/nf-core/bam_dedup_stats_samtools_umitools' include { BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS as BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_TRANSCRIPTOME } from '../../subworkflows/nf-core/bam_dedup_stats_samtools_umitools' include { BEDGRAPH_BEDCLIP_BEDGRAPHTOBIGWIG as BEDGRAPH_BEDCLIP_BEDGRAPHTOBIGWIG_FORWARD } from '../../subworkflows/nf-core/bedgraph_bedclip_bedgraphtobigwig' @@ -231,21 +233,32 @@ workflow RNASEQ { // if (params.with_umi) { // Deduplicate genome BAM file before downstream analysis - BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME ( - ch_genome_bam.join(ch_genome_bam_index, by: [0]), - params.umitools_dedup_stats - ) - ch_genome_bam = BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME.out.bam - ch_genome_bam_index = BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME.out.bai - ch_multiqc_files = ch_multiqc_files.mix(BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME.out.deduplog.collect{it[1]}) - ch_multiqc_files = ch_multiqc_files.mix(BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME.out.stats.collect{it[1]}) - ch_multiqc_files = ch_multiqc_files.mix(BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME.out.flagstat.collect{it[1]}) - ch_multiqc_files = ch_multiqc_files.mix(BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME.out.idxstats.collect{it[1]}) + if (params.umi_dedup_tool == "umicollapse") { + BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_GENOME ( + ch_genome_bam.join(ch_genome_bam_index, by: [0]) + ) + umi_dedup_genome = BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_GENOME + ch_multiqc_files = ch_multiqc_files.mix(umi_dedup_genome.out.dedup_stats.collect{it[1]}.ifEmpty([])) + } else if (params.umi_dedup_tool == "umitools") { + BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME ( + ch_genome_bam.join(ch_genome_bam_index, by: [0]), + params.umitools_dedup_stats + ) + umi_dedup_genome = BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME + ch_multiqc_files = ch_multiqc_files.mix(umi_dedup_genome.out.deduplog.collect{it[1]}) + } else { + error("Unknown umi_dedup_tool '${params.umi_dedup_tool}'") + } + ch_genome_bam = umi_dedup_genome.out.bam + ch_genome_bam_index = umi_dedup_genome.out.bai + ch_multiqc_files = ch_multiqc_files.mix(umi_dedup_genome.out.stats.collect{it[1]}) + ch_multiqc_files = ch_multiqc_files.mix(umi_dedup_genome.out.flagstat.collect{it[1]}) + ch_multiqc_files = ch_multiqc_files.mix(umi_dedup_genome.out.idxstats.collect{it[1]}) if (params.bam_csi_index) { - ch_genome_bam_index = BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME.out.csi + ch_genome_bam_index = umi_dedup_genome.out.csi } - ch_versions = ch_versions.mix(BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME.out.versions) + ch_versions = ch_versions.mix(umi_dedup_genome.out.versions) // Co-ordinate sort, index and run stats on transcriptome BAM BAM_SORT_STATS_SAMTOOLS ( @@ -256,14 +269,24 @@ workflow RNASEQ { ch_transcriptome_sorted_bai = BAM_SORT_STATS_SAMTOOLS.out.bai // Deduplicate transcriptome BAM file before read counting with Salmon - BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_TRANSCRIPTOME ( - ch_transcriptome_sorted_bam.join(ch_transcriptome_sorted_bai, by: [0]), - params.umitools_dedup_stats - ) + if (params.umi_dedup_tool == "umicollapse") { + BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_TRANSCRIPTOME ( + ch_transcriptome_sorted_bam.join(ch_transcriptome_sorted_bai, by: [0]) + ) + umi_dedup_transcriptome = BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_TRANSCRIPTOME + } else if (params.umi_dedup_tool == "umitools") { + BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_TRANSCRIPTOME ( + ch_transcriptome_sorted_bam.join(ch_transcriptome_sorted_bai, by: [0]), + params.umitools_dedup_stats + ) + umi_dedup_transcriptome = BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_TRANSCRIPTOME + } else { + error("Unknown umi_dedup_tool '${params.umi_dedup_tool}'") + } // Name sort BAM before passing to Salmon SAMTOOLS_SORT ( - BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_TRANSCRIPTOME.out.bam, + umi_dedup_transcriptome.out.bam, ch_fasta.map { [ [:], it ] } ) @@ -278,16 +301,16 @@ workflow RNASEQ { paired_end: !meta.single_end return [ meta, bam ] } - .set { ch_umitools_dedup_bam } + .set { ch_dedup_bam } // Fix paired-end reads in name sorted BAM file // See: https://github.com/nf-core/rnaseq/issues/828 UMITOOLS_PREPAREFORSALMON ( - ch_umitools_dedup_bam.paired_end.map { meta, bam -> [ meta, bam, [] ] } + ch_dedup_bam.paired_end.map { meta, bam -> [ meta, bam, [] ] } ) ch_versions = ch_versions.mix(UMITOOLS_PREPAREFORSALMON.out.versions.first()) - ch_umitools_dedup_bam + ch_dedup_bam .single_end .mix(UMITOOLS_PREPAREFORSALMON.out.bam) .set { ch_transcriptome_bam } @@ -385,20 +408,31 @@ workflow RNASEQ { // SUBWORKFLOW: Remove duplicate reads from BAM file based on UMIs // if (params.with_umi) { - BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME ( - ch_genome_bam.join(ch_genome_bam_index, by: [0]), - params.umitools_dedup_stats - ) - ch_genome_bam = BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME.out.bam - ch_genome_bam_index = BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME.out.bai - ch_multiqc_files = ch_multiqc_files.mix(BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME.out.deduplog.collect{it[1]}) - ch_multiqc_files = ch_multiqc_files.mix(BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME.out.stats.collect{it[1]}) - ch_multiqc_files = ch_multiqc_files.mix(BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME.out.flagstat.collect{it[1]}) - ch_multiqc_files = ch_multiqc_files.mix(BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME.out.idxstats.collect{it[1]}) + if (params.umi_dedup_tool == "umicollapse") { + BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_GENOME ( + ch_genome_bam.join(ch_genome_bam_index, by: [0]), + ) + umi_dedup_genome = BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_GENOME + ch_multiqc_files = ch_multiqc_files.mix(umi_dedup_genome.out.dedup_stats.collect{it[1]}.ifEmpty([])) + } else if (params.umi_dedup_tool == "umitools") { + BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME ( + ch_genome_bam.join(ch_genome_bam_index, by: [0]), + params.umitools_dedup_stats + ) + umi_dedup_genome = BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME + ch_multiqc_files = ch_multiqc_files.mix(umi_dedup_genome.out.deduplog.collect{it[1]}) + } else { + error("Unknown umi_dedup_tool '${params.umi_dedup_tool}'") + } + ch_genome_bam = umi_dedup_genome.out.bam + ch_genome_bam_index = umi_dedup_genome.out.bai + ch_multiqc_files = ch_multiqc_files.mix(umi_dedup_genome.out.stats.collect{it[1]}) + ch_multiqc_files = ch_multiqc_files.mix(umi_dedup_genome.out.flagstat.collect{it[1]}) + ch_multiqc_files = ch_multiqc_files.mix(umi_dedup_genome.out.idxstats.collect{it[1]}) if (params.bam_csi_index) { - ch_genome_bam_index = BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME.out.csi + ch_genome_bam_index = umi_dedup_genome.out.csi } - ch_versions = ch_versions.mix(BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME.out.versions) + ch_versions = ch_versions.mix(umi_dedup_genome.out.versions) } } diff --git a/workflows/rnaseq/nextflow.config b/workflows/rnaseq/nextflow.config index c7eeac733..e39b31e2c 100644 --- a/workflows/rnaseq/nextflow.config +++ b/workflows/rnaseq/nextflow.config @@ -18,6 +18,31 @@ includeConfig "../../subworkflows/nf-core/fastq_fastqc_umitools_fastp/nextflow.c includeConfig "../../subworkflows/nf-core/fastq_fastqc_umitools_trimgalore/nextflow.config" includeConfig "../../subworkflows/nf-core/fastq_subsample_fq_salmon/nextflow.config" +def umi_dedup_args() { + if (params.umi_dedup_tool == "umicollapse") { + def algo = params.umitools_grouping_method + if (params.umitools_grouping_method == 'directional') { + algo = 'dir' + } else if (params.umitools_grouping_method == 'adjacency') { + algo = 'adj' + } else if (params.umitools_grouping_method == 'cluster') { + algo = 'cc' +[] } + return { [ + '--two-pass', + meta.single_end ? '' : '--paired --remove-unpaired --remove-chimeric', + params.umitools_grouping_method ? "--algo '${algo}'" : '', + params.umitools_umi_separator ? "--umi-sep '${params.umitools_umi_separator}'" : '' + ].join(' ').trim() } + } else { + return { [ + meta.single_end ? '' : '--unpaired-reads=discard --chimeric-pairs=discard', + params.umitools_grouping_method ? "--method='${params.umitools_grouping_method}'" : '', + params.umitools_umi_separator ? "--umi-separator='${params.umitools_umi_separator}'" : '' + ].join(' ').trim() } + } +} + // // STAR Salmon alignment options // @@ -131,12 +156,8 @@ if (!params.skip_alignment && params.aligner == 'star_salmon') { ] } - withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_TRANSCRIPTOME:UMITOOLS_DEDUP' { - ext.args = { [ - meta.single_end ? '' : '--unpaired-reads=discard --chimeric-pairs=discard', - params.umitools_grouping_method ? "--method='${params.umitools_grouping_method}'" : '', - params.umitools_umi_separator ? "--umi-separator='${params.umitools_umi_separator}'" : '' - ].join(' ').trim() } + withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMI(COLLAPSE|TOOLS)_TRANSCRIPTOME:UMI(COLLAPSE|TOOLS_DEDUP)' { + ext.args = umi_dedup_args() ext.prefix = { "${meta.id}.umi_dedup.transcriptome.sorted" } publishDir = [ [ @@ -158,7 +179,7 @@ if (!params.skip_alignment && params.aligner == 'star_salmon') { ] } - withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_TRANSCRIPTOME:SAMTOOLS_INDEX' { + withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMI(COLLAPSE|TOOLS)_TRANSCRIPTOME:SAMTOOLS_INDEX' { publishDir = [ path: { params.save_align_intermeds || params.save_umi_intermeds ? "${params.outdir}/${params.aligner}" : params.outdir }, mode: params.publish_dir_mode, @@ -167,7 +188,7 @@ if (!params.skip_alignment && params.aligner == 'star_salmon') { ] } - withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_TRANSCRIPTOME:BAM_STATS_SAMTOOLS:.*' { + withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMI(COLLAPSE|TOOLS)_TRANSCRIPTOME:BAM_STATS_SAMTOOLS:.*' { ext.prefix = { "${meta.id}.umi_dedup.transcriptome.sorted.bam" } publishDir = [ path: { "${params.outdir}/${params.aligner}/samtools_stats" }, @@ -225,12 +246,8 @@ if (!params.skip_alignment) { if (params.with_umi && ['star_salmon','hisat2'].contains(params.aligner)) { process { - withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME:UMITOOLS_DEDUP' { - ext.args = { [ - meta.single_end ? '' : '--unpaired-reads=discard --chimeric-pairs=discard', - params.umitools_grouping_method ? "--method='${params.umitools_grouping_method}'" : '', - params.umitools_umi_separator ? "--umi-separator='${params.umitools_umi_separator}'" : '' - ].join(' ').trim() } + withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMI(COLLAPSE|TOOLS)_GENOME:UMI(COLLAPSE|TOOLS_DEDUP)' { + ext.args = umi_dedup_args() ext.prefix = { "${meta.id}.umi_dedup.sorted" } publishDir = [ [ @@ -252,7 +269,7 @@ if (!params.skip_alignment) { ] } - withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME:SAMTOOLS_INDEX' { + withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMI(COLLAPSE|TOOLS)_GENOME:SAMTOOLS_INDEX' { ext.args = { params.bam_csi_index ? '-c' : '' } ext.prefix = { "${meta.id}.umi_dedup.sorted" } publishDir = [ @@ -263,7 +280,7 @@ if (!params.skip_alignment) { ] } - withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME:BAM_STATS_SAMTOOLS:.*' { + withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMI(COLLAPSE|TOOLS)_GENOME:BAM_STATS_SAMTOOLS:.*' { ext.prefix = { "${meta.id}.umi_dedup.sorted.bam" } publishDir = [ path: { "${params.outdir}/${params.aligner}/samtools_stats" }, From 761c56d4d3503672bd494766cb16bda24741ffba Mon Sep 17 00:00:00 2001 From: Siddhartha Bagaria <1929612+siddharthab@users.noreply.github.com> Date: Wed, 4 Sep 2024 16:44:42 +0000 Subject: [PATCH 002/107] Keep umitools as the default Signed-off-by: Siddhartha Bagaria <1929612+siddharthab@users.noreply.github.com> --- nextflow.config | 2 +- nextflow_schema.json | 2 +- workflows/rnaseq/nextflow.config | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/nextflow.config b/nextflow.config index b56fa1e48..170f74163 100644 --- a/nextflow.config +++ b/nextflow.config @@ -30,7 +30,7 @@ params { with_umi = false skip_umi_extract = false umitools_extract_method = 'string' - umi_dedup_tool = 'umicollapse' + umi_dedup_tool = 'umitools' umitools_grouping_method = 'directional' umitools_dedup_stats = false umitools_bc_pattern = null diff --git a/nextflow_schema.json b/nextflow_schema.json index 28ac08976..ba30a9ea7 100644 --- a/nextflow_schema.json +++ b/nextflow_schema.json @@ -307,7 +307,7 @@ }, "umi_dedup_tool": { "type": "string", - "default": "umicollapse", + "default": "umitools", "description": "Specifies the tool to use for UMI deduplication - available options are 'umitools' and 'umicollapse'.", "fa_icon": "fas fa-barcode", "enum": ["umitools", "umicollapse"] diff --git a/workflows/rnaseq/nextflow.config b/workflows/rnaseq/nextflow.config index e39b31e2c..19050c5a3 100644 --- a/workflows/rnaseq/nextflow.config +++ b/workflows/rnaseq/nextflow.config @@ -27,7 +27,7 @@ def umi_dedup_args() { algo = 'adj' } else if (params.umitools_grouping_method == 'cluster') { algo = 'cc' -[] } + } return { [ '--two-pass', meta.single_end ? '' : '--paired --remove-unpaired --remove-chimeric', From 314bd7683301b7e23abc89dd33638aac239dddb9 Mon Sep 17 00:00:00 2001 From: LorenzoS96 <47034913+LorenzoS96@users.noreply.github.com> Date: Tue, 29 Oct 2024 14:45:50 +0100 Subject: [PATCH 003/107] add DE_analys folder --- docs/usage/DE_analysis/de_rstudio.md | 553 ++++++++++++++++++ docs/usage/DE_analysis/img/DESeq_function.png | Bin 0 -> 14389 bytes .../DE_analysis/img/Excalidraw_RNAseq.png | Bin 0 -> 311041 bytes docs/usage/DE_analysis/img/MA_plot.png | Bin 0 -> 12100 bytes docs/usage/DE_analysis/img/PCA.png | Bin 0 -> 21817 bytes .../img/RNA_seq_scheme_tutorial.png | Bin 0 -> 424447 bytes .../DE_analysis/img/count_distribution.png | Bin 0 -> 18987 bytes docs/usage/DE_analysis/img/dds_comparison.png | Bin 0 -> 137676 bytes .../usage/DE_analysis/img/enrichment_plot.png | Bin 0 -> 49785 bytes .../DE_analysis/img/heatmap_de_genes.png | Bin 0 -> 33336 bytes .../img/hierarchical_clustering.png | Bin 0 -> 28751 bytes .../img/nf-core-rnaseq_metro_map_grey.png | Bin 0 -> 306439 bytes docs/usage/DE_analysis/img/overdispersion.png | Bin 0 -> 35441 bytes docs/usage/DE_analysis/img/plotCounts.png | Bin 0 -> 23578 bytes docs/usage/DE_analysis/img/project_R.png | Bin 0 -> 159756 bytes docs/usage/DE_analysis/img/volcanoplot.png | Bin 0 -> 24035 bytes .../usage/DE_analysis/img/workdir_RStudio.png | Bin 0 -> 84275 bytes docs/usage/DE_analysis/interpretation.md | 81 +++ docs/usage/DE_analysis/intro.md | 49 ++ docs/usage/DE_analysis/rnaseq.md | 134 +++++ docs/usage/DE_analysis/theory.md | 144 +++++ 21 files changed, 961 insertions(+) create mode 100644 docs/usage/DE_analysis/de_rstudio.md create mode 100644 docs/usage/DE_analysis/img/DESeq_function.png create mode 100644 docs/usage/DE_analysis/img/Excalidraw_RNAseq.png create mode 100644 docs/usage/DE_analysis/img/MA_plot.png create mode 100644 docs/usage/DE_analysis/img/PCA.png create mode 100644 docs/usage/DE_analysis/img/RNA_seq_scheme_tutorial.png create mode 100644 docs/usage/DE_analysis/img/count_distribution.png create mode 100644 docs/usage/DE_analysis/img/dds_comparison.png create mode 100644 docs/usage/DE_analysis/img/enrichment_plot.png create mode 100644 docs/usage/DE_analysis/img/heatmap_de_genes.png create mode 100644 docs/usage/DE_analysis/img/hierarchical_clustering.png create mode 100644 docs/usage/DE_analysis/img/nf-core-rnaseq_metro_map_grey.png create mode 100644 docs/usage/DE_analysis/img/overdispersion.png create mode 100644 docs/usage/DE_analysis/img/plotCounts.png create mode 100644 docs/usage/DE_analysis/img/project_R.png create mode 100644 docs/usage/DE_analysis/img/volcanoplot.png create mode 100644 docs/usage/DE_analysis/img/workdir_RStudio.png create mode 100644 docs/usage/DE_analysis/interpretation.md create mode 100644 docs/usage/DE_analysis/intro.md create mode 100644 docs/usage/DE_analysis/rnaseq.md create mode 100644 docs/usage/DE_analysis/theory.md diff --git a/docs/usage/DE_analysis/de_rstudio.md b/docs/usage/DE_analysis/de_rstudio.md new file mode 100644 index 000000000..de58d8bcc --- /dev/null +++ b/docs/usage/DE_analysis/de_rstudio.md @@ -0,0 +1,553 @@ +--- +order: 4 +--- + +# Differential Analysis with DESeq2 + +In this section of the tutorial, we will guide you through the practical steps necessary to set up the RStudio environment, load the required libraries and data, and execute the DESeq2 analysis. By the end of this section, you will have a fully functional DESeq2 analysis pipeline set up in RStudio, ready to uncover the differentially expressed genes in your dataset. + + +## Launching the RStudio environment + +Once the nf-core/rnaseq pipeline is terminated, the resulting data are stored in the folder `results_star_salmon`. Now, we can analyse the results by running DESeq2 on RStudio. First of all, we need to launch it: + +```bash +sudo rstudio-server start +``` + +A pop-up will appear and by clicking on **Open**, we will be redirected to the RStudio login page. By inserting the username and the password reported below, we will be able to connect to RStudio: + +```bash +Username: gitpod +Password: pass +``` + +To prevent losing connection, go back to gitpod and type on the **Terminal**: + +```bash +sleep 2h +``` + +This command will keep the gitpod session active for exactly 2 hours, providing sufficient time to complete our analysis without interruption. + +Now come back to our **RStudio session**. + + +## Differential Expression Analysis + +As in all analysis, firstly we need to create a new project: + +1) Go to the **File** menu and select **New Project**; + +2) Select **New Directory**, **New Project**, name the project as shown below and click on **Create Project**; + +![RNAseq](./img/project_R.png) + +3) The new project will be automatically opened in RStudio + +We can check whether we are in the correct working directory with `getwd()` The path `/workspace/gitpod/training/DE_analysis/` should appear on your console. +To store our results in an organized way, we will create a folder named **de_results** using the **New Folder** button in the bottom right panel. We will save all our resulting tables and plots in this folder. Next, go to the **File menu**, select **New File** and then **R Script** to create a script editor in which we will save all commands required for the analysis. In the editor type: + +```r +#### Differential expression analysis with DESeq2 #### +``` + +and save the file as **de_script.R**. From now on, each command described in the tutorial can be added to your script. The resulting working directory should look like this: + +![RNAseq](./img/workdir_RStudio.png) + +The analysis requires several R packages. To utilise them, we need to load the following libraries: + +```r +#### Loading libraries #### + +# tidyverse: collection of R packages for data manipulation, visualization and modeling + +library("tidyverse") + +# DESeq2: package for differential gene expression analysis + +library("DESeq2") + +# pheatmap: package for creating heatmaps, which will be used to visualise the results + +install.packages("pheatmap") # To install the package missing in the current RStudio env + +library("pheatmap") + +# RColorBrewer: package for creating color palettes, which will be used to customise the heatmaps + +library("RColorBrewer") + +# ggrepel: package that provides geoms for ggplot2 to repel overlapping text labels in the plots + +library("ggrepel") +``` + +and the pre-computed DESeq2 object (`dds`) generated by the nfcore/rnaseq pipeline: + +```r +#### Import the dds obtained from nfcore/rnaseq #### + +load("/workspace/gitpod/training/results_star_salmon/star_salmon/deseq2_qc/deseq2.dds.RData") +``` + +In this tutorial we will analyse the `dds` object generated by running the alignment with **STAR** and the quantification with **Salmon**. Alternatively, a user could choose to analyse the the `dds` object generated by running only **Salmon** for both lightweight alignment and quantification. The file is stored in `/workspace/gitpod/training/results_star_salmon/salmon/deseq2_qc/deseq2.dds.RData`. + +In DESEq2, the `dds` object is a central data structure that contains the following components: +- `countData`: a matrix of raw count data, where each row represents a gene and each column represents a sample; +- `colData`: a data frame containing information about the samples, such as the experimental design, treatment and other relevant metadata; +- `design`: a formula specifying the experimental design utilised to estimate the dispersion and the log2foldchange. + +All these components can be checked with specific commands: + +```r +#### dds inspection #### + +head(counts(dds)) # to check the raw counts + +colData(dds) # to check the sample info + +design(dds) # to check the design formula +``` + +The `colData` and the `design` are the ones created within the nfcore/rnaseq pipeline and must be reorganised prior to the analysis. With the following commands we will create our metadata starting from the info stored in the `dds`. We will rename the column of the `colData`, we will ensure that the rownames of the metadata are present in the same order as the column names and finally we will update the `colData` of the `dds` object with our newly created metadata. + +```r +#### Creation of metadata starting from the dds colData #### + +metadata <- DataFrame( + sample = colData(dds)$sample, + condition = colData(dds)$Group1, + replica = colData(dds)$Group2 +) + +# Assign names to rows of metadata + +rownames(metadata) <- colnames(counts(dds)) + +# Fill the dds colData with the generated metadata + +colData(dds) <- metadata +``` + +!!! note + + With this operation we also eliminate the `sizeFactors` already estimated by the **nfcore DESeq2 module**. + +To avoid errors in DESeq2 is essential to check that sample names match between the `colData` and the `countData`, and that the sample are in the correct order: + +```r +#### Check that sample names match in both files #### + +all(colnames(dds$counts) %in% rownames(metadata)) # Must be TRUE + +all(colnames(dds$counts) == rownames(metadata)) # Must be TRUE +``` + +Now that everything is setted up, we can proceed to generate a new DESeq2 object with the corrected metadata and the right design: + +```r +#### Creation of a new dds #### + +dds_new <- DESeqDataSet(dds, design = ~ condition) + +# dds inspection + +head(counts(dds_new)) # to check the raw counts + +colData(dds_new) # to check the sample info + +design(dds_new) # to check the design formula +``` + +Comparing the structure of the newly created dds (`dds_new`) with the one automatically produced by the pipeline (`dds`), we can observe the differences: + +![RNAseq](./img/dds_comparison.png) + +Before running the different steps of the analysis, a good practice consists in pre-filtering the genes to remove those with very low counts. This is useful to improve computional efficiency and enhance interpretability. In general, it is reasonable to keep only genes with a sum counts of at least 10 for a minimal number of 3 samples: + +```r +#### Pre-filtering #### + +# Select a minimal number of samples = 3 + +smallestGroupSize <- 3 + +# Select genes with a sum counts of at least 10 in 3 samples + +keep <- rowSums(counts(dds_new) >= 10) >= smallestGroupSize + +# Keep only the genes that pass the threshold + +dds_filtered <- dds_new[keep,] +``` + +Now, it is time to run the differential expression analysis with the `DESeq()` function: + +```r +#### Run the DESeq2 analysis #### + +dds_final <- DESeq(dds_filtered) +``` + +The `DESeq()` function is a high-level wrapper that simplifies the process of differential expression analysis by combining multiple steps into a single function call. + +This makes the workflow more user-friendly and ensures that all necessary preprocessing and statistical steps are executed in the correct order. The key functions that **DESeq2** calls include: +- **estimateSizeFactors**: to normalise the count data; +- **estimateDispersion**: to estimate the dispersion; +- **nbinomWaldTest**: to perform differential expression test. + +The individual functions can be carried out also singularly as shown below: + +```r +#### Differential expression analysis step-by-step #### + +dds_final <- estimateSizeFactors(dds_filtered) + +dds_final <- estimateDispersions(dds_final) + +dds_final <- nbinomWaldTest(dds_final) +``` + +The next step in the DESeq2 workflow is to perform quality control (QC) analysis on our data. This analysis is crucial for identifying potential issues or biases and ensuring the data are suitable for downstream analysis. For QC analysis, it is useful to work with transformed versions of the count data, `variance stabilizing transformations (vst)` and `regularized logarithm (rlog)`. While, the rlog is more robust to outliers and extreme values, vst is computationally faster and so preferred for larger dataset. +Notice that these transformations are used for visualisation purposes, while DESeq2 requires raw counts for differential expression analysis. + +```r +#### Transform normalised counts for data visualisation #### +# A user can choose among vst and rlog. In this tutorial we will work with rlog transformed data. + +rld <- rlog(dds_final, blind = TRUE) +``` + +The `rlog` and the `vst` transformations have an argument, **blind** that can be set to: +- **TRUE** (default): useful for QC analysis because it re-estimates the dispersion, allowing for comparison of samples in an unbiased manner with respect to experimental conditions; +- **FALSE**: the function utilizes the already estimated dispersion, generally applied when differences in counts are expected to be due to the experimental design. + +Next, we perform Principal Component Analysis (PCA) to explore the data. DESeq2 provides a built-in function, `plotPCA()`, which uses [ggplot2](https://ggplot2.tidyverse.org) for visualisation, taking the `rld` (or the `vst`) object as input. +Since the **treatment** is the principal condition of interest in our metadata, we will use the `condition` information from our metadata to plot the PCA: + +```r +#### Plot PCA #### + +plotPCA(rld, intgroup = "condition") +``` + +The second essential step in QC analysis is hierarchical clustering. Although DESeq2 does not have a built-in function for this analysis, we can use the `pheatmap()` function from the **pheatmap** package. +We will extract the matrix of rlog-transformed counts from the `rld` object (pheatmap input), compute pairwise correlations and plot the heatmap: + +```r +#### Plot sample to sample distance (hierarchical clustering) #### + +# Extract the matrix of rlog-transformed counts from the rld object + +sampleDists <- dist(t(assay(rld))) # Calculate pairwise distances between samples using the dist() function with Euclidean distance as the default method. By transposing the matrix with t(), we ensure that samples become rows and genes become columns, so that the dist function computes pairwise distances between samples. + +# Convert distances to a matrix + +sampleDistMatrix <- as.matrix(sampleDists) + +# Set the row and column names of the distance matrix + +rownames(sampleDistMatrix) <- paste(rld$condition, rld$replica, sep = "_") + +colnames(sampleDistMatrix) <- paste(rld$condition, rld$replica, sep = "_") + +# Define a color palette for the heatmap + +colors <- colorRampPalette(rev(brewer.pal(9, "Greens")))(255) # function from RColorBrewer package + +# Create the heatmap + +pheatmap(sampleDistMatrix, + clustering_distance_rows = sampleDists, + clustering_distance_cols = sampleDists, + col = colors, + fontsize_col = 8, + fontsize_row = 8) +``` + +The normalised counts stored in the `dds` can be inspected with the `counts()` function and saved in our results folder: + +```r +#### Inspect the normalised counts #### + +# Display the first few rows of the normalised counts to inspect the data + +head(counts(dds_final, normalized = TRUE)) + +# Display the first few rows of the raw counts (not normalised) to compare with the normalised counts + +head(counts(dds_final)) + +# Convert the normalised counts from the DESeq2 object to a tibble + +normalised_counts <- as_tibble(counts(dds_final, normalized = TRUE)) + +# Add a column for gene names to the normalised counts tibble + +normalised_counts$gene <- rownames(counts(dds_final)) + +# Relocate the gene column to the first position + +normalised_counts <- normalised_counts %>% + relocate(gene, .before = control_rep1) + +# Save normalised counts + +write.csv(normalised_counts, file = "de_results/normalised_counts.csv") +``` + +The `results()` function in DESeq2 is used to extract the results of the DE analysis. This function takes the `dds` object as input and returns a DataFrame containing the results of the analysis: + +- **baseMean**: the average expression level of the gene across all samples; +- **log2FoldChange**: the log2 fold change of the gene between the condition of interest and the reference level; +- **lfcSE**: the standard error of the log2 fold change; +- **stat**: the Wald statistic, which is used to calculate the p-value; +- **pvalue**: the p-value from the Wald test indicates the probability of observing the measured difference in gene expression (log2 fold change) by chance, assuming no true difference exists (null hypothesis). A low p-value suggests that the observed expression change between samples is unlikely due to random chance, so we can reject the null hypothesis; +- **padj**: the adjusted p-value, which takes into account multiple testing corrections, using the Benjamini-Hochberg method to control the false discovery rate; + +By default, the `results()` function returns the results for all genes in the analysis with an adjusted p-value below a specific FDR cutoff, set by default to 0.1. This threshold can be modified with the parameter `alpha`. The `results()` function can also be customised to filter the results based on certain criteria (log2 fold change or padj) or to set a specific contrast. A contrast is a specific comparison between two or more levels of a factor, such as the comparison between the treatment and control groups. The order of the contrast names determines the direction of the fold change that is reported in the results. Specifically, the first level of the contrast is the condition of interest and the second level is the reference level. +Notice that in this tutorial the contrast is already correctly specified. + +```r +#### Extract results table from the dds object #### + +res <- results(dds_final) + +# Visualise the results + +head(res) + +# Summarise the results showing the number of tested genes (genes with non-zero total read count), the genes up- and down-regulated at the selected threshold (alpha) and the number of genes excluded by the multiple testing due to a low mean count + +summary(res) + +# DESeq2 function to extract the name of the contrast + +resultsNames(dds_final) + +# res <- results(dds, contrast = c("design_formula", "condition_of_interest", "reference_condition")) +# Command to set the contrast, if necessary + +# Store the res object inside another variable because the original res file will be required for other functions + +res_viz <- res + +# Add gene names as a new column to the results table + +res_viz$gene <- rownames(res) + +# Convert the results to a tibble for easier manipulation and relocate the gene column to the first position + +res_viz <- as_tibble(res_viz) %>% + relocate(gene, .before = baseMean) + +# Save the results table + +write.csv(res_viz, file = "de_results/de_result_table.csv") +``` + +In the *Experimental Design* section, we emphasised the importance of estimating the log2 fold change threshold using a statistical power calculation, rather than selecting it arbitrarily. This approach ensures that the chosen threshold is statistically appropriate and tailored to the specifics of the experiment. However, since we are working with simulated data for demonstration purposes, we will use a padj threshold of 0.05 and consider genes with a log2 fold change greater than 1 or lower than -1 as differentially expressed. + +```r +#### Extract significant DE genes from the results #### + +# Filter the results to include only significantly differentially expressed genes with an adjusted p-value (padj) less than 0.05 and a log2foldchange greater than 1 or less than -1 + +resSig <- subset(res_viz, padj < 0.05 & abs(log2FoldChange) > 1) + +# Convert the results to a tibble for easier manipulation and relocate the 'Gene' column to be the first column + +resSig <- as_tibble(resSig) %>% + relocate(gene, .before = baseMean) + +# Order the significant genes by their adjusted p-value (padj) in ascending order + +resSig <- resSig[order(resSig$padj),] + +# Display the final results table of significant genes + +resSig + +# Save the significant DE genes + +write.csv(resSig, file = "de_results/sig_de_genes.csv") +``` + +Now that we have obtained the results of the differential expression analysis, it's time to visualise the data to gain a deeper understanding of the biological processes that are affected by the experimental conditions. Visualisation is a crucial step in RNA-seq analysis, as it allows us to identify patterns and trends in the data that may not be immediately apparent from the numerical results. In the following sections, we will explore different types of plots that are commonly used to visualise the results of RNA-seq analysis, including: + +- **MA plot**: it is a type of scatter plot that is commonly used to visualise the results of differential expression analysis for all the samples. The plot displays the mean of the normalised counts on the x-axis and the log2 fold change on the y-axis. This allows the visualisation of the relationship between the magnitude of the fold change and the mean expression level of the genes. Genes that are highly differentially expressed will appear farthest from the horizontal line, while genes with low expression levels will appear closer to the line. The MA plot is useful for identifying genes that are both highly expressed and highly differentially expressed between two conditions. + +```r +#### MA plot #### + +plotMA(res, ylim = c(-2,2)) +``` + +- **counts plot**: it plots the normalised counts for a single gene across the different conditions in your experiment. It’s particularly useful for visualising the expression levels of specific genes of interest and comparing them across sample groups. + +```r +#### Plot a specific gene in this case ENSG00000142192, a DE gene #### + +plotCounts(dds_final, gene = "ENSG00000142192") +``` + +**heatmap**: plot of the normalised counts for all the significant genes obtained with the `pheatmap()` function. The heatmap provides insights into genes and sample relationships that may not be apparent from individual gene plots alone. +Notiche that this plot is based on the normalised counts. + +```r +#### Heatmap #### + +# Extract only the first column (gene names) from the result object containing the significant genes + +significant_genes <- resSig[, 1] + +# Extract normalised counts for significant genes from the normalised counts matrix and convert the "gene" column to row names + +significant_counts <- inner_join(normalised_counts, significant_genes, by = "gene") %>% + column_to_rownames("gene") + +# Create the heatmap using pheatmap + +pheatmap(significant_counts, + cluster_rows = TRUE, + fontsize = 8, + scale = "row", + fontsize_row = 8, + height = 10) + +``` + +- **volcano plot**: it is a type of scatter plot that displays the log2 fold change on the x-axis and the log transformed padj on the y-axis. This allows for the visualisation of both the magnitude and significance of the changes in gene expression between two conditions. Genes that are highly differentially expressed (i.e., have a large log2 fold change) and are statistically significant (i.e., have a low padj) will appear in the top-left or top-right corners of the plot making easier to identify the most biologically meaningful changes. + +```r +#### Volcano plot #### + +# Convert the results to a tibble and add a column indicating differential expression status + +res_tb <- as_tibble(res) %>% + mutate(diffexpressed = case_when( + log2FoldChange > 1 & padj < 0.05 ~ 'upregulated', + log2FoldChange < -1 & padj < 0.05 ~ 'downregulated', + TRUE ~ 'not_de')) + +# Add a new column with gene names + +res_tb$gene <- rownames(res) + +# Relocate the 'gene' column to be the first column + +res_tb <- res_tb %>% + relocate(gene, .before = baseMean) + +# Order the table by adjusted p-value (padj) and add a new column for gene labels + +res_tb <- res_tb %>% arrange(padj) %>% + mutate(genelabels = "") + +# Label the top 5 most significant genes + +res_tb$genelabels[1:5] <- res_tb$gene[1:5] + +# Create a volcano plot using ggplot2 + +ggplot(data = res_tb, aes(x = log2FoldChange, y = -log10(padj), col = diffexpressed)) + + geom_point(size = 0.6) + + geom_text_repel(aes(label = genelabels), size = 2.5, max.overlaps = Inf) + + ggtitle("DE genes treatment versus control") + + geom_vline(xintercept = c(-1, 1), col = "black", linetype = 'dashed', linewidth = 0.2) + + geom_hline(yintercept = -log10(0.05), col = "black", linetype = 'dashed', linewidth = 0.2) + + theme(plot.title = element_text(size = rel(1.25), hjust = 0.5), + axis.title = element_text(size = rel(1))) + + scale_color_manual(values = c("upregulated" = "red", + "downregulated" = "blue", + "not_de" = "grey")) + + labs(color = 'DE genes') + + xlim(-3,5) + +``` + + +## Functional analysis + +The output of the differential expression analysis is a list of significant DE genes. To uncover the underlying biological mechanisms, various downstream analyses can be performed, such as functional enrichment analysis (identify overrepresented biological processes, molecular functions, cellular components or pathways) and network analysis (group genes based on similar expression patterns to identify novel interactions). To facilitate the interpretation of the resulting list of DE genes, a range of freely available web- and R-based tools can be employed. + +In this tutorial, we will explore an enrichment analysis technique known as Over-Representation Analysis (ORA), a powerful tool for identifying biological pathways or processes that are significantly enriched with DE genes. The underlying statistic behind ORA is the **hypergeometric test**, which considers three key components: + +- **Universe**: the background list of genes (for example the genes annotated in a genome); +- **Gene Set**: a subset of genes known to be involved in a specific biological pathway or process (for example genes from the Gene Ontology database); +- **Gene List**: the number of DE genes. + +The hypergeometric test calculates the probability of observing a certain number of genes from the gene set (pathway or process) within the gene list (DE genes) by chance. +An important aspect of this analysis is the concept of **membership**. It defines the relationship between DE genes and genes from the analysed gene set. By knowing which genes belong to which pathways/processes, we can determine whether the observed overlap between DE genes and a particular pathway/process is greater than what would be expected by random chance. + +```r +#### Enrichment analysis (ORA) #### + +# Loading libraries + +# clusterProfiler: package for enrichment analysis + +library(clusterProfiler) + +# org.Hs.eg.db: package for the human gene annotation database + +library(org.Hs.eg.db) + +# cowplot: package for combining multiple plots + +install.packages("cowplot") # To install the package missing in the current RStudio env + +library(cowplot) + +# Prepare gene list +# Extract the log2 fold change values from the results data frame + +gene_list <- res$log2FoldChange + +# Name the vector with the corresponding gene identifiers + +names(gene_list) <- res$gene + +# Sort the list in decreasing order (required for clusterProfiler) + +gene_list <- sort(gene_list, decreasing = TRUE) + +# Extract the significantly differentially expressed genes from the results data frame + +res_genes <- resSig$gene + +# Run GO enrichment analysis using the enrichGO function + +go_enrich <- enrichGO( + gene = res_genes, # Genes of interest + universe = names(gene_list), # Background gene set + OrgDb = org.Hs.eg.db, # Annotation database + keyType = 'ENSEMBL', # Key type for gene identifiers + readable = TRUE, # Convert gene IDs to gene names + ont = "ALL", # Ontology: can be "BP", "MF", "CC", or "ALL" + pvalueCutoff = 0.05, # P-value cutoff for significance + qvalueCutoff = 0.10 # Q-value cutoff for significance +) + +# Create a bar plot of the top enriched GO terms + +barplot <- barplot( + go_enrich, + title = "Enrichment analysis barplot", + font.size = 8 +) + +# Create a dot plot of the top enriched GO terms + +dotplot <- dotplot( + go_enrich, + title = "Enrichment analysis dotplot", + font.size = 8 +) + +# Combine the bar plot and dot plot into a single plot grid + +plot_grid(barplot, dotplot, nrow = 2) +``` diff --git 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From dfa8a04734a80863a12207f9027b2345fbd282cf Mon Sep 17 00:00:00 2001 From: LorenzoS96 <47034913+LorenzoS96@users.noreply.github.com> Date: Thu, 7 Nov 2024 09:19:38 +0100 Subject: [PATCH 019/107] update interpretation --- docs/usage/DEanalysis/interpretation.md | 55 +++++++++++++++++-------- 1 file changed, 37 insertions(+), 18 deletions(-) diff --git a/docs/usage/DEanalysis/interpretation.md b/docs/usage/DEanalysis/interpretation.md index 2257fcf59..9069dd6f1 100644 --- a/docs/usage/DEanalysis/interpretation.md +++ b/docs/usage/DEanalysis/interpretation.md @@ -1,35 +1,43 @@ # Interpretation -Once DE genes have been identified, the next crucial step is to interpret the results. This involves the inspection of tables and plots generated during the analysis to understand the biological significance of the data. In this part of the tutorial, we will delve into the results by discussing the significant DE genes and we will explore various plots generated during the analysis. +Once DE genes have been identified, the next crucial step is to interpret the results. This involves the inspection of tables and plots generated during the analysis to understand the biological significance of the data. In this part of the tutorial, we will explore the results by discussing the significant DE genes and we will examine various plots generated during the analysis. -!!! note +:::note +The results illustrated in this section might show slight variations compared to your runs due to randomness in the STAR algorithm. This randomness arises from using variable seed values and parallel processing, leading to minor differences in results between runs on the same data. These small discrepancies are not biologically significant and may affect counts and subsequent plots (such as PCA and count plots). However, the overall patterns and main findings should remain consistent. While exact reproducibility is ideal, minor variations are acceptable in practice, as long as they do not impact the main conclusions of the study. +::: - The results illustrated in this section might show slight variations compared to your runs due to randomness in the STAR algorithm. This randomness arises from using variable seed values and parallel processing, leading to minor differences in results between runs on the same data. These small discrepancies are not biologically significant and may affect counts and subsequent plots (such as PCA and count plots). However, the overall patterns and main findings should remain consistent. While exact reproducibility is ideal, minor variations are acceptable in practice, as long as they do not impact the main conclusions of the study. ## Quality control plots -The first plot we will examine is the Principal Component Analysis (PCA) plot. Since we're working with simulated data, our metadata is relatively simple, consisting of just three variables: sample, condition and replica. In a typical RNA-seq experiment, however, metadata can be complex and encompass a wide range of variables that could contribute to sample variation, such as sex, age and developmental stage. +The first plot we will examine is the Principal Component Analysis (PCA) plot. Since we're working with simulated data, our metadata is relatively simple, consisting of just three variables: `sample`, `condition`, and `replica`. In a typical RNA-seq experiment, however, metadata can be complex and encompass a wide range of variables that could contribute to sample variation, such as sex, age, and developmental stage. -![PCA](./img/PCA.png) +

    + ![pca](./img/pca_plot.png){ width="400" } +
    By plotting the PCA on the PC1 and PC2 axes, using `condition` as the main variable of interest, we can quickly identify the primary source of variation in our data. By accounting for this variation in our design model, we should be able to detect more differentially expressed genes related to `condition`. When working with real data, it's often useful to plot the data using different variables to explore how much variation is explained by the first two PCs. Depending on the results, it may be informative to examine variation on additional PC axes, such as PC3 and PC4, to gain a more comprehensive understanding of the data. Next, we will examine the hierarchical clustering plot to explore the relationships between samples based on their gene expression profiles. The heatmap is organized such that samples with similar expression profiles are close to each other, allowing us to identify patterns in the data. -![Cluster](./img/hierarchical_clustering.png) +
    + ![cluster](./img/hierarchical_clustering.png){ width="400" } +
    Remember that to create this plot, we utilized the `dist()` function, so in the legend on the right, a value of 0 corresponds to high correlation, while a value of 5 corresponds to very low correlation. Similar to PCA, we can see that samples tend to cluster together according to `condition`, indeed we can observe a high degree of correlation between the three control samples and between the three treated samples. Overall, the integration of these plots suggests that we are working with high-quality data and we can confidently proceed to the differential expression analysis. + ## Differential expression results -From this point, we will examine plots that are generated after the differential expression analysis. These plots are not quality control (QC) plots, but rather plots that help us to interpret the results. +In this part of the tutorial, we will examine plots that are generated after the differential expression analysis. These plots are not quality control plots, but rather plots that help us to interpret the results. After running the `results()` function, a good way to start to have an idea about the results is to look at the MA plot. -![RNAseq](./img/MA_plot.png) +
    + ![ma_plot](./img/MA_plot.png){ width="500" } +
    -By default, genes are coloured in blue if the padj is less than 0.1 and the log2foldchange greater than or less than 0. Genes that fall outside the plotting region are represented as open triangles. Note that we have not yet applied a filter to select only significant, which we define as those with a padj value less than 0.5 and a log2 fold change of at least 1 or -1. +By default, genes are coloured in blue if the padj is less than 0.1 and the log2 fold change greater than or less than 0. Genes that fall outside the plotting region are represented as open triangles. At this stage, we have not yet applied a filter to select only significant DE genes, which we define as those with a padj value less than 0.5 and a log2 fold change of at least 1 or -1. After filtering our genes of interest according to our threshold, let's have a look to our significatnt genes: @@ -44,30 +52,41 @@ ENSG00000156282 481.7624 1.095272 0.2969594 3.688289 To gain a comprehensive overview of the transcriptional profile, the volcano plot represents a highly informative tool. -![Volcano_plot](./img/volcanoplot.png) +
    + ![volcano_plot](./img/volcanoplot.png){ width="400"} +
    -The treatment induced differential expression in five genes, with one downregulated and four upregulated. This plot visually represents the numerical results reported in the table above. +The treatment induced differential expression in five genes: one downregulated and four upregulated. This plot visually represents the numerical results reported in the table above. After the identification of DE genes, it's informative to visualise the expression of specific genes of interest. The `plotCounts()` function applied directly on the `dds` object allows us to examine individual gene expression profiles without accessing the full `res` object. -![Counts](./img/plotCounts.png) -In our example, post-treatment, we observe a significant increase in the expression of the _ENSG00000142192_ gene, highlighting its responsiveness to the experimental conditions. +
    + ![counts](./img/plotCounts.png){ width="400" } +
    + +In our example, post-treatment, we observe a significant increase in the expression of the *ENSG00000142192* gene, highlighting its responsiveness to the experimental conditions. Finally, we can create a heatmap using the normalised expression counts of DE genes. The resulting heatmap visualises how the expression of significant genes varies across samples. Each row represents a gene, and each column represents a sample. The color intensity in the heatmap reflects the normalised expression levels: red colors indicate higher expression, while blue colors indicate lower expression. -![Heatmap](./img/heatmap_de_genes.png) +
    + ![heatmap](./img/heatmap_de_genes.png){ width="400" } +
    + +By examining the heatmap, we can visually identify the expression patterns of our five significant differentially expressed genes. This visualisation allows us to identify how these genes respond to the treatment. The heatmap provides a clear and intuitive way to explore gene expression dynamics. -By examining the heatmap, we can visually identify the expression patterns of our five significant differentially expressed genes. This visualization allows us to identify how these genes respond to the treatment. The heatmap provides a clear and intuitive way to explore gene expression dynamics. ## Over Representation Analysis (ORA) -Finally, we can attempt to assign biological significance to our differentially expressed genes through **Enrichment Analysis (ORA)**. The ORA analysis identifies specific biological pathways, molecular functions and cellular processes, according to the **Gene Ontology (GO)** database, that are enriched within our differentially expressed genes. +Finally, we can attempt to assign biological significance to our differentially expressed genes through **Over Representation Analysis (ORA)**. The ORA analysis identifies specific biological pathways, molecular functions and cellular processes, according to the **Gene Ontology (GO)** database, that are enriched within our differentially expressed genes. -![Enrichment](./img/enrichment_plot.png) +
    + ![enrichment](./img/enrichment_plot.png){ width="400" } +
    The enrichment analysis reveals a possible involvement of cellular structures and processes, including "clathrin-coated pit", "dendritic spine", "neuron spine" and "endoplasmic reticulum lumen". These terms suggest a focus on cellular transport, structural integrity and protein processing, especially in neural contexts. This pattern points to pathways related to cellular organization and maintenance, possibly playing an important role in the biological condition under study. + ## Conclusions -In this tutorial, we have walked through the steps of performing differential expression analysis using DESeq2, from preparing the data to interpreting the results. We have seen how to identify differentially expressed genes, visualise the results and perform enrichment analysis to gain insights into the biological significance of the results. By following this tutorial, you should now be able to perform differential expression analysis using DESeq2 and interpret the results in the context of your own research question. +In this tutorial, we have walked through the steps of the RNA-seq analysis, from launching the nfcore/rnaseq pipeline to interpreting differential expression results. You learned how the data are generated, identified differentially expressed genes, and conducted enrichment analysis. By following this tutorial, you should now be able to use the nfcore/rnaseq pipeline and perform differential expression analysis with DESeq2, interpreting the results within the context of your research. From aa55fe5e06f02fa42d9487e5dabad24066b2867e Mon Sep 17 00:00:00 2001 From: LorenzoS96 <47034913+LorenzoS96@users.noreply.github.com> Date: Thu, 7 Nov 2024 09:24:57 +0100 Subject: [PATCH 020/107] update rnaseq --- docs/usage/DEanalysis/interpretation.md | 5 ++ docs/usage/DEanalysis/rnaseq.md | 64 ++++++++++++++++++------- 2 files changed, 52 insertions(+), 17 deletions(-) diff --git a/docs/usage/DEanalysis/interpretation.md b/docs/usage/DEanalysis/interpretation.md index 9069dd6f1..20a8a7279 100644 --- a/docs/usage/DEanalysis/interpretation.md +++ b/docs/usage/DEanalysis/interpretation.md @@ -1,3 +1,8 @@ +--- +order: 5 +--- + + # Interpretation Once DE genes have been identified, the next crucial step is to interpret the results. This involves the inspection of tables and plots generated during the analysis to understand the biological significance of the data. In this part of the tutorial, we will explore the results by discussing the significant DE genes and we will examine various plots generated during the analysis. diff --git a/docs/usage/DEanalysis/rnaseq.md b/docs/usage/DEanalysis/rnaseq.md index 094a60273..b6a1fa5b8 100644 --- a/docs/usage/DEanalysis/rnaseq.md +++ b/docs/usage/DEanalysis/rnaseq.md @@ -1,67 +1,95 @@ +--- +order: 3 +--- + + # The nf-core/rnaseq pipeline In order to carry out a RNA-Seq analysis we will use the nf-core pipeline [rnaseq](https://nf-co.re/rnaseq/3.14.0). + ## Overview -The pipeline is organised following the diffent blocks: pre-processing, alignment (or lightweight alignment) and quantification, post-processing and final QC. +The pipeline is organised following the diffent blocks shown below: pre-processing, traditional alignment (or lightweight alignment) and quantification, post-processing and final QC. -![Metromap](./img/nf-core-rnaseq_metro_map_grey.png) +
    + ![metromap](./img/nf-core-rnaseq_metro_map_grey.png){ width="1000"} +
    In each process, the users can choose among a range of different options. Importantly, the users can decide to follow one of the two different routes in the alignment and quantification step: -- alignment and quantification (stage 2); -- pseudoalignment and quantification (stage 3). +- traditional alignment and quantification (stage 2); -## Experimental Design -The number of reads and the number of biological replicates are two critical factors that researchers need to carefully consider during the design of a RNA-seq experiment. While it may seem intuitive that having a large number of reads is always desirable, an excessive number can lead to unnecessary costs and computational burdens, without providing significant improvements in quality of the data. Instead, it is often more beneficial to prioritise the number of biological replicates, as it allows to capture the natural biological variation of the data. Biological replicates involve collecting and sequencing RNA from distinct biological samples (e.g., different individuals, tissues, or time points), helping to detect genuine changes in gene expression. +- lightweight alignment and quantification (stage 3). + + +## Experimental Design -::: note +The number of reads and the number of biological replicates are two critical factors that researchers need to carefully consider during the design of a RNA-seq experiment. While it may seem intuitive that having a large number of reads is always desirable, an excessive number can lead to unnecessary costs and computational burdens, without providing significant improvements. Instead, it is often more beneficial to prioritise the number of biological replicates, as it allows to capture the natural biological variation of the data. Biological replicates involve collecting and sequencing RNA from distinct biological samples (e.g., different individuals, tissues, or time points), helping to detect genuine changes in gene expression. +:::warning This concept must not be confused with technical replicates that asses the technical variability of the sequencing platform by sequencing the same RNA sample multiple time. ::: -To obtain optimal results, it is crucial to balance the number of biological replicates and sequencing depth. While deeper sequencing improves the detection of lowly expressed genes, it reaches a plateau, beyond which no additional benefits are gained. Statistical power calculations can inform experimental design by estimating the optimal number of reads and replicates required. For instance, this approach helps establish a suitable log2 fold change (log2FC) threshold for the DE analysis. By incorporating multiple biological replicates into the design and optimizing sequencing depth, researchers can enhance the statistical power of the analysis, reducing the number of false positive results and increasing the reliability of the findings. + +To obtain optimal results, it is crucial to balance the number of biological replicates and the sequencing depth. While increasing the depth of sequencing enhances the ability to detect genes with low expression levels, there is a plateau beyond which no further benefits are gained. Statistical power calculations can inform experimental design by estimating the optimal number of reads and replicates required. For instance, this approach helps to establish a suitable log2 fold change threshold for the DE analysis. By incorporating multiple biological replicates into the design and optimizing sequencing depth, researchers can enhance the statistical power of the analysis, reducing the number of false positive results, and increasing the reliability of the findings. + ## Library design -RNA-seq library design involves critical decisions, including the choice between paired-end and single-end sequencing. Paired-end sequencing provides valuable information on structural variations and transcript isoforms, improving mapping accuracy, especially for longer transcripts and repetitive regions. In contrast, single-end sequencing, where only one end of the fragment is sequenced, can be more cost-effective while still providing high-quality data for gene expression analysis. The decision between paired-end and single-end sequencing ultimately depends on the research question and experimental goals. Paired-end sequencing is preferred for novel transcript identification or isoform characterization, while single-end sequencing is sufficient for gene expression quantification. The type of RNA (e.g., mRNA or total RNA), read length, budget and computational resources can impact the choice. +RNA-seq library design involves important decisions, particularly the choice between paired-end and single-end sequencing. Paired-end sequencing offers insights into structural variations and transcript isoforms, significantly improving mapping accuracy for longer transcripts and repetitive regions. In contrast, single-end sequencing—where only one end of the fragment is sequenced—can be a more cost-effective option while still delivering high-quality data for gene expression analysis. The choice between these two methods ultimately depends on the research question and experimental objectives. Paired-end sequencing is ideal for identifying novel transcripts or characterizing isoforms, whereas single-end sequencing is often sufficient for quantifying gene expression. Factors such as the type of RNA (e.g., mRNA or total RNA), read length, budget, and available computational resources also influence this decision. + ## Reference genome nf-core pipelines make use of the Illumina iGenomes collection as [reference genomes](https://nf-co.re/docs/usage/reference_genomes). -Before starting the analysis, the users might want to check whether the genome they need is part of this collection. -They also might want to consider downloading the reference locally, when running on premises: this would be useful for multiple runs and to speed up the analysis. In this case the parameter `--igenomes_base` might be used to pass the root directory of the downloaded references. + +Before starting the analysis, the users might want to check whether the genome they need is part of this collection. They also might want to consider downloading the reference locally, when running on premises: this would be useful for multiple runs and to speed up the analysis. In this case the parameter `--igenomes_base` might be used to pass the root directory of the downloaded references. One might also need to use custom files: in this case the users might either provide specific parameters at command line (`--fasta` option followed by the genome of choiche), or create a config file adding a new section to the `genome` object. See [here](https://nf-co.re/docs/usage/reference_genomes#custom-genomes) for more details. -We will follow this specific approach in this tutorial, since the data we will be using have been simulated on chromosome 21 of the Human GRCh38 reference, and we have prepared genome fasta and genome index containing only this chromosome locally. The two files are `/workspace/gitpod/training/data/refs/Homo_sapiens_assembly38_chr21.fa` and `/workspace/gitpod/training/data/refs/Homo_sapiens_assembly38_chr21.fa.fai`, respectively. +In this tutorial we will edit the config file, since the data we will be using have been simulated on chromosome 21 of the Human GRCh38 reference, and we have prepared genome fasta and genome index containing only this chromosome locally. + +The two files are `/workspace/gitpod/training/data/refs/Homo_sapiens_assembly38_chr21.fa` and `/workspace/gitpod/training/data/refs/Homo_sapiens_assembly38_chr21.fa.fai`, respectively. + ## Reference annoation -The reference annotation plays a crucial role in the RNA-seq analysis. Without a high-quality reference annotation, RNA-seq analysis would result in inaccurate or incomplete results. The reference annotation provides a precise guide for aligning sequencing reads to specific genomic regions, allowing to identify genes, transcripts and regulatory elements. This is particularly important for identifying novel transcripts and alternative splicing events. +The reference annotation plays a crucial role in the RNA-seq analysis. Without a high-quality reference annotation, RNA-seq analysis would result in inaccurate or incomplete results. The reference annotation provides a precise guide for aligning sequencing reads to specific genomic regions, allowing to identify genes, transcripts, and regulatory elements, as well as novel transcripts and alternative splicing events. nf-core pipelines make use of the Illumina iGenomes collection also as [reference annotation](https://nf-co.re/docs/usage/reference_genomes). The reference annotations are vastly out of date with respect to current annotations and miss certain features. So, the general recommendation is to download a newest annotation version compatible with the genome. A user can utilize the `--gtf` or the `--gff` options to specify the annottation files of choiche, or create a config file adding a new section to the `genome` object. -Similarly to the approach utilised for the genome, in this tutorial we will follow this approach. The annotation files include only the annotated transcripts on chromosome 21 of the Human GRCh38 reference genome and we have already prepared these files locally. The two files are `/workspace/gitpod/training/data/refs/gencode_v29_chr21.gff` and `/workspace/gitpod/training/data/refs/gencode_v29_transcripts_chr21.fa`, respectively. +Similarly to the approach utilised for the genome, in this tutorial we will edit the config file. The annotation files include only the annotated transcripts on chromosome 21 of the Human GRCh38 reference genome and we have already prepared these files locally. + +The two files are `/workspace/gitpod/training/data/refs/gencode_v29_chr21.gff` and `/workspace/gitpod/training/data/refs/gencode_v29_transcripts_chr21.fa`, respectively. + ## Input files The input data should be provided in a CSV file, according to a format that is largely common for nf-core pipelines. The format is described in the [rnaseq usage page](https://nf-co.re/rnaseq/3.14.0/docs/usage). -In the tutorial, the input file is `/workspace/gitpod/training/data/reads/rnaseq_samplesheet.csv`. + +The input file is `/workspace/gitpod/training/data/reads/rnaseq_samplesheet.csv`. + ## Running nf-core/rnaseq In the following sections we will: - prepare our references; + + - set our computational resources in order to be able to run the pipeline on a gitpod VM; + + - edit the optional settings; + + - run the pipeline. + ## Reference and annotation files Following the considerations above, we will first of all edit the `nextflow.config` file in our working directory to add a new genome. @@ -83,12 +111,13 @@ genomes { To speed up the analysis we will include the `star_index` and the `salmon_index` in the config. These files have already been created locally. + ## Computing resources Based on the choices we made when starting up the gitpod environment, we recommend to use the following additional parameters. They can also be added to the parameters directive in the config file we just edited. -```groovy +```groovy title="nextflow.config" params { max_cpus = 2 max_memory = '6.5GB' @@ -119,5 +148,6 @@ nextflow run nf-core/rnaseq -r 3.12.0 \ --skip_qualimap ``` -Notice that we will run the pipeline with STAR as aligner and Salmon in alignment-based mode to quantify gene expression. We will also run the pipeline with Salmon in mapping-based mode to perform a lightweight alignment and quantification. +Notice that we will run the pipeline with STAR as aligner and Salmon in alignment-based mode to quantify gene expression. We will also run the pipeline with Salmon in quasi-mapping mode to perform a lightweight alignment and quantification. + The `skip` parameters were inserted to reduce the running time. From f7e3c972174a1a4f14b59e575510ee5df3d64c9c Mon Sep 17 00:00:00 2001 From: LorenzoS96 <47034913+LorenzoS96@users.noreply.github.com> Date: Thu, 7 Nov 2024 09:30:43 +0100 Subject: [PATCH 021/107] update theory --- docs/usage/DEanalysis/theory.md | 136 +++++++++++++++++++++----------- 1 file changed, 92 insertions(+), 44 deletions(-) diff --git a/docs/usage/DEanalysis/theory.md b/docs/usage/DEanalysis/theory.md index b829ec3c4..d9d095aee 100644 --- a/docs/usage/DEanalysis/theory.md +++ b/docs/usage/DEanalysis/theory.md @@ -1,43 +1,69 @@ +--- +order: 2 +--- + + # RNAseq Analysis Before we dive into the nf-core pipeline for analysing RNA-sequencing data, it's worth looking at some theoretical aspects of RNA-seq. + ## Overview -Given the central role of RNA in a wide range of cellular and molecular functions, RNA-seq has emerged as a powerful tool for measuring the presence and levels of RNA species in biological samples. The technique is based on next-generation sequencing (NGS) technologies and is now considered the gold standard in the field of transcriptomics. +Given the central role of RNA in a wide range of molecular functions, RNA-seq has emerged as a powerful tool for measuring the presence and levels of RNA species in biological samples. The technique is based on next-generation sequencing (NGS) technologies and is now considered the gold standard in the field of transcriptomics. After RNA extraction and reverse transcription into complementary DNA (cDNA), the biological material is sequenced, generating NGS "reads" that correspond to the RNA captured in a specific cell, tissue, or organ at a given time. The sequencing data is then bioinformatically processed through a typical workflow summarised in the diagram below: -![Excalidraw_RNAseq](./img/Excalidraw_RNAseq.png) +
    + ![excalidraw](./img/Excalidraw_RNAseq.png){ width="1000" } +
    + +In the scheme, we can identify three key phases in the workflow: + +- **data pre-processing**: raw reads are handled to remove adapters or contaminants enhancing their quality; + + +- **traditional or lightweight alignment and quantification**: reads are mapped to a reference genome, and gene abundance is estimated. The workflow can also follow an alternative route based on lightweight alignment and quantification, reducing the time required for the analysis. -In the scheme, we can identify three key phases in the workflow: data pre-processing, alignment and quantification, and differential expression analysis. In the data pre-processing step, the raw reads are processed to remove adapters and contaminants, and their quality is checked. Then, reads are mapped to a reference genome, and gene abundance is estimated. The workflow can also follow an alternative route based on lightweight alignment and quantification, reducing the time required for analysis. Finally, differentially expressed genes are identified using statistical tests, annotated, and visualised. + +- **differential expression analysis**: differentially expressed genes are identified using statistical tests, annotated, and visualised. Depending on the user's needs, the workflow can include additional downstream analyses such as functional enrichment analysis, co-expression analysis, and integration with other omics data. + ## Pre-processing -The pre-processing of sequencing reads from RNA-seq data is a critical step to ensure the quality and accuracy of downstream analysis. The raw reads obtained from the sequencer are stored as [FASTQ](https://en.wikipedia.org/wiki/FASTQ_format) files, which contain both the sequence data and quality scores. The initial processing step involves evaluating the quality of the reads and identifying potential issues such as adapter contamination, sequencing errors, or low-quality bases. The presence of adapters (short DNA sequences ligated to the ends of DNA fragments during library preparation) is detected through comparison with known adapter sequences or by using algorithms that identify adapter-like sequences, and removed in a process known as **read trimming**. Next, reads containing contaminants (genomic DNA and/or rRNA) and those with low-quality bases are filtered out. Finally, the quality of the filtered reads is checked again to ensure their suitability for downstream processing. +The pre-processing of sequencing reads from RNA-seq data is a critical step to ensure the quality and accuracy of downstream analysis. The raw reads obtained from the sequencer are stored as [FASTQ](https://en.wikipedia.org/wiki/FASTQ_format) files, which contain both the sequence data and quality scores. The initial processing step involves evaluating the quality of the reads to identify potential issues such as adapter contamination, sequencing errors, or low-quality bases. The presence of adapters (short DNA sequences ligated to the ends of DNA fragments during library preparation) is firstly detected through comparison with known adapter sequences or by using algorithms that identify adapter-like sequences. These sequences are then removed in a process known as **read trimming**. Next, reads containing contaminants (genomic DNA and/or rRNA), and those with low-quality bases are filtered out. Finally, the quality of the filtered reads is checked to ensure their suitability for downstream processing. + ## Alignment (or lightweight alignment) and quantification In the RNA-seq workflow, the alignment step involves mapping sequencing reads to a reference genome or transcriptome to determine the position and orientation of each read relative to the reference sequence. -Errors, gaps, regions of poor sequence quality, insertions/deletions (INDELs), as well as duplicated and repetitive regions in the reference sequence, make this step challenging. Addressing these issues by choosing a high-quality reference and an appropriate aligner is essential for accurate results. A crucial component in the alignment step is the [annotation](https://www.ncbi.nlm.nih.gov/genbank/genomes_gff) file, which can be in General Feature Format (GFF) or Gene Transfer Format (GTF). These files contain key information about the location and structure of genes and transcripts, playing a crucial role in accurate mapping and gene expression quantification. Additionally, RNA-seq data often includes reads that span exon-exon junctions, and the annotation files provide information about splice junctions, allowing for the detection of different isoforms. +Errors, gaps, regions of poor sequence quality, insertions/deletions (INDELs), as well as duplicated, and repetitive regions in the reference sequence, make this step challenging. Addressing these issues by choosing a high-quality reference and an appropriate aligner is essential for accurate results. A crucial component in the alignment step is the [annotation](https://www.ncbi.nlm.nih.gov/genbank/genomes_gff) file, which can be in General Feature Format (GFF) or Gene Transfer Format (GTF). These files contain key information about the location, and structure of genes and transcripts, playing an essential role in accurate mapping and gene expression quantification. Additionally, RNA-seq data often includes reads that span exon-exon junctions, and the annotation files provide information about splice junctions, allowing for the detection of different isoforms. The alignment and quantification steps can follow two different approaches depending on user preferences: - traditional alignment and quantification; + + - lightweight alignment and quantification. -In RNA-seq analysis, traditional alignment is often performed with **splice-aware aligners**, which are designed to recognise the splicing process. These aligners can align reads across known splice junctions and detect novel splice sites or alternative splicing events. They use advanced algorithms and optimisation techniques, like reference genome indexing and parallel processing, to achieve accurate and fast alignment. Popular splice-aware aligners include [STAR](https://github.com/alexdobin/STAR) and [HISAT2](https://github.com/DaehwanKimLab/hisat2). Once alignment is complete, the next step is quantification, which involves estimating the abundance (number of reads) associated with each gene. Tools like [featureCounts](https://subread.sourceforge.net/featureCounts.html), [HTSeq](https://htseq.readthedocs.io), [Salmon](http://combine-lab.github.io/salmon), and [RSEM](http://deweylab.github.io/RSEM) are commonly used for this purpose. +In RNA-seq analysis, traditional alignment is often performed with **splice-aware aligners**, which are designed to recognise the splicing process. These aligners can align reads across known splice junctions and detect novel splice sites or alternative splicing events. Popular splice-aware aligners include [STAR](https://github.com/alexdobin/STAR), and [HISAT2](https://github.com/DaehwanKimLab/hisat2). Once alignment is complete, the following step is quantification, which involves estimating the abundance (number of reads) associated with each gene. Tools like [featureCounts](https://subread.sourceforge.net/featureCounts.html), [HTSeq](https://htseq.readthedocs.io), [Salmon](http://combine-lab.github.io/salmon), and [RSEM](http://deweylab.github.io/RSEM) are commonly used for this purpose. + +Traditional alignment tools are time-consuming and require substantial computational resources, particularly in terms of memory, and CPU usage. Alternatively, methods based on **lightweight alignment** allows for faster analysis by determining the likely origin of reads without a full, base-by-base alignment. Lightweight alignment tools like [Kallisto](https://pachterlab.github.io/kallisto/about.html), [Sailfish](http://www.cs.cmu.edu/~ckingsf/software/sailfish), and [Salmon](https://salmon.readthedocs.io/en/latest/salmon.html) avoid full-scale alignment, providing quantification estimates more quickly than traditional splice-aware algorithms, while still maintaining high accuracy. The quantification results from these tools are often called **pseudocounts** or **abundance estimates**, which can be used for downstream analysis. + +:::info +Salmon can be run in two different modes: alignment-based mode or quasi-mapping mode. In the first mode, Salmon estimates abudance starting from `bam` file obtained with the chosen aligner. In the second mode, Salmon requires a reference transcriptome and raw reads to perform both mapping and quantification. +::: -Traditional alignment tools are time-consuming and require substantial computational resources, particularly in terms of memory and CPU usage. Alternatively, the method known as **lightweight alignment** allows for faster analysis by determining the likely origin of reads without a full, base-by-base alignment. This method is sometimes referred to as **quasi-mapping**. Lightweight alignment tools like [Kallisto](https://pachterlab.github.io/kallisto/about.html), [Sailfish](http://www.cs.cmu.edu/~ckingsf/software/sailfish), and [Salmon](https://salmon.readthedocs.io/en/latest/salmon.html) avoid full-scale alignment, providing quantification estimates more quickly than traditional splice-aware algorithms, while still maintaining high accuracy. The quantification results from these tools are often called **pseudocounts** or **abundance estimates**, which can be used for downstream analysis. ## Differential expression (DE) analysis with DESeq2 -Differential expression analysis is a statistical method for comparing gene expression levels between different experimental conditions, such as disease vs healthy (e.g., tumour tissue vs healthy tissue), treatment vs control (e.g., a sample treated with a specific stimulus, drug, or compound vs an untreated sample), and tissue vs tissue (e.g., brain vs heart). Differential expression analysis aims to assess, for each gene, whether the observed differences in expression between groups are statistically significant, accounting for the variation observed within groups (replicates). This part of the analysis is typically performed in R using various packages that have been developed, such as [DESeq2](https://bioconductor.org/packages/release/bioc/html/DESeq2.html), [edgeR](https://bioconductor.org/packages/release/bioc/html/edgeR.html), and [limma](https://bioconductor.org/packages/release/bioc/html/limma.html). This tutorial focuses on **DESeq2**, a popular R package known for its robustness. For more detailed information, see the [DESeq2 vignette](https://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html). +Differential expression analysis is a statistical method for comparing gene expression levels between different experimental conditions, such as disease vs healthy (e.g., tumour tissue vs healthy tissue), treatment vs control (e.g., a sample treated with a specific stimulus, drug, or compound vs an untreated sample), and tissue vs tissue (e.g., brain vs heart). Differential expression analysis aims to assess, for each gene, whether the observed differences in expression between groups are statistically significant, accounting for the variation observed within groups (replicates). This part of the analysis is typically performed in R using various packages, such as [DESeq2](https://bioconductor.org/packages/release/bioc/html/DESeq2.html), [edgeR](https://bioconductor.org/packages/release/bioc/html/edgeR.html), and [limma](https://bioconductor.org/packages/release/bioc/html/limma.html). This tutorial focuses on **DESeq2**, a popular R package known for its robustness. For more detailed information, see the [DESeq2 vignette](https://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html). + +The analysis begins with the input data, which generally consist of a count matrix obtained during the alignment and quantification step, summarising the expression levels of the different genes in each sample of the dataset. The rows of the matrix typically correspond to genes, while the columns represent the samples. Another essential prerequisite is a metadata table describing the samples. -The analysis begins with the input data, which generally consist of a matrix obtained during the alignment and quantification step, summarising the expression levels of the different genes in each sample of the dataset. The rows of the matrix typically correspond to genes, and the columns represent the samples. Another essential prerequisite is a metadata table describing the samples. ### Quality Control @@ -45,17 +71,18 @@ To ensure robust differential expression results, it is common to start by explo Quality control in DESeq2 typically uses **variance-stabilised (vst)** or **regularised log-transformed (rlog)** counts. Since raw counts in RNA-seq follow a discrete distribution, which is not suitable for many statistical and machine learning algorithms that assume continuous distributions, transformations such as `vst` and `rlog` are applied to stabilise the variance across genes. This stabilisation ensures that, after transformation, genes with both low and high expression levels have variances that are more comparable, making the data more suitable for downstream analyses. -Finally, it is often beneficial to filter out genes that are unlikely to show differential expression, such as those with zero counts across samples or extreme count outliers. By filtering out these genes, we can increase the sensitivity of our differential expression analysis and reduce the risk of false positives. +Finally, it is often beneficial to filter out genes that are unlikely to show differential expression, such as those with zero counts across samples or extreme count outliers. By filtering out these genes, we can increase the sensitivity of our differential expression analysis, and reduce the risk of false positives. -!!! note +:::note +The `vst` or `rlog` transformations are applied to the normalised counts stored in the `dds` object, which is generated by running either the `DESeq()` or `estimateSizeFactors()` function. Since the estimation of size factors is an early step in the `DESeq()` function, the transformations are generally applied immediately afterwards. +::: - The `vst` or `rlog` transformations are applied to the normalised counts stored in the `dds` object, which is generated by running either the `DESeq()` or `estimateSizeFactors()` function. Since the estimation of size factors is an early step in the `DESeq()` function, the transformations are generally applied immediately afterwards. ### Design Formula The design formula specifies the sources of variation that the statistical model needs to account for. It defines how the counts will be related to the experimental conditions, allowing the software to model the relationship between gene expression and the factors of interest, such as treatment, time points or batch effects. It is important to specify the main factor of interest as well as additional covariates in the design formula. -A well-defined design formula can account for potential confounding variables. For instance, if the experiment includes multiple conditions or batches, specifying these in the design helps to isolate the effect of the primary variable of interest on gene expression. An example is provided below: +A well-defined design formula can account for potential confounding variables. For instance, if the experiment includes multiple conditions, specifying these in the design helps to isolate the effect of the primary variable of interest on gene expression. An example is provided below: ```r # Basic design with a single factor of interest @@ -67,67 +94,88 @@ design = ~ condition design = ~ sex + developmental_stage + condition ``` -!!! note - - In R, the tilde (`~`) is used in formula syntax to specify relationships between variables in statistical models. Here, it indicates that gene counts (dependent variable) will be modelled as a function of the specified variables (predictors). +:::tip +In R, the tilde (`~`) is used in formula syntax to specify relationships between variables in statistical models. Here, it indicates that gene counts (dependent variable) will be modelled as a function of the specified variables (predictors). +::: The results will not be affected by the order of variables but the common practice is to specify the main source of variation in the last position of the design formula. + ### Differential Expression Analysis RNA-seq data typically contain a large number of genes with low expression counts, indicating that many genes are expressed at very low levels across samples. At the same time, RNA-seq data exhibit a skewed distribution with a long right tail due to the absence of an upper limit for gene expression levels. This means that while most genes have low to moderate expression levels, a small number are expressed at high levels. Accurate statistical modelling must therefore account for this distribution to avoid misleading conclusions. -![RNAseq](./img/count_distribution.png) +
    + ![count_distribution](./img/count_distribution.png){ width="400"} +
    The core of the differential expression analysis is the `DESeq()` function, a wrapper that streamlines several key steps into a single command. The different functions are listed below: -![RNAseq](./img/DESeq_function.png) - -!!! note +
    + ![deseq2_function](./img/DESeq_function.png){ width="400"} +
    - While `DESeq()` combines these steps, a user could choose to perform each function separately to have more control over the whole process. +:::note +While `DESeq()` combines these steps, a user could choose to perform each function separately to have more control over the whole process. +::: The different steps are explained in detail below: -1. **Normalisation**: since DESeq2 compares counts between sample groups for the same gene, it does not need to adjust for gene length. However, it is essential to account for variations in sequencing depth and RNA composition among samples. To normalise the data, DESeq2 utilises size factors, which correct for these variations in library sizes and RNA composition. +1) **Normalisation**: since DESeq2 compares counts between sample groups for the same gene, it does not need to adjust for gene length. However, it is essential to account for variations in sequencing depth and RNA composition among samples. To normalise the data, DESeq2 utilises **size factors**. The size factors are calculated using the **median ratio** method: -- **Calculate the geometric mean**: For each gene, compute the geometric mean of its counts across all samples. This gives a row-wise geometric mean for each gene; -- **Calculate ratios**: Divide each gene's count by its geometric mean to obtain a ratio for each sample; -- **Determine size factors**: For each sample, take the median of these ratios (column-wise) to derive the size factors; -- **Normalise counts**: Divide each raw count by the corresponding sample size factor to generate normalised counts. +- **Calculate the geometric mean**: for each gene, compute the geometric mean of its counts across all samples. This gives a row-wise geometric mean for each gene; + -!!! note +- **Calculate ratios**: divide each gene's count by its geometric mean to obtain a ratio for each sample; - While normalised counts are useful for downstream visualisation of results, they should not be used as input for DESeq2. Instead, DESeq2 requires count data in the form of a matrix of integer values. -2. **Estimate dispersion and gene-wise dispersion**: the dispersion is a measure of how much the variance deviates from the mean. The estimation of dispersion is essential to model the variance of the count data. Importantly, RNA-seq data are characterised by **overdispersion**, where the variance in gene expression levels often exceeds the mean (variance > mean). +- **Determine size factors**: for each sample, take the median of these ratios (column-wise) to derive the size factors; -![RNAseq](./img/overdispersion.png) -DESeq2 addresses this issue by employing the **negative binomial distribution**, which generalises the Poisson distribution by introducing an additional dispersion parameter. This parameter quantifies the extra variability present in RNA-seq data, providing a more realistic representation than the Poisson distribution, which assumes equal mean and variance. DESeq2 starts by estimating the **common dispersion**, a single estimate of dispersion applicable to all genes in the dataset. This estimate provides a baseline for variability across all genes in the dataset. Next, DESeq2 estimates **gene-wise dispersion**, a separate estimate of dispersion for each individual gene, taking into account that different genes may exhibit varying levels of expression variability due to biological differences. -The dispersion parameter (α) is related to the mean (μ) and variance of the data, as described by the equation: +- **Normalise counts**: divide each raw count by the corresponding sample size factor to generate normalised counts. + +:::warning +While normalised counts are useful for downstream visualisation of results, they should not be used as input for DESeq2. Instead, DESeq2 requires raw count data in the form of a matrix of integer values. +::: + +2) **Estimate dispersion and gene-wise dispersion**: the dispersion is a measure of how much the variance deviates from the mean. The dispersion estimates indicate the variance in gene expression at a specific mean expression level. Importantly, RNA-seq data are characterised by **overdispersion**, where the variance in gene expression levels often exceeds the mean (variance > mean). + +
    + ![overdispersion](./img/overdispersion.png){ width="400"} +
    + +DESeq2 addresses this issue by employing the **negative binomial distribution**, which generalises the Poisson distribution by introducing an additional dispersion parameter. This parameter quantifies the extra variability present in RNA-seq data, providing a more realistic representation than the Poisson distribution, which assumes mean = variance. DESeq2 starts by estimating the **common dispersion**, a single estimate of dispersion applicable to all genes in the dataset. This estimate provides a baseline for variability across all genes in the dataset. Next, DESeq2 estimates **gene-wise dispersion**, a separate estimate of dispersion for each individual gene, taking into account that different genes may exhibit varying levels of expression variability due to biological differences. +The dispersion parameter (α) is related to the mean (μ), and variance of the data, as described by the equation: `Var = μ + α ⋅ μ²` -A key feature of DESeq2's dispersion estimates is their negative correlation with the mean and positive correlation with variance. Genes with low expression have higher dispersion values, while genes with high expression tend to have lower dispersion. Additionally, genes sharing similar mean expression levels can exhibit different dispersion estimates based on their variance. To improve the accuracy of dispersion estimates, DESeq2 assumes that genes with similar expression profiles share similar dispersion patterns and leverages this information to refine the estimates. +A key feature of DESeq2's dispersion estimates is their negative correlation with the mean, and positive correlation with the variance. Genes with low expression tend to have higher dispersion values, while genes with high expression tend to have lower dispersion. + +3) **Mean-dispersion relationship**: this process, known as dispersion fitting, models the relationship between the mean expression level of a gene, and its dispersion. DESeq2 assumes that genes with similar expression profiles share similar dispersion patterns, and leverages this information to refine the estimates identifying a trend in the dispersion estimates across genes. The fitted curve, typically a smooth curve, describes how dispersion changes as a function of the mean expression level. + +4) **Final dispersion estimates**: DESeq2 refines the gene-wise dispersion by shrinking it towards the fitted curve. The "shrinkage" helps control for overfitting, and makes the dispersion estimates more reliable. The strength of the shrinkage depends on the sample size (more samples = less shrinkage), and how close the initial estimates are to the fitted curve. -3. **Mean-dispersion relationship**: this process, known as dispersion fitting, models the relationship between the mean expression level of a gene and its dispersion. In this process, DESeq2 identifies a trend in the dispersion estimates across genes. The fitted curve, typically a smooth curve, describes how dispersion changes as a function of the mean expression level. +
    + ![dispersion](./img/dispersion_estimates.png){ width="400"} +
    -4. **Final dispersion estimates**: DESeq2 refines the gene-wise dispersion by shrinking it towards the fitted curve. The "shrinkage" helps control for overfitting, particularly in genes with low counts or few replicates, and makes the dispersion estimates more reliable. However, genes with exceptionally high dispersion values are not shrunk because they likely deviate from the modelling assumptions, exhibiting elevated variability due to biological or technical factors. Shrinking these values could lead to false positives. +The initial estimates (black dots) are shrunk toward the fitted curve (red line) to obtain the final estimates (blue dots). However, genes with exceptionally high dispersion values are not shrunk, as they likely deviate from the model assumptions exhibiting elevated variability due to biological or technical factors. Shrinking these values could lead to false positives. -5. **Fitting model and testing**: the initial step in hypothesis testing involves defining a **null hypothesis** for each gene. The null hypothesis states that there is no difference in expression between the tested sample groups (LFC == 0). Next, DESeq applies a statistical test to assess whether the null hypothesis is true. - DESeq2 fits a **generalised linear model (GLM)** to the normalised counts using the calculated size factors and final dispersion estimates. A GLM is a flexible extension of linear regression that models the relationship between a response variable (normalised counts) and predictors (e.g., condition). - By using a **negative binomial distribution**, DESeq2's GLM can handle the additional variability in gene expression counts that often occurs in RNA-seq data.Once the model is fit, coefficients are estimated for each sample group along with their standard errors. These coefficients represent the estimated log2 fold changes between groups and serve as the basis for hypothesis testing, using either a **Wald test** or a **Likelihood Ratio Test (LRT)**, depending on the experimental design: +5) **Fitting model and testing**: the initial step in hypothesis testing involves defining a **null hypothesis** for each gene. In DESeq2, The null hypothesis states that there is no difference in expression between the sample groups (log2 fold change == 0). Next, DESeq applies a statistical test to assess whether the null hypothesis is true. +DESeq2 fits a **generalised linear model (GLM)** to the normalised counts using the calculated size factors and final dispersion estimates. A GLM is a flexible extension of linear regression that models the relationship between a response variable (normalised counts), and predictors (e.g., condition). +By using a **negative binomial distribution**, DESeq2's GLM can handle the additional variability in gene expression counts that often occurs in RNA-seq data. Once the model is fit, coefficients are estimated for each sample group along with their standard errors. These coefficients represent the estimated log2 fold changes between groups, and serve as the basis for hypothesis testing, using either a **Wald test** or a **Likelihood Ratio Test (LRT)**, depending on the experimental design: -- **Wald Test**: The Wald test is ideal for simpler experimental designs, such as comparing two conditions (e.g., treated vs untreated). It tests whether the estimated effect size (log2 fold change) of a predictor variable (like treatment) is significantly different from zero. This test provides direct estimates of fold changes with associated p-values, making it computationally efficient for straightforward comparisons. -- **Likelihood Ratio Test (LRT)**: The LRT is more suitable for complex experimental designs involving multiple variables (e.g., time points, treatments and batches). It compares the fit of two nested models: one with the factor of interest (e.g., treatment) and one without it to assess whether including that factor significantly improves model fit. This approach allows DESeq2 to account for confounding variables and to isolate the effect of specific variables on gene expression, offering flexibility for multi-factor analyses. +- **Wald Test**: The Wald test is ideal for simpler experimental designs, such as comparing two conditions (e.g., treated vs untreated). In DESeq2, the Wald test is implemented by calculating the log2 fold change and dividing it by its standard error to obtain a z-statistic. This z-statistic is then evaluated against a standard normal distribution, leading to the computation of a p-value that indicates the likelihood of observing a z-statistic as extreme as the calculated value under the null hypothesis. When the p-value is small, we reject the null hypothesis, concluding that the gene is differentially expressed. -When performing multiple tests, such as in the case of RNA-seq data where thousands of genes are tested, the risk of false positives increases. To account for this, DESeq2 employs multiple test correction methods (the Benjamini-Hochberg procedure is the default) to adjust the p-values and control the false discovery rate (FDR). +- **Likelihood Ratio Test (LRT)**: The LRT is more suitable for complex experimental designs involving multiple variables. It compares the fit of two nested models: the full model, which includes the factor of interest (e.g., treatment), and the reduced model, which excludes that factor. This approach allows DESeq2 to account for confounding variables and to isolate the effect of specific variables on gene expression. In contrast to the Wald test, which assesses up- or down-regulation between two conditions, the LRT identifies genes that exhibit changes in expression in any direction across multiple sample classes, thereby making it more appropriate for multi-factor analyses. -!!! note +Regardless of the utilised test, for each gene DESeq2 computes a log2 fold change along with an associated p-value. The p-value is the result of a single test (single gene), but in the case of RNA-seq data thousands of genes are tested, increasing the false positive rate. +To account for this, DESeq2 employs multiple test correction methods (the Benjamini-Hochberg procedure is the default) to adjust the p-values, and control the false discovery rate (FDR). The FDR is the expected proportion of false positives among the identified significant results. - The FDR is the expected proportion of false positives among the identified significant results. For example, by setting the FDR cutoff to < 0.05, 5% of genes identified as differentially expressed are expected to be false positives. For instance, if 400 genes are identified as differentially expressed with an FDR cutoff of 0.05, you would expect 20 of them to be false positives. +:::info +By setting the FDR cutoff to < 0.05, 5% of genes identified as differentially expressed are expected to be false positives. For instance, if 400 genes are identified as differentially expressed with an FDR cutoff of 0.05, you would expect 20 of them to be false positives. +::: -After identifying DE genes using DESeq2, it is essential to interpret the biological significance of these genes through functional analysis. This involves examining the roles of the differentially expressed genes in various biological processes, molecular functions and pathways, providing insights into the underlying mechanisms driving the observed changes in gene expression. This interpretation can help in discovering pathways involved in disease or identifying potential therapeutic targets. Different tools are available to carry out these functional analyses, such as [Gene Ontology](https://geneontology.org), [Reactome](https://reactome.org/), [KEGG](https://www.genome.jp/kegg), [clusterProfiler](https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html), [g:Profiler](https://biit.cs.ut.ee/gprofiler) and [WikiPathways](https://www.wikipathways.org). +After identifying DE genes using DESeq2, it is essential to interpret the biological significance of these genes through functional analysis. This involves examining the roles of the differentially expressed genes in various biological processes, molecular functions, and pathways, providing insights into the underlying mechanisms driving the observed changes in gene expression. This interpretation can help in discovering pathways involved in disease or identifying potential therapeutic targets. Different tools are available to carry out these functional analyses, such as [Gene Ontology](https://geneontology.org), [Reactome](https://reactome.org/), [KEGG](https://www.genome.jp/kegg), [clusterProfiler](https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html), [g:Profiler](https://biit.cs.ut.ee/gprofiler), and [WikiPathways](https://www.wikipathways.org). From dbc526043de1bc9e7a9518f7235e87bf66c1fd66 Mon Sep 17 00:00:00 2001 From: LorenzoS96 <47034913+LorenzoS96@users.noreply.github.com> Date: Thu, 7 Nov 2024 09:45:33 +0100 Subject: [PATCH 022/107] update de_rstudio --- docs/usage/DEanalysis/de_rstudio.md | 207 +++++++++++++++++++--------- 1 file changed, 142 insertions(+), 65 deletions(-) diff --git a/docs/usage/DEanalysis/de_rstudio.md b/docs/usage/DEanalysis/de_rstudio.md index 89a258c3a..761e63dc0 100644 --- a/docs/usage/DEanalysis/de_rstudio.md +++ b/docs/usage/DEanalysis/de_rstudio.md @@ -1,13 +1,19 @@ +--- +order: 4 +--- + + # Differential Analysis with DESeq2 In this section of the tutorial, we will guide you through the practical steps necessary to set up the RStudio environment, load the required libraries and data, and execute the DESeq2 analysis. By the end of this section, you will have a fully functional DESeq2 analysis pipeline set up in RStudio, ready to uncover the differentially expressed genes in your dataset. + ## Launching the RStudio environment Once the nf-core/rnaseq pipeline is terminated, the resulting data are stored in the folder `results_star_salmon`. Now, we can analyse the results by running DESeq2 on RStudio. First of all, we need to launch it: ```bash -sudo rstudio-server start +sudo rstudio-server start && sleep 5000 ``` A pop-up will appear and by clicking on **Open**, we will be redirected to the RStudio login page. By inserting the username and the password reported below, we will be able to connect to RStudio: @@ -17,29 +23,26 @@ Username: gitpod Password: pass ``` -To prevent losing connection, go back to gitpod and type on the **Terminal**: - -```bash -sleep 2h -``` - -This command will keep the gitpod session active for exactly 2 hours, providing sufficient time to complete our analysis without interruption. +:::note +Using `sleep` will keep the Gitpod session active, preventing disconnection and providing enough time to complete our analysis without interruptions +::: -Now come back to our **RStudio session**. ## Differential Expression Analysis As in all analysis, firstly we need to create a new project: -1. Go to the **File** menu and select **New Project**; +1) Go to the **File** menu and select **New Project**; -2. Select **New Directory**, **New Project**, name the project as shown below and click on **Create Project**; +2) Select **New Directory**, **New Project**, name the project as shown below and click on **Create Project**; -![R_project](./img/project_R.png) +
    + ![r_project](./img/project_R.png){ width="400" } +
    -3. The new project will be automatically opened in RStudio +3) The new project will be automatically opened in RStudio. -We can check whether we are in the correct working directory with `getwd()` The path `/workspace/gitpod/training/DE_analysis/` should appear on your console. +We can check whether we are in the correct working directory with `getwd()`. The path `/workspace/gitpod/training/DE_analysis/` should appear on your console. To store our results in an organized way, we will create a folder named **de_results** using the **New Folder** button in the bottom right panel. We will save all our resulting tables and plots in this folder. Next, go to the **File menu**, select **New File** and then **R Script** to create a script editor in which we will save all commands required for the analysis. In the editor type: ```r @@ -48,7 +51,9 @@ To store our results in an organized way, we will create a folder named **de_res and save the file as **de_script.R**. From now on, each command described in the tutorial can be added to your script. The resulting working directory should look like this: -![Workdir](./img/workdir_RStudio.png) +
    + ![work_dir](./img/workdir_RStudio.png){ width="600" } +
    The analysis requires several R packages. To utilise them, we need to load the following libraries: @@ -78,7 +83,7 @@ library("RColorBrewer") library("ggrepel") ``` -and the pre-computed DESeq2 object (`dds`) generated by the nfcore/rnaseq pipeline: +and the pre-computed DESeq2 object (`dds`) generated by the nfcore/rnaseq pipeline. In this tutorial we will analyse the `dds` object generated by running the alignment with **STAR** and the quantification with **Salmon**: ```r #### Import the dds obtained from nfcore/rnaseq #### @@ -86,13 +91,17 @@ and the pre-computed DESeq2 object (`dds`) generated by the nfcore/rnaseq pipeli load("/workspace/gitpod/training/results_star_salmon/star_salmon/deseq2_qc/deseq2.dds.RData") ``` -In this tutorial we will analyse the `dds` object generated by running the alignment with **STAR** and the quantification with **Salmon**. Alternatively, a user could choose to analyse the the `dds` object generated by running only **Salmon** for both lightweight alignment and quantification. The file is stored in `/workspace/gitpod/training/results_star_salmon/salmon/deseq2_qc/deseq2.dds.RData`. +Alternatively, a user could choose to analyse the the `dds` object generated by running only **Salmon** for both lightweight alignment and quantification. In DESEq2, the `dds` object is a central data structure that contains the following components: - `countData`: a matrix of raw count data, where each row represents a gene and each column represents a sample; + + - `colData`: a data frame containing information about the samples, such as the experimental design, treatment and other relevant metadata; -- `design`: a formula specifying the experimental design utilised to estimate the dispersion and the log2foldchange. + + +- `design`: a formula specifying the experimental design utilised to estimate the dispersion and the log2 fold change. All these components can be checked with specific commands: @@ -126,9 +135,9 @@ rownames(metadata) <- colnames(counts(dds)) colData(dds) <- metadata ``` -!!! note - - With this operation we also eliminate the `sizeFactors` already estimated by the **nfcore DESeq2 module**. +:::note +With this operation we also eliminate the `sizeFactors` already estimated by the nfcore/rnaseq pipeline. +::: To avoid errors in DESeq2 is essential to check that sample names match between the `colData` and the `countData`, and that the sample are in the correct order: @@ -158,7 +167,9 @@ design(dds_new) # to check the design formula Comparing the structure of the newly created dds (`dds_new`) with the one automatically produced by the pipeline (`dds`), we can observe the differences: -![Comparison_dds](./img/dds_comparison.png) +
    + ![comparison_dds](./img/dds_comparison.png){ width="400" } +
    Before running the different steps of the analysis, a good practice consists in pre-filtering the genes to remove those with very low counts. This is useful to improve computional efficiency and enhance interpretability. In general, it is reasonable to keep only genes with a sum counts of at least 10 for a minimal number of 3 samples: @@ -186,13 +197,15 @@ Now, it is time to run the differential expression analysis with the `DESeq()` f dds_final <- DESeq(dds_filtered) ``` -The `DESeq()` function is a high-level wrapper that simplifies the process of differential expression analysis by combining multiple steps into a single function call. +The `DESeq()` function is a high-level wrapper that simplifies the process of differential expression analysis by combining multiple steps into a single function call. This makes the workflow more user-friendly and ensures that all necessary preprocessing and statistical steps are executed in the correct order. The key functions that **DESeq2** calls include: + +- `estimateSizeFactors`: to normalise the count data; + + +- `estimateDispersion`: to estimate the dispersion; -This makes the workflow more user-friendly and ensures that all necessary preprocessing and statistical steps are executed in the correct order. The key functions that **DESeq2** calls include: -- **estimateSizeFactors**: to normalise the count data; -- **estimateDispersion**: to estimate the dispersion; -- **nbinomWaldTest**: to perform differential expression test. +- `nbinomWaldTest`: to perform differential expression test. The individual functions can be carried out also singularly as shown below: @@ -206,8 +219,11 @@ dds_final <- estimateDispersions(dds_final) dds_final <- nbinomWaldTest(dds_final) ``` -The next step in the DESeq2 workflow is to perform quality control (QC) analysis on our data. This analysis is crucial for identifying potential issues or biases and ensuring the data are suitable for downstream analysis. For QC analysis, it is useful to work with transformed versions of the count data, `variance stabilizing transformations (vst)` and `regularized logarithm (rlog)`. While, the rlog is more robust to outliers and extreme values, vst is computationally faster and so preferred for larger dataset. -Notice that these transformations are used for visualisation purposes, while DESeq2 requires raw counts for differential expression analysis. +The next step in the DESeq2 workflow is to perform quality control (QC) analysis on our data. This analysis is crucial for identifying potential issues ensuring that the data are suitable for downstream analysis. For QC analysis, it is useful to work with transformed versions of the count data, `variance-stabilised (vst)` or `regularised log-transformed (rlog)` counts. While, the rlog is more robust to outliers and extreme values, vst is computationally faster and so preferred for larger dataset. + +:::warning +These transformations are used for visualisation purposes, while DESeq2 requires raw counts for differential expression analysis. +::: ```r #### Transform normalised counts for data visualisation #### @@ -219,6 +235,8 @@ rld <- rlog(dds_final, blind = TRUE) The `rlog` and the `vst` transformations have an argument, **blind** that can be set to: - **TRUE** (default): useful for QC analysis because it re-estimates the dispersion, allowing for comparison of samples in an unbiased manner with respect to experimental conditions; + + - **FALSE**: the function utilizes the already estimated dispersion, generally applied when differences in counts are expected to be due to the experimental design. Next, we perform Principal Component Analysis (PCA) to explore the data. DESeq2 provides a built-in function, `plotPCA()`, which uses [ggplot2](https://ggplot2.tidyverse.org) for visualisation, taking the `rld` (or the `vst`) object as input. @@ -227,7 +245,11 @@ Since the **treatment** is the principal condition of interest in our metadata, ```r #### Plot PCA #### -plotPCA(rld, intgroup = "condition") +pca_plot <- plotPCA(rld, intgroup = "condition") + +# Save the plot + +ggsave("de_results/pca_plot.png", plot = pca_plot, width = 6, height = 5, dpi = 300) ``` The second essential step in QC analysis is hierarchical clustering. Although DESeq2 does not have a built-in function for this analysis, we can use the `pheatmap()` function from the **pheatmap** package. @@ -256,12 +278,16 @@ colors <- colorRampPalette(rev(brewer.pal(9, "Greens")))(255) # function from RC # Create the heatmap -pheatmap(sampleDistMatrix, - clustering_distance_rows = sampleDists, - clustering_distance_cols = sampleDists, - col = colors, - fontsize_col = 8, - fontsize_row = 8) +clustering_plot <- pheatmap(sampleDistMatrix, + clustering_distance_rows = sampleDists, + clustering_distance_cols = sampleDists, + col = colors, + fontsize_col = 8, + fontsize_row = 8) + +# Save the plot + +ggsave("de_results/clustering_plot.png", plot = clustering_plot, width = 6, height = 5, dpi = 300) ``` The normalised counts stored in the `dds` can be inspected with the `counts()` function and saved in our results folder: @@ -290,7 +316,7 @@ normalised_counts$gene <- rownames(counts(dds_final)) normalised_counts <- normalised_counts %>% relocate(gene, .before = control_rep1) -# Save normalised counts +# Save the normalised counts write.csv(normalised_counts, file = "de_results/normalised_counts.csv") ``` @@ -298,13 +324,28 @@ write.csv(normalised_counts, file = "de_results/normalised_counts.csv") The `results()` function in DESeq2 is used to extract the results of the DE analysis. This function takes the `dds` object as input and returns a DataFrame containing the results of the analysis: - **baseMean**: the average expression level of the gene across all samples; + + - **log2FoldChange**: the log2 fold change of the gene between the condition of interest and the reference level; + + - **lfcSE**: the standard error of the log2 fold change; + + - **stat**: the Wald statistic, which is used to calculate the p-value; -- **pvalue**: the p-value from the Wald test indicates the probability of observing the measured difference in gene expression (log2 fold change) by chance, assuming no true difference exists (null hypothesis). A low p-value suggests that the observed expression change between samples is unlikely due to random chance, so we can reject the null hypothesis; -- **padj**: the adjusted p-value, which takes into account multiple testing corrections, using the Benjamini-Hochberg method to control the false discovery rate; -By default, the `results()` function returns the results for all genes in the analysis with an adjusted p-value below a specific FDR cutoff, set by default to 0.1. This threshold can be modified with the parameter `alpha`. The `results()` function can also be customised to filter the results based on certain criteria (log2 fold change or padj) or to set a specific contrast. A contrast is a specific comparison between two or more levels of a factor, such as the comparison between the treatment and control groups. The order of the contrast names determines the direction of the fold change that is reported in the results. Specifically, the first level of the contrast is the condition of interest and the second level is the reference level. + +- **pvalue**: the p-value from the Wald test indicates the probability of observing the measured difference in gene expression (log2 fold change) by chance, assuming no true difference exists (null hypothesis). A low p-value suggests that the observed expression change between samples is unlikely due to random chance, so we can reject the null hypothesis --> the gene is differentially expressed; + + +- **padj**: the adjusted p-value, which takes into account multiple testing corrections, (Benjamini-Hochberg method default) to control the false discovery rate; + +The `results()` function returns the results for all genes in the analysis with an adjusted p-value below a specific FDR cutoff, set by default to 0.1. This threshold can be modified with the parameter `alpha`. The `results()` function can also be customised to filter the results based on certain criteria (log2 fold change or padj) or to set a specific contrast (specific comparison between two or more levels). + +:::tip +The order of the contrast names determines the direction of the fold change that is reported in the results. Specifically, the first level of the contrast is the condition of interest and the second level is the reference level. +::: + Notice that in this tutorial the contrast is already correctly specified. ```r @@ -345,16 +386,16 @@ res_viz <- as_tibble(res_viz) %>% write.csv(res_viz, file = "de_results/de_result_table.csv") ``` -In the _Experimental Design_ section, we emphasised the importance of estimating the log2 fold change threshold using a statistical power calculation, rather than selecting it arbitrarily. This approach ensures that the chosen threshold is statistically appropriate and tailored to the specifics of the experiment. However, since we are working with simulated data for demonstration purposes, we will use a padj threshold of 0.05 and consider genes with a log2 fold change greater than 1 or lower than -1 as differentially expressed. +In the *Experimental Design* section, we emphasised the importance of estimating the log2 fold change threshold using a statistical power calculation, rather than selecting it arbitrarily. This approach ensures that the chosen threshold is statistically appropriate and tailored to the specifics of the experiment. However, since we are working with simulated data for demonstration purposes, we will use a padj threshold of 0.05 and consider genes with a log2 fold change of at least 1 or -1 as differentially expressed. ```r #### Extract significant DE genes from the results #### -# Filter the results to include only significantly differentially expressed genes with an adjusted p-value (padj) less than 0.05 and a log2foldchange greater than 1 or less than -1 +# Filter the results to include only significantly DE genes with a padj less than 0.05 and a log2FoldChange of at least 1 or -1 resSig <- subset(res_viz, padj < 0.05 & abs(log2FoldChange) > 1) -# Convert the results to a tibble for easier manipulation and relocate the 'Gene' column to be the first column +# Convert the results to a tibble for easier manipulation and relocate the gene column to the first position resSig <- as_tibble(resSig) %>% relocate(gene, .before = baseMean) @@ -372,26 +413,46 @@ resSig write.csv(resSig, file = "de_results/sig_de_genes.csv") ``` -Now that we have obtained the results of the differential expression analysis, it's time to visualise the data to gain a deeper understanding of the biological processes that are affected by the experimental conditions. Visualisation is a crucial step in RNA-seq analysis, as it allows us to identify patterns and trends in the data that may not be immediately apparent from the numerical results. In the following sections, we will explore different types of plots that are commonly used to visualise the results of RNA-seq analysis, including: -- **MA plot**: it is a type of scatter plot that is commonly used to visualise the results of differential expression analysis for all the samples. The plot displays the mean of the normalised counts on the x-axis and the log2 fold change on the y-axis. This allows the visualisation of the relationship between the magnitude of the fold change and the mean expression level of the genes. Genes that are highly differentially expressed will appear farthest from the horizontal line, while genes with low expression levels will appear closer to the line. The MA plot is useful for identifying genes that are both highly expressed and highly differentially expressed between two conditions. +## Plot the results + +Now that we have obtained the results of the differential expression analysis, it's time to visualise the data to gain a deeper understanding of the biological processes that are affected by the experimental conditions. Visualisation is a crucial step in RNA-seq analysis, as it allows us to identify patterns and trends in the data that may not be immediately apparent from the numerical results. + +In the following sections, we will explore different types of plots that are commonly used to visualise the results of RNA-seq analysis, including: + +- **MA plot**: scatter plot commonly utilised to visualise the results of the DE analysis for all the samples. The plot displays the mean of the normalised counts on the x-axis and the log2 fold change on the y-axis. This allows the visualisation of the relationship between the magnitude of the fold change and the mean expression level of the genes. Genes that are differentially expressed will appear farthest from the horizontal line, while genes with low expression levels will appear closer to the line. ```r #### MA plot #### -plotMA(res, ylim = c(-2,2)) +# The MA plot is not a ggplot, so we have to save it in a different way + +# Open a graphics device to save the plot as a PNG file + +png("MA_plot.png", width = 1500, height = 100, res = 300) + +# Generate the MA plot (it will be saved to the file instead of displayed on screen) + +plotMA(res, ylim = c(-2, 2)) + +# Close the device to save the file + +dev.off() ``` -- **counts plot**: it plots the normalised counts for a single gene across the different conditions in your experiment. It’s particularly useful for visualising the expression levels of specific genes of interest and comparing them across sample groups. +- **counts plot**: plot of the normalised counts for a single gene across the different conditions in your experiment. It’s particularly useful for visualising the expression levels of specific genes of interest and comparing them across sample groups. ```r #### Plot a specific gene in this case ENSG00000142192, a DE gene #### +png("de_results/plotCounts.png", width = 1000, height = 1200, res = 300) + plotCounts(dds_final, gene = "ENSG00000142192") + +dev.off() ``` **heatmap**: plot of the normalised counts for all the significant genes obtained with the `pheatmap()` function. The heatmap provides insights into genes and sample relationships that may not be apparent from individual gene plots alone. -Notiche that this plot is based on the normalised counts. ```r #### Heatmap #### @@ -400,23 +461,27 @@ Notiche that this plot is based on the normalised counts. significant_genes <- resSig[, 1] -# Extract normalised counts for significant genes from the normalised counts matrix and convert the "gene" column to row names +# Extract normalised counts for significant genes from the normalised counts matrix and convert the gene column to row names significant_counts <- inner_join(normalised_counts, significant_genes, by = "gene") %>% column_to_rownames("gene") # Create the heatmap using pheatmap -pheatmap(significant_counts, - cluster_rows = TRUE, - fontsize = 8, - scale = "row", - fontsize_row = 8, - height = 10) +heatmap <- pheatmap(significant_counts, + cluster_rows = TRUE, + fontsize = 8, + scale = "row", + fontsize_row = 8, + height = 10) + +# Save the plot + +ggsave("de_results/heatmap.png", plot = heatmap, width = 6, height = 5, dpi = 300) ``` -- **volcano plot**: it is a type of scatter plot that displays the log2 fold change on the x-axis and the log transformed padj on the y-axis. This allows for the visualisation of both the magnitude and significance of the changes in gene expression between two conditions. Genes that are highly differentially expressed (i.e., have a large log2 fold change) and are statistically significant (i.e., have a low padj) will appear in the top-left or top-right corners of the plot making easier to identify the most biologically meaningful changes. +- **volcano plot**: scatter plot that displays the log2 fold change on the x-axis and the log transformed padj on the y-axis. This allows for the visualisation of both the magnitude and significance of the changes in gene expression between two conditions. Genes that are differentially expressed (i.e., have a large log2 fold change) and are statistically significant (i.e., have a low padj) will appear in the left (downregulated genes) or in the right (upregulated genes) corners of the plot making easier their identification. ```r #### Volcano plot #### @@ -433,12 +498,12 @@ res_tb <- as_tibble(res) %>% res_tb$gene <- rownames(res) -# Relocate the 'gene' column to be the first column +# Relocate the gene column to the first position res_tb <- res_tb %>% relocate(gene, .before = baseMean) -# Order the table by adjusted p-value (padj) and add a new column for gene labels +# Order the table by padj and add a new column for gene labels res_tb <- res_tb %>% arrange(padj) %>% mutate(genelabels = "") @@ -449,7 +514,7 @@ res_tb$genelabels[1:5] <- res_tb$gene[1:5] # Create a volcano plot using ggplot2 -ggplot(data = res_tb, aes(x = log2FoldChange, y = -log10(padj), col = diffexpressed)) + +volcano_plot <- ggplot(data = res_tb, aes(x = log2FoldChange, y = -log10(padj), col = diffexpressed)) + geom_point(size = 0.6) + geom_text_repel(aes(label = genelabels), size = 2.5, max.overlaps = Inf) + ggtitle("DE genes treatment versus control") + @@ -463,20 +528,28 @@ ggplot(data = res_tb, aes(x = log2FoldChange, y = -log10(padj), col = diffexpres labs(color = 'DE genes') + xlim(-3,5) +# Save the plot + +ggsave("de_results/volcano_plot.png", plot = volcano_plot, width = 6, height = 5, dpi = 300) ``` + ## Functional analysis -The output of the differential expression analysis is a list of significant DE genes. To uncover the underlying biological mechanisms, various downstream analyses can be performed, such as functional enrichment analysis (identify overrepresented biological processes, molecular functions, cellular components or pathways) and network analysis (group genes based on similar expression patterns to identify novel interactions). To facilitate the interpretation of the resulting list of DE genes, a range of freely available web- and R-based tools can be employed. +The output of the differential expression analysis is a list of significant DE genes. To uncover the underlying biological mechanisms, various downstream analyses can be performed, such as functional enrichment analysis (identify overrepresented biological processes, molecular functions, cellular components or pathways), and network analysis (group genes based on similar expression patterns to identify novel interactions). To facilitate the interpretation of the resulting list of DE genes, a range of freely available web- and R-based tools can be employed. -In this tutorial, we will explore an enrichment analysis technique known as Over-Representation Analysis (ORA), a powerful tool for identifying biological pathways or processes that are significantly enriched with DE genes. The underlying statistic behind ORA is the **hypergeometric test**, which considers three key components: +In this tutorial, we will explore an enrichment analysis technique known as Over-Representation Analysis (ORA), a powerful tool for identifying biological pathways or processes significantly enriched within the list of DE genes. The underlying statistic behind ORA is the **hypergeometric test**, which considers three key components: - **Universe**: the background list of genes (for example the genes annotated in a genome); -- **Gene Set**: a subset of genes known to be involved in a specific biological pathway or process (for example genes from the Gene Ontology database); -- **Gene List**: the number of DE genes. + + +- **GeneSet**: a collection of genes annotated by a reference database (such as Gene Ontology), and known to be involved in a particular biological pathway or process; + + +- **Gene List**: the differentially expressed genes. The hypergeometric test calculates the probability of observing a certain number of genes from the gene set (pathway or process) within the gene list (DE genes) by chance. -An important aspect of this analysis is the concept of **membership**. It defines the relationship between DE genes and genes from the analysed gene set. By knowing which genes belong to which pathways/processes, we can determine whether the observed overlap between DE genes and a particular pathway/process is greater than what would be expected by random chance. +An important aspect of this analysis is the concept of **membership**. It defines the relationship between DE genes and genes from the analysed gene set. By knowing which genes belong to which pathway/process, we can determine whether the observed overlap between DE genes and the particular pathway/process is greater than what would be expected by random chance. ```r #### Enrichment analysis (ORA) #### @@ -545,5 +618,9 @@ dotplot <- dotplot( # Combine the bar plot and dot plot into a single plot grid -plot_grid(barplot, dotplot, nrow = 2) +go_plot <- plot_grid(barplot, dotplot, col = 2) + +# Save the plot + +ggsave("de_results/go_plot.png", plot = go_plot, width = 13, height = 6, dpi = 300) ``` From 08efda662793dd6a89837295f7075a6e5959f68a Mon Sep 17 00:00:00 2001 From: LorenzoS96 <47034913+LorenzoS96@users.noreply.github.com> Date: Thu, 7 Nov 2024 09:50:04 +0100 Subject: [PATCH 023/107] correct formula in theory --- docs/usage/DEanalysis/theory.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/docs/usage/DEanalysis/theory.md b/docs/usage/DEanalysis/theory.md index d9d095aee..0d663fe61 100644 --- a/docs/usage/DEanalysis/theory.md +++ b/docs/usage/DEanalysis/theory.md @@ -149,7 +149,9 @@ While normalised counts are useful for downstream visualisation of results, they DESeq2 addresses this issue by employing the **negative binomial distribution**, which generalises the Poisson distribution by introducing an additional dispersion parameter. This parameter quantifies the extra variability present in RNA-seq data, providing a more realistic representation than the Poisson distribution, which assumes mean = variance. DESeq2 starts by estimating the **common dispersion**, a single estimate of dispersion applicable to all genes in the dataset. This estimate provides a baseline for variability across all genes in the dataset. Next, DESeq2 estimates **gene-wise dispersion**, a separate estimate of dispersion for each individual gene, taking into account that different genes may exhibit varying levels of expression variability due to biological differences. The dispersion parameter (α) is related to the mean (μ), and variance of the data, as described by the equation: -`Var = μ + α ⋅ μ²` +$$ +Var = μ + α ⋅ μ² +$$ A key feature of DESeq2's dispersion estimates is their negative correlation with the mean, and positive correlation with the variance. Genes with low expression tend to have higher dispersion values, while genes with high expression tend to have lower dispersion. From 6be4d8294da01d1327a35c43f815e000c56d4070 Mon Sep 17 00:00:00 2001 From: LorenzoS96 <47034913+LorenzoS96@users.noreply.github.com> Date: Thu, 7 Nov 2024 09:52:29 +0100 Subject: [PATCH 024/107] prettier check --- docs/usage/DEanalysis/de_rstudio.md | 25 ++++------------------ docs/usage/DEanalysis/index.md | 6 ------ docs/usage/DEanalysis/interpretation.md | 8 +------ docs/usage/DEanalysis/rnaseq.md | 15 ------------- docs/usage/DEanalysis/theory.md | 28 +++++++------------------ 5 files changed, 12 insertions(+), 70 deletions(-) diff --git a/docs/usage/DEanalysis/de_rstudio.md b/docs/usage/DEanalysis/de_rstudio.md index 761e63dc0..f3adcc30b 100644 --- a/docs/usage/DEanalysis/de_rstudio.md +++ b/docs/usage/DEanalysis/de_rstudio.md @@ -2,12 +2,10 @@ order: 4 --- - # Differential Analysis with DESeq2 In this section of the tutorial, we will guide you through the practical steps necessary to set up the RStudio environment, load the required libraries and data, and execute the DESeq2 analysis. By the end of this section, you will have a fully functional DESeq2 analysis pipeline set up in RStudio, ready to uncover the differentially expressed genes in your dataset. - ## Launching the RStudio environment Once the nf-core/rnaseq pipeline is terminated, the resulting data are stored in the folder `results_star_salmon`. Now, we can analyse the results by running DESeq2 on RStudio. First of all, we need to launch it: @@ -27,20 +25,19 @@ Password: pass Using `sleep` will keep the Gitpod session active, preventing disconnection and providing enough time to complete our analysis without interruptions ::: - ## Differential Expression Analysis As in all analysis, firstly we need to create a new project: -1) Go to the **File** menu and select **New Project**; +1. Go to the **File** menu and select **New Project**; -2) Select **New Directory**, **New Project**, name the project as shown below and click on **Create Project**; +2. Select **New Directory**, **New Project**, name the project as shown below and click on **Create Project**;
    ![r_project](./img/project_R.png){ width="400" }
    -3) The new project will be automatically opened in RStudio. +3. The new project will be automatically opened in RStudio. We can check whether we are in the correct working directory with `getwd()`. The path `/workspace/gitpod/training/DE_analysis/` should appear on your console. To store our results in an organized way, we will create a folder named **de_results** using the **New Folder** button in the bottom right panel. We will save all our resulting tables and plots in this folder. Next, go to the **File menu**, select **New File** and then **R Script** to create a script editor in which we will save all commands required for the analysis. In the editor type: @@ -97,10 +94,8 @@ In DESEq2, the `dds` object is a central data structure that contains the follow - `countData`: a matrix of raw count data, where each row represents a gene and each column represents a sample; - - `colData`: a data frame containing information about the samples, such as the experimental design, treatment and other relevant metadata; - - `design`: a formula specifying the experimental design utilised to estimate the dispersion and the log2 fold change. All these components can be checked with specific commands: @@ -201,10 +196,8 @@ The `DESeq()` function is a high-level wrapper that simplifies the process of di - `estimateSizeFactors`: to normalise the count data; - - `estimateDispersion`: to estimate the dispersion; - - `nbinomWaldTest`: to perform differential expression test. The individual functions can be carried out also singularly as shown below: @@ -236,7 +229,6 @@ The `rlog` and the `vst` transformations have an argument, **blind** that can be - **TRUE** (default): useful for QC analysis because it re-estimates the dispersion, allowing for comparison of samples in an unbiased manner with respect to experimental conditions; - - **FALSE**: the function utilizes the already estimated dispersion, generally applied when differences in counts are expected to be due to the experimental design. Next, we perform Principal Component Analysis (PCA) to explore the data. DESeq2 provides a built-in function, `plotPCA()`, which uses [ggplot2](https://ggplot2.tidyverse.org) for visualisation, taking the `rld` (or the `vst`) object as input. @@ -325,19 +317,14 @@ The `results()` function in DESeq2 is used to extract the results of the DE anal - **baseMean**: the average expression level of the gene across all samples; - - **log2FoldChange**: the log2 fold change of the gene between the condition of interest and the reference level; - - **lfcSE**: the standard error of the log2 fold change; - - **stat**: the Wald statistic, which is used to calculate the p-value; - - **pvalue**: the p-value from the Wald test indicates the probability of observing the measured difference in gene expression (log2 fold change) by chance, assuming no true difference exists (null hypothesis). A low p-value suggests that the observed expression change between samples is unlikely due to random chance, so we can reject the null hypothesis --> the gene is differentially expressed; - - **padj**: the adjusted p-value, which takes into account multiple testing corrections, (Benjamini-Hochberg method default) to control the false discovery rate; The `results()` function returns the results for all genes in the analysis with an adjusted p-value below a specific FDR cutoff, set by default to 0.1. This threshold can be modified with the parameter `alpha`. The `results()` function can also be customised to filter the results based on certain criteria (log2 fold change or padj) or to set a specific contrast (specific comparison between two or more levels). @@ -386,7 +373,7 @@ res_viz <- as_tibble(res_viz) %>% write.csv(res_viz, file = "de_results/de_result_table.csv") ``` -In the *Experimental Design* section, we emphasised the importance of estimating the log2 fold change threshold using a statistical power calculation, rather than selecting it arbitrarily. This approach ensures that the chosen threshold is statistically appropriate and tailored to the specifics of the experiment. However, since we are working with simulated data for demonstration purposes, we will use a padj threshold of 0.05 and consider genes with a log2 fold change of at least 1 or -1 as differentially expressed. +In the _Experimental Design_ section, we emphasised the importance of estimating the log2 fold change threshold using a statistical power calculation, rather than selecting it arbitrarily. This approach ensures that the chosen threshold is statistically appropriate and tailored to the specifics of the experiment. However, since we are working with simulated data for demonstration purposes, we will use a padj threshold of 0.05 and consider genes with a log2 fold change of at least 1 or -1 as differentially expressed. ```r #### Extract significant DE genes from the results #### @@ -413,7 +400,6 @@ resSig write.csv(resSig, file = "de_results/sig_de_genes.csv") ``` - ## Plot the results Now that we have obtained the results of the differential expression analysis, it's time to visualise the data to gain a deeper understanding of the biological processes that are affected by the experimental conditions. Visualisation is a crucial step in RNA-seq analysis, as it allows us to identify patterns and trends in the data that may not be immediately apparent from the numerical results. @@ -533,7 +519,6 @@ volcano_plot <- ggplot(data = res_tb, aes(x = log2FoldChange, y = -log10(padj), ggsave("de_results/volcano_plot.png", plot = volcano_plot, width = 6, height = 5, dpi = 300) ``` - ## Functional analysis The output of the differential expression analysis is a list of significant DE genes. To uncover the underlying biological mechanisms, various downstream analyses can be performed, such as functional enrichment analysis (identify overrepresented biological processes, molecular functions, cellular components or pathways), and network analysis (group genes based on similar expression patterns to identify novel interactions). To facilitate the interpretation of the resulting list of DE genes, a range of freely available web- and R-based tools can be employed. @@ -542,10 +527,8 @@ In this tutorial, we will explore an enrichment analysis technique known as Over - **Universe**: the background list of genes (for example the genes annotated in a genome); - - **GeneSet**: a collection of genes annotated by a reference database (such as Gene Ontology), and known to be involved in a particular biological pathway or process; - - **Gene List**: the differentially expressed genes. The hypergeometric test calculates the probability of observing a certain number of genes from the gene set (pathway or process) within the gene list (DE genes) by chance. diff --git a/docs/usage/DEanalysis/index.md b/docs/usage/DEanalysis/index.md index 68e35bb99..56d9d5e24 100644 --- a/docs/usage/DEanalysis/index.md +++ b/docs/usage/DEanalysis/index.md @@ -2,7 +2,6 @@ order: 1 --- - # Welcome These pages are a tutorial workshop for the [Nextflow](https://www.nextflow.io) pipeline [nf-core/rnaseq](https://nf-co.re/rnaseq). @@ -20,7 +19,6 @@ By the end of this workshop, you will be able to: Let's get started! - ## Running with Gitpod In order to run this using GitPod, please make sure: @@ -30,17 +28,13 @@ In order to run this using GitPod, please make sure: Now you're all set and can use the following button to launch the service: - [![Open in GitPod](https://img.shields.io/badge/Gitpod-%20Open%20in%20Gitpod-908a85?logo=gitpod)](https://gitpod.io/#https://github.com/lescai-teaching/rnaseq-tutorial) - - ## Additional documentation - You can find detailed documentation on **Nextflow** [here](https://www.nextflow.io/docs/latest/) - You can find additional training on [these pages](https://training.nextflow.io) - ## Credits & Copyright This training material has been written and completed by [Lorenzo Sola](https://github.com/LorenzoS96), [Francesco Lescai](https://github.com/lescai), and [Mariangela Santorsola](https://github.com/msantorsola) during the [nf-core](https://nf-co.re) Hackathon in Barcellona, 2024. Thank you to [Victoria Cepeda](https://github.com/vcepeda) for her contributions to the tutorial's revision. The tutorial is aimed at anyone who is interested in using nf-core pipelines for their studies or research activities. diff --git a/docs/usage/DEanalysis/interpretation.md b/docs/usage/DEanalysis/interpretation.md index 20a8a7279..476f627cd 100644 --- a/docs/usage/DEanalysis/interpretation.md +++ b/docs/usage/DEanalysis/interpretation.md @@ -2,7 +2,6 @@ order: 5 --- - # Interpretation Once DE genes have been identified, the next crucial step is to interpret the results. This involves the inspection of tables and plots generated during the analysis to understand the biological significance of the data. In this part of the tutorial, we will explore the results by discussing the significant DE genes and we will examine various plots generated during the analysis. @@ -11,7 +10,6 @@ Once DE genes have been identified, the next crucial step is to interpret the re The results illustrated in this section might show slight variations compared to your runs due to randomness in the STAR algorithm. This randomness arises from using variable seed values and parallel processing, leading to minor differences in results between runs on the same data. These small discrepancies are not biologically significant and may affect counts and subsequent plots (such as PCA and count plots). However, the overall patterns and main findings should remain consistent. While exact reproducibility is ideal, minor variations are acceptable in practice, as long as they do not impact the main conclusions of the study. ::: - ## Quality control plots The first plot we will examine is the Principal Component Analysis (PCA) plot. Since we're working with simulated data, our metadata is relatively simple, consisting of just three variables: `sample`, `condition`, and `replica`. In a typical RNA-seq experiment, however, metadata can be complex and encompass a wide range of variables that could contribute to sample variation, such as sex, age, and developmental stage. @@ -32,7 +30,6 @@ Remember that to create this plot, we utilized the `dist()` function, so in the Overall, the integration of these plots suggests that we are working with high-quality data and we can confidently proceed to the differential expression analysis. - ## Differential expression results In this part of the tutorial, we will examine plots that are generated after the differential expression analysis. These plots are not quality control plots, but rather plots that help us to interpret the results. @@ -65,12 +62,11 @@ The treatment induced differential expression in five genes: one downregulated a After the identification of DE genes, it's informative to visualise the expression of specific genes of interest. The `plotCounts()` function applied directly on the `dds` object allows us to examine individual gene expression profiles without accessing the full `res` object. -
    ![counts](./img/plotCounts.png){ width="400" }
    -In our example, post-treatment, we observe a significant increase in the expression of the *ENSG00000142192* gene, highlighting its responsiveness to the experimental conditions. +In our example, post-treatment, we observe a significant increase in the expression of the _ENSG00000142192_ gene, highlighting its responsiveness to the experimental conditions. Finally, we can create a heatmap using the normalised expression counts of DE genes. The resulting heatmap visualises how the expression of significant genes varies across samples. Each row represents a gene, and each column represents a sample. The color intensity in the heatmap reflects the normalised expression levels: red colors indicate higher expression, while blue colors indicate lower expression. @@ -80,7 +76,6 @@ Finally, we can create a heatmap using the normalised expression counts of DE ge By examining the heatmap, we can visually identify the expression patterns of our five significant differentially expressed genes. This visualisation allows us to identify how these genes respond to the treatment. The heatmap provides a clear and intuitive way to explore gene expression dynamics. - ## Over Representation Analysis (ORA) Finally, we can attempt to assign biological significance to our differentially expressed genes through **Over Representation Analysis (ORA)**. The ORA analysis identifies specific biological pathways, molecular functions and cellular processes, according to the **Gene Ontology (GO)** database, that are enriched within our differentially expressed genes. @@ -91,7 +86,6 @@ Finally, we can attempt to assign biological significance to our differentially The enrichment analysis reveals a possible involvement of cellular structures and processes, including "clathrin-coated pit", "dendritic spine", "neuron spine" and "endoplasmic reticulum lumen". These terms suggest a focus on cellular transport, structural integrity and protein processing, especially in neural contexts. This pattern points to pathways related to cellular organization and maintenance, possibly playing an important role in the biological condition under study. - ## Conclusions In this tutorial, we have walked through the steps of the RNA-seq analysis, from launching the nfcore/rnaseq pipeline to interpreting differential expression results. You learned how the data are generated, identified differentially expressed genes, and conducted enrichment analysis. By following this tutorial, you should now be able to use the nfcore/rnaseq pipeline and perform differential expression analysis with DESeq2, interpreting the results within the context of your research. diff --git a/docs/usage/DEanalysis/rnaseq.md b/docs/usage/DEanalysis/rnaseq.md index b6a1fa5b8..9fd0e123e 100644 --- a/docs/usage/DEanalysis/rnaseq.md +++ b/docs/usage/DEanalysis/rnaseq.md @@ -2,12 +2,10 @@ order: 3 --- - # The nf-core/rnaseq pipeline In order to carry out a RNA-Seq analysis we will use the nf-core pipeline [rnaseq](https://nf-co.re/rnaseq/3.14.0). - ## Overview The pipeline is organised following the diffent blocks shown below: pre-processing, traditional alignment (or lightweight alignment) and quantification, post-processing and final QC. @@ -20,10 +18,8 @@ In each process, the users can choose among a range of different options. Import - traditional alignment and quantification (stage 2); - - lightweight alignment and quantification (stage 3). - ## Experimental Design The number of reads and the number of biological replicates are two critical factors that researchers need to carefully consider during the design of a RNA-seq experiment. While it may seem intuitive that having a large number of reads is always desirable, an excessive number can lead to unnecessary costs and computational burdens, without providing significant improvements. Instead, it is often more beneficial to prioritise the number of biological replicates, as it allows to capture the natural biological variation of the data. Biological replicates involve collecting and sequencing RNA from distinct biological samples (e.g., different individuals, tissues, or time points), helping to detect genuine changes in gene expression. @@ -32,15 +28,12 @@ The number of reads and the number of biological replicates are two critical fac This concept must not be confused with technical replicates that asses the technical variability of the sequencing platform by sequencing the same RNA sample multiple time. ::: - To obtain optimal results, it is crucial to balance the number of biological replicates and the sequencing depth. While increasing the depth of sequencing enhances the ability to detect genes with low expression levels, there is a plateau beyond which no further benefits are gained. Statistical power calculations can inform experimental design by estimating the optimal number of reads and replicates required. For instance, this approach helps to establish a suitable log2 fold change threshold for the DE analysis. By incorporating multiple biological replicates into the design and optimizing sequencing depth, researchers can enhance the statistical power of the analysis, reducing the number of false positive results, and increasing the reliability of the findings. - ## Library design RNA-seq library design involves important decisions, particularly the choice between paired-end and single-end sequencing. Paired-end sequencing offers insights into structural variations and transcript isoforms, significantly improving mapping accuracy for longer transcripts and repetitive regions. In contrast, single-end sequencing—where only one end of the fragment is sequenced—can be a more cost-effective option while still delivering high-quality data for gene expression analysis. The choice between these two methods ultimately depends on the research question and experimental objectives. Paired-end sequencing is ideal for identifying novel transcripts or characterizing isoforms, whereas single-end sequencing is often sufficient for quantifying gene expression. Factors such as the type of RNA (e.g., mRNA or total RNA), read length, budget, and available computational resources also influence this decision. - ## Reference genome nf-core pipelines make use of the Illumina iGenomes collection as [reference genomes](https://nf-co.re/docs/usage/reference_genomes). @@ -53,7 +46,6 @@ In this tutorial we will edit the config file, since the data we will be using h The two files are `/workspace/gitpod/training/data/refs/Homo_sapiens_assembly38_chr21.fa` and `/workspace/gitpod/training/data/refs/Homo_sapiens_assembly38_chr21.fa.fai`, respectively. - ## Reference annoation The reference annotation plays a crucial role in the RNA-seq analysis. Without a high-quality reference annotation, RNA-seq analysis would result in inaccurate or incomplete results. The reference annotation provides a precise guide for aligning sequencing reads to specific genomic regions, allowing to identify genes, transcripts, and regulatory elements, as well as novel transcripts and alternative splicing events. @@ -65,7 +57,6 @@ Similarly to the approach utilised for the genome, in this tutorial we will edit The two files are `/workspace/gitpod/training/data/refs/gencode_v29_chr21.gff` and `/workspace/gitpod/training/data/refs/gencode_v29_transcripts_chr21.fa`, respectively. - ## Input files The input data should be provided in a CSV file, according to a format that is largely common for nf-core pipelines. @@ -73,23 +64,18 @@ The format is described in the [rnaseq usage page](https://nf-co.re/rnaseq/3.14. The input file is `/workspace/gitpod/training/data/reads/rnaseq_samplesheet.csv`. - ## Running nf-core/rnaseq In the following sections we will: - prepare our references; - - set our computational resources in order to be able to run the pipeline on a gitpod VM; - - edit the optional settings; - - run the pipeline. - ## Reference and annotation files Following the considerations above, we will first of all edit the `nextflow.config` file in our working directory to add a new genome. @@ -111,7 +97,6 @@ genomes { To speed up the analysis we will include the `star_index` and the `salmon_index` in the config. These files have already been created locally. - ## Computing resources Based on the choices we made when starting up the gitpod environment, we recommend to use the following additional parameters. diff --git a/docs/usage/DEanalysis/theory.md b/docs/usage/DEanalysis/theory.md index 0d663fe61..dd932fbb6 100644 --- a/docs/usage/DEanalysis/theory.md +++ b/docs/usage/DEanalysis/theory.md @@ -2,12 +2,10 @@ order: 2 --- - # RNAseq Analysis Before we dive into the nf-core pipeline for analysing RNA-sequencing data, it's worth looking at some theoretical aspects of RNA-seq. - ## Overview Given the central role of RNA in a wide range of molecular functions, RNA-seq has emerged as a powerful tool for measuring the presence and levels of RNA species in biological samples. The technique is based on next-generation sequencing (NGS) technologies and is now considered the gold standard in the field of transcriptomics. @@ -22,20 +20,16 @@ In the scheme, we can identify three key phases in the workflow: - **data pre-processing**: raw reads are handled to remove adapters or contaminants enhancing their quality; - - **traditional or lightweight alignment and quantification**: reads are mapped to a reference genome, and gene abundance is estimated. The workflow can also follow an alternative route based on lightweight alignment and quantification, reducing the time required for the analysis. - - **differential expression analysis**: differentially expressed genes are identified using statistical tests, annotated, and visualised. Depending on the user's needs, the workflow can include additional downstream analyses such as functional enrichment analysis, co-expression analysis, and integration with other omics data. - ## Pre-processing The pre-processing of sequencing reads from RNA-seq data is a critical step to ensure the quality and accuracy of downstream analysis. The raw reads obtained from the sequencer are stored as [FASTQ](https://en.wikipedia.org/wiki/FASTQ_format) files, which contain both the sequence data and quality scores. The initial processing step involves evaluating the quality of the reads to identify potential issues such as adapter contamination, sequencing errors, or low-quality bases. The presence of adapters (short DNA sequences ligated to the ends of DNA fragments during library preparation) is firstly detected through comparison with known adapter sequences or by using algorithms that identify adapter-like sequences. These sequences are then removed in a process known as **read trimming**. Next, reads containing contaminants (genomic DNA and/or rRNA), and those with low-quality bases are filtered out. Finally, the quality of the filtered reads is checked to ensure their suitability for downstream processing. - ## Alignment (or lightweight alignment) and quantification In the RNA-seq workflow, the alignment step involves mapping sequencing reads to a reference genome or transcriptome to determine the position and orientation of each read relative to the reference sequence. @@ -46,7 +40,6 @@ The alignment and quantification steps can follow two different approaches depen - traditional alignment and quantification; - - lightweight alignment and quantification. In RNA-seq analysis, traditional alignment is often performed with **splice-aware aligners**, which are designed to recognise the splicing process. These aligners can align reads across known splice junctions and detect novel splice sites or alternative splicing events. Popular splice-aware aligners include [STAR](https://github.com/alexdobin/STAR), and [HISAT2](https://github.com/DaehwanKimLab/hisat2). Once alignment is complete, the following step is quantification, which involves estimating the abundance (number of reads) associated with each gene. Tools like [featureCounts](https://subread.sourceforge.net/featureCounts.html), [HTSeq](https://htseq.readthedocs.io), [Salmon](http://combine-lab.github.io/salmon), and [RSEM](http://deweylab.github.io/RSEM) are commonly used for this purpose. @@ -57,14 +50,12 @@ Traditional alignment tools are time-consuming and require substantial computati Salmon can be run in two different modes: alignment-based mode or quasi-mapping mode. In the first mode, Salmon estimates abudance starting from `bam` file obtained with the chosen aligner. In the second mode, Salmon requires a reference transcriptome and raw reads to perform both mapping and quantification. ::: - ## Differential expression (DE) analysis with DESeq2 Differential expression analysis is a statistical method for comparing gene expression levels between different experimental conditions, such as disease vs healthy (e.g., tumour tissue vs healthy tissue), treatment vs control (e.g., a sample treated with a specific stimulus, drug, or compound vs an untreated sample), and tissue vs tissue (e.g., brain vs heart). Differential expression analysis aims to assess, for each gene, whether the observed differences in expression between groups are statistically significant, accounting for the variation observed within groups (replicates). This part of the analysis is typically performed in R using various packages, such as [DESeq2](https://bioconductor.org/packages/release/bioc/html/DESeq2.html), [edgeR](https://bioconductor.org/packages/release/bioc/html/edgeR.html), and [limma](https://bioconductor.org/packages/release/bioc/html/limma.html). This tutorial focuses on **DESeq2**, a popular R package known for its robustness. For more detailed information, see the [DESeq2 vignette](https://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html). The analysis begins with the input data, which generally consist of a count matrix obtained during the alignment and quantification step, summarising the expression levels of the different genes in each sample of the dataset. The rows of the matrix typically correspond to genes, while the columns represent the samples. Another essential prerequisite is a metadata table describing the samples. - ### Quality Control To ensure robust differential expression results, it is common to start by exploring the sources of variation in the data. DESeq2 provides quality control tools, including Principal Component Analysis (PCA) and hierarchical clustering. PCA is used to reduce the dimensionality of the data, allowing the visualisation of the samples in a lower-dimensional space. In contrast, hierarchical clustering shows the correlation of gene expression for all pairwise combinations of samples in the dataset. These methods can identify groups of samples with similar behaviour, as well as potential outliers or unexpected patterns. @@ -77,7 +68,6 @@ Finally, it is often beneficial to filter out genes that are unlikely to show di The `vst` or `rlog` transformations are applied to the normalised counts stored in the `dds` object, which is generated by running either the `DESeq()` or `estimateSizeFactors()` function. Since the estimation of size factors is an early step in the `DESeq()` function, the transformations are generally applied immediately afterwards. ::: - ### Design Formula The design formula specifies the sources of variation that the statistical model needs to account for. It defines how the counts will be related to the experimental conditions, allowing the software to model the relationship between gene expression and the factors of interest, such as treatment, time points or batch effects. It is important to specify the main factor of interest as well as additional covariates in the design formula. @@ -100,7 +90,6 @@ In R, the tilde (`~`) is used in formula syntax to specify relationships between The results will not be affected by the order of variables but the common practice is to specify the main source of variation in the last position of the design formula. - ### Differential Expression Analysis RNA-seq data typically contain a large number of genes with low expression counts, indicating that many genes are expressed at very low levels across samples. At the same time, RNA-seq data exhibit a skewed distribution with a long right tail due to the absence of an upper limit for gene expression levels. This means that while most genes have low to moderate expression levels, a small number are expressed at high levels. Accurate statistical modelling must therefore account for this distribution to avoid misleading conclusions. @@ -121,26 +110,23 @@ While `DESeq()` combines these steps, a user could choose to perform each functi The different steps are explained in detail below: -1) **Normalisation**: since DESeq2 compares counts between sample groups for the same gene, it does not need to adjust for gene length. However, it is essential to account for variations in sequencing depth and RNA composition among samples. To normalise the data, DESeq2 utilises **size factors**. +1. **Normalisation**: since DESeq2 compares counts between sample groups for the same gene, it does not need to adjust for gene length. However, it is essential to account for variations in sequencing depth and RNA composition among samples. To normalise the data, DESeq2 utilises **size factors**. The size factors are calculated using the **median ratio** method: - **Calculate the geometric mean**: for each gene, compute the geometric mean of its counts across all samples. This gives a row-wise geometric mean for each gene; - - **Calculate ratios**: divide each gene's count by its geometric mean to obtain a ratio for each sample; - - **Determine size factors**: for each sample, take the median of these ratios (column-wise) to derive the size factors; - - **Normalise counts**: divide each raw count by the corresponding sample size factor to generate normalised counts. :::warning While normalised counts are useful for downstream visualisation of results, they should not be used as input for DESeq2. Instead, DESeq2 requires raw count data in the form of a matrix of integer values. ::: -2) **Estimate dispersion and gene-wise dispersion**: the dispersion is a measure of how much the variance deviates from the mean. The dispersion estimates indicate the variance in gene expression at a specific mean expression level. Importantly, RNA-seq data are characterised by **overdispersion**, where the variance in gene expression levels often exceeds the mean (variance > mean). +2. **Estimate dispersion and gene-wise dispersion**: the dispersion is a measure of how much the variance deviates from the mean. The dispersion estimates indicate the variance in gene expression at a specific mean expression level. Importantly, RNA-seq data are characterised by **overdispersion**, where the variance in gene expression levels often exceeds the mean (variance > mean).
    ![overdispersion](./img/overdispersion.png){ width="400"} @@ -155,9 +141,9 @@ $$ A key feature of DESeq2's dispersion estimates is their negative correlation with the mean, and positive correlation with the variance. Genes with low expression tend to have higher dispersion values, while genes with high expression tend to have lower dispersion. -3) **Mean-dispersion relationship**: this process, known as dispersion fitting, models the relationship between the mean expression level of a gene, and its dispersion. DESeq2 assumes that genes with similar expression profiles share similar dispersion patterns, and leverages this information to refine the estimates identifying a trend in the dispersion estimates across genes. The fitted curve, typically a smooth curve, describes how dispersion changes as a function of the mean expression level. +3. **Mean-dispersion relationship**: this process, known as dispersion fitting, models the relationship between the mean expression level of a gene, and its dispersion. DESeq2 assumes that genes with similar expression profiles share similar dispersion patterns, and leverages this information to refine the estimates identifying a trend in the dispersion estimates across genes. The fitted curve, typically a smooth curve, describes how dispersion changes as a function of the mean expression level. -4) **Final dispersion estimates**: DESeq2 refines the gene-wise dispersion by shrinking it towards the fitted curve. The "shrinkage" helps control for overfitting, and makes the dispersion estimates more reliable. The strength of the shrinkage depends on the sample size (more samples = less shrinkage), and how close the initial estimates are to the fitted curve. +4. **Final dispersion estimates**: DESeq2 refines the gene-wise dispersion by shrinking it towards the fitted curve. The "shrinkage" helps control for overfitting, and makes the dispersion estimates more reliable. The strength of the shrinkage depends on the sample size (more samples = less shrinkage), and how close the initial estimates are to the fitted curve.
    ![dispersion](./img/dispersion_estimates.png){ width="400"} @@ -165,9 +151,9 @@ A key feature of DESeq2's dispersion estimates is their negative correlation wit The initial estimates (black dots) are shrunk toward the fitted curve (red line) to obtain the final estimates (blue dots). However, genes with exceptionally high dispersion values are not shrunk, as they likely deviate from the model assumptions exhibiting elevated variability due to biological or technical factors. Shrinking these values could lead to false positives. -5) **Fitting model and testing**: the initial step in hypothesis testing involves defining a **null hypothesis** for each gene. In DESeq2, The null hypothesis states that there is no difference in expression between the sample groups (log2 fold change == 0). Next, DESeq applies a statistical test to assess whether the null hypothesis is true. -DESeq2 fits a **generalised linear model (GLM)** to the normalised counts using the calculated size factors and final dispersion estimates. A GLM is a flexible extension of linear regression that models the relationship between a response variable (normalised counts), and predictors (e.g., condition). -By using a **negative binomial distribution**, DESeq2's GLM can handle the additional variability in gene expression counts that often occurs in RNA-seq data. Once the model is fit, coefficients are estimated for each sample group along with their standard errors. These coefficients represent the estimated log2 fold changes between groups, and serve as the basis for hypothesis testing, using either a **Wald test** or a **Likelihood Ratio Test (LRT)**, depending on the experimental design: +5. **Fitting model and testing**: the initial step in hypothesis testing involves defining a **null hypothesis** for each gene. In DESeq2, The null hypothesis states that there is no difference in expression between the sample groups (log2 fold change == 0). Next, DESeq applies a statistical test to assess whether the null hypothesis is true. + DESeq2 fits a **generalised linear model (GLM)** to the normalised counts using the calculated size factors and final dispersion estimates. A GLM is a flexible extension of linear regression that models the relationship between a response variable (normalised counts), and predictors (e.g., condition). + By using a **negative binomial distribution**, DESeq2's GLM can handle the additional variability in gene expression counts that often occurs in RNA-seq data. Once the model is fit, coefficients are estimated for each sample group along with their standard errors. These coefficients represent the estimated log2 fold changes between groups, and serve as the basis for hypothesis testing, using either a **Wald test** or a **Likelihood Ratio Test (LRT)**, depending on the experimental design: - **Wald Test**: The Wald test is ideal for simpler experimental designs, such as comparing two conditions (e.g., treated vs untreated). In DESeq2, the Wald test is implemented by calculating the log2 fold change and dividing it by its standard error to obtain a z-statistic. This z-statistic is then evaluated against a standard normal distribution, leading to the computation of a p-value that indicates the likelihood of observing a z-statistic as extreme as the calculated value under the null hypothesis. When the p-value is small, we reject the null hypothesis, concluding that the gene is differentially expressed. From b372821e8e2b57e684fe6b2454055df61e98f581 Mon Sep 17 00:00:00 2001 From: nf-core-bot Date: Tue, 12 Nov 2024 18:31:49 +0000 Subject: [PATCH 025/107] [automated] Fix code linting --- CHANGELOG.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 25d35d657..81a9e1b48 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -17,9 +17,9 @@ Special thanks to the following for their contributions to the release: ### Software dependencies -| Dependency | Old version | New version | -| -------------- | ----------- | ----------- | -| `UMICollapse` | | 1.1.0 | +| Dependency | Old version | New version | +| ------------- | ----------- | ----------- | +| `UMICollapse` | | 1.1.0 | > **NB:** Dependency has been **updated** if both old and new version information is present. > From 88d64118a04699b641a4c6dbf849a33ca45bf0fa Mon Sep 17 00:00:00 2001 From: Matthias Zepper Date: Fri, 29 Nov 2024 14:21:39 +0100 Subject: [PATCH 026/107] Update tests for UMICollapse module. --- modules.json | 2 +- .../nf-core/umicollapse/tests/main.nf.test | 226 ++++++------------ .../umicollapse/tests/main.nf.test.snap | 128 +++++----- .../nf-core/umicollapse/tests/nextflow.config | 5 +- .../umicollapse/tests/nextflow_PE.config | 10 - .../umicollapse/tests/nextflow_SE.config | 10 - 6 files changed, 136 insertions(+), 245 deletions(-) delete mode 100644 modules/nf-core/umicollapse/tests/nextflow_PE.config delete mode 100644 modules/nf-core/umicollapse/tests/nextflow_SE.config diff --git a/modules.json b/modules.json index 93b28f2ab..2aac9c739 100644 --- a/modules.json +++ b/modules.json @@ -269,7 +269,7 @@ }, "umicollapse": { "branch": "master", - "git_sha": "78991a2306e7631c006498a372e87f135a4212db", + "git_sha": "0b27602842d3d79fd0e8db79f4afa764967fc3d1", "installed_by": ["bam_dedup_stats_samtools_umicollapse"] }, "umitools/dedup": { diff --git a/modules/nf-core/umicollapse/tests/main.nf.test b/modules/nf-core/umicollapse/tests/main.nf.test index cc28359a6..db578775e 100644 --- a/modules/nf-core/umicollapse/tests/main.nf.test +++ b/modules/nf-core/umicollapse/tests/main.nf.test @@ -7,63 +7,18 @@ nextflow_process { tag "modules" tag "modules_nfcore" tag "umicollapse" - tag "umitools/extract" - tag "samtools/index" - tag "bwa/index" - tag "bwa/mem" test("umicollapse single end test") { - setup{ - run("UMITOOLS_EXTRACT"){ - script "../../umitools/extract/main.nf" - config "./nextflow_SE.config" - process{ - """ - input[0] = [ - [ id:'test', single_end:true ], // meta map - [ - file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_1.fastq.gz', checkIfExists: true), - file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_2.fastq.gz', checkIfExists: true) - ] - ] - """ - } - } - - run("BWA_INDEX"){ - script "../../bwa/index/main.nf" - process{ - """ - input[0] = [[ id:'sarscov2'],file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)] - """ - } - } - run("BWA_MEM"){ - script "../../bwa/mem/main.nf" - process{ - """ - input[0] = UMITOOLS_EXTRACT.out.reads - input[1] = BWA_INDEX.out.index - input[2] = [[ id:'sarscov2'],file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)] - input[3] = true - """ - } - } - run("SAMTOOLS_INDEX"){ - script "../../samtools/index/main.nf" - process{ - """ - input[0] = BWA_MEM.out.bam - """ - } - } - } when { - config "./nextflow_SE.config" + config "./nextflow.config" process { """ - input[0] = BWA_MEM.out.bam.join(SAMTOOLS_INDEX.out.bai, by: [0]) + input[0] = Channel.of([ + [ id:'test', single_end:true ], // meta map + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.single_end.umi.sorted.bam', checkIfExists: true), + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.single_end.umi.sorted.bam.bai', checkIfExists: true) + ]) input[1] = 'bam' """ } @@ -73,7 +28,7 @@ nextflow_process { assertAll( { assert process.success }, { assert snapshot( - process.out.bam, + bam(process.out.bam[0][1]).getSamLinesMD5(), process.out.versions).match() } ) } @@ -81,60 +36,16 @@ nextflow_process { } test("umicollapse paired tests") { - setup{ - run("UMITOOLS_EXTRACT"){ - script "../../umitools/extract/main.nf" - config "./nextflow_PE.config" - process{ - """ - input[0] = [ - [ id:'test', single_end:false ], // meta map - [ - file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_1.fastq.gz', checkIfExists: true), - file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_2.fastq.gz', checkIfExists: true) - ] - ] - """ - } - } - - run("BWA_INDEX"){ - script "../../bwa/index/main.nf" - process{ - """ - input[0] = [ - [ id:'sarscov2'], - file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true) - ] - """ - } - } - run("BWA_MEM"){ - script "../../bwa/mem/main.nf" - process{ - """ - input[0] = UMITOOLS_EXTRACT.out.reads - input[1] = BWA_INDEX.out.index - input[2] = [[ id:'sarscov2'],file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)] - input[3] = true - """ - } - } - run("SAMTOOLS_INDEX"){ - script "../../samtools/index/main.nf" - process{ - """ - input[0] = BWA_MEM.out.bam - """ - } - } - } when { - config "./nextflow_PE.config" + config "./nextflow.config" process { """ - input[0] = BWA_MEM.out.bam.join(SAMTOOLS_INDEX.out.bai, by: [0]) + input[0] = Channel.of([ + [ id:'test'], // meta map + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.umi.sorted.bam', checkIfExists: true), + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.umi.sorted.bam.bai', checkIfExists: true) + ]) input[1] = 'bam' """ } @@ -144,22 +55,22 @@ nextflow_process { assertAll( { assert process.success }, { assert snapshot( - process.out.bam, + bam(process.out.bam[0][1]).getSamLinesMD5(), process.out.versions).match() } ) } } - test("umicollapse fastq tests") { + test("umicollapse fastq test (single-end)") { when { - config "./nextflow_SE.config" + config "./nextflow.config" process { """ input[0] = [ [ id:'test', single_end:true ], // meta map - file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_1.fastq.gz', checkIfExists: true), + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test.umi_extract_single.fastq.gz', checkIfExists: true), [] ] input[1] = 'fastq' @@ -177,61 +88,76 @@ nextflow_process { } } - test("umicollapse stub tests") { - options "-stub-run" - setup{ - run("UMITOOLS_EXTRACT"){ - script "../../umitools/extract/main.nf" - config "./nextflow_PE.config" - process{ + test("umicollapse fastq test (paired-end)") { + + when { + config "./nextflow.config" + process { """ input[0] = [ - [ id:'test', single_end:false ], // meta map - [ - file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_1.fastq.gz', checkIfExists: true), - file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_2.fastq.gz', checkIfExists: true) - ] + [ id:'test' ], // meta map + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test.umi_extract_1.fastq.gz', checkIfExists: true), + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test.umi_extract_2.fastq.gz', checkIfExists: true) ] + input[1] = 'fastq' """ } } - run("BWA_INDEX"){ - script "../../bwa/index/main.nf" - process{ - """ - input[0] = [ - [ id:'sarscov2'], - file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true) - ] - """ - } - } - run("BWA_MEM"){ - script "../../bwa/mem/main.nf" - process{ - """ - input[0] = UMITOOLS_EXTRACT.out.reads - input[1] = BWA_INDEX.out.index - input[2] = [[ id:'sarscov2'],file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)] - input[3] = true - """ - } + then { + assertAll( + { assert process.success }, + { assert snapshot( + process.out.fastq, + process.out.versions).match() } + ) } - run("SAMTOOLS_INDEX"){ - script "../../samtools/index/main.nf" - process{ - """ - input[0] = BWA_MEM.out.bam - """ - } + } + + // Stub tests + + test("umicollapse single end test - stub") { + + options "-stub" + + when { + config "./nextflow.config" + process { + """ + input[0] = Channel.of([ + [ id:'test', single_end:true ], // meta map + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.single_end.umi.sorted.bam', checkIfExists: true), + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.single_end.umi.sorted.bam.bai', checkIfExists: true) + ]) + input[1] = 'bam' + """ } } + + then { + assertAll( + { assert process.success }, + { assert snapshot( + process.out.bam, + process.out.versions).match() } + ) + } + + } + + test("umicollapse paired tests - stub") { + + options "-stub" + when { - config "./nextflow_PE.config" + config "./nextflow.config" process { """ - input[0] = BWA_MEM.out.bam.join(SAMTOOLS_INDEX.out.bai, by: [0]) + input[0] = Channel.of([ + [ id:'test'], // meta map + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.umi.sorted.bam', checkIfExists: true), + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.umi.sorted.bam.bai', checkIfExists: true) + ]) input[1] = 'bam' """ } @@ -240,7 +166,9 @@ nextflow_process { then { assertAll( { assert process.success }, - { assert snapshot(process.out).match() } + { assert snapshot( + process.out.bam, + process.out.versions).match() } ) } diff --git a/modules/nf-core/umicollapse/tests/main.nf.test.snap b/modules/nf-core/umicollapse/tests/main.nf.test.snap index bf6d5f30c..e903c0ce0 100644 --- a/modules/nf-core/umicollapse/tests/main.nf.test.snap +++ b/modules/nf-core/umicollapse/tests/main.nf.test.snap @@ -1,13 +1,38 @@ { "umicollapse single end test": { + "content": [ + "9158ea6e7a0e54819e25cbac5fbc5cc0", + [ + "versions.yml:md5,03fdbcb1ba9bd40325ca42859d39deb1" + ] + ], + "meta": { + "nf-test": "0.8.4", + "nextflow": "24.10.1" + }, + "timestamp": "2024-11-25T17:31:45.024306" + }, + "umicollapse paired tests": { + "content": [ + "b7be15ac7aae194b04bdbb56f3534495", + [ + "versions.yml:md5,03fdbcb1ba9bd40325ca42859d39deb1" + ] + ], + "meta": { + "nf-test": "0.8.4", + "nextflow": "24.10.1" + }, + "timestamp": "2024-11-25T17:31:52.072799" + }, + "umicollapse fastq test (paired-end)": { "content": [ [ [ { - "id": "test", - "single_end": true + "id": "test" }, - "test.dedup.bam:md5,89e844724f73fae9e7100506d0be5775" + "test.dedup.fastq.gz:md5,721a84a2accac988d636e837c60e47bc" ] ], [ @@ -15,12 +40,12 @@ ] ], "meta": { - "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nf-test": "0.8.4", + "nextflow": "24.10.1" }, - "timestamp": "2024-10-22T10:43:04.890267074" + "timestamp": "2024-11-24T13:57:36.968147" }, - "umicollapse fastq tests": { + "umicollapse fastq test (single-end)": { "content": [ [ [ @@ -28,7 +53,7 @@ "id": "test", "single_end": true }, - "test.dedup.fastq.gz:md5,c9bac08c7fd8df3e0203e3eeafc73155" + "test.dedup.fastq.gz:md5,2e602ed23eb87f434e4f0a9e491c0310" ] ], [ @@ -36,89 +61,50 @@ ] ], "meta": { - "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nf-test": "0.8.4", + "nextflow": "24.10.1" }, - "timestamp": "2024-10-22T10:43:45.691571914" + "timestamp": "2024-11-24T13:57:28.328682" }, - "umicollapse stub tests": { + "umicollapse single end test - stub": { "content": [ - { - "0": [ - [ - { - "id": "test", - "single_end": false - }, - "test.dedup.dedup.bam:md5,d41d8cd98f00b204e9800998ecf8427e" - ] - ], - "1": [ - - ], - "2": [ - [ - { - "id": "test", - "single_end": false - }, - "test.dedup_UMICollapse.log:md5,d41d8cd98f00b204e9800998ecf8427e" - ] - ], - "3": [ - "versions.yml:md5,c1e0275d81b1c97a9344d216f9154996" - ], - "bam": [ - [ - { - "id": "test", - "single_end": false - }, - "test.dedup.dedup.bam:md5,d41d8cd98f00b204e9800998ecf8427e" - ] - ], - "fastq": [ - - ], - "log": [ - [ - { - "id": "test", - "single_end": false - }, - "test.dedup_UMICollapse.log:md5,d41d8cd98f00b204e9800998ecf8427e" - ] - ], - "versions": [ - "versions.yml:md5,c1e0275d81b1c97a9344d216f9154996" + [ + [ + { + "id": "test", + "single_end": true + }, + "test.dedup.dedup.bam:md5,d41d8cd98f00b204e9800998ecf8427e" ] - } + ], + [ + "versions.yml:md5,c1e0275d81b1c97a9344d216f9154996" + ] ], "meta": { "nf-test": "0.8.4", - "nextflow": "23.10.1" + "nextflow": "24.10.1" }, - "timestamp": "2024-01-30T10:46:12.482697713" + "timestamp": "2024-11-24T14:09:40.277318" }, - "umicollapse paired tests": { + "umicollapse paired tests - stub": { "content": [ [ [ { - "id": "test", - "single_end": false + "id": "test" }, - "test.dedup.bam:md5,3e2ae4701e3d2ca074ea878a314a3e4f" + "test.dedup.dedup.bam:md5,d41d8cd98f00b204e9800998ecf8427e" ] ], [ - "versions.yml:md5,03fdbcb1ba9bd40325ca42859d39deb1" + "versions.yml:md5,c1e0275d81b1c97a9344d216f9154996" ] ], "meta": { - "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nf-test": "0.8.4", + "nextflow": "24.10.1" }, - "timestamp": "2024-10-22T10:43:33.250587075" + "timestamp": "2024-11-24T14:09:44.224965" } } \ No newline at end of file diff --git a/modules/nf-core/umicollapse/tests/nextflow.config b/modules/nf-core/umicollapse/tests/nextflow.config index 844edbdc6..105d8e132 100644 --- a/modules/nf-core/umicollapse/tests/nextflow.config +++ b/modules/nf-core/umicollapse/tests/nextflow.config @@ -1,8 +1,5 @@ process { - withName: UMITOOLS_EXTRACT { - ext.args = '--bc-pattern="NNNN"' - } withName: UMICOLLAPSE { ext.prefix = { "${meta.id}.dedup" } } -} \ No newline at end of file +} diff --git a/modules/nf-core/umicollapse/tests/nextflow_PE.config b/modules/nf-core/umicollapse/tests/nextflow_PE.config deleted file mode 100644 index ae4c96320..000000000 --- a/modules/nf-core/umicollapse/tests/nextflow_PE.config +++ /dev/null @@ -1,10 +0,0 @@ -process { - - withName: UMITOOLS_EXTRACT { - ext.args = '--bc-pattern="NNNN" --bc-pattern2="NNNN"' - } - - withName: UMICOLLAPSE { - ext.prefix = { "${meta.id}.dedup" } - } -} diff --git a/modules/nf-core/umicollapse/tests/nextflow_SE.config b/modules/nf-core/umicollapse/tests/nextflow_SE.config deleted file mode 100644 index d4b944365..000000000 --- a/modules/nf-core/umicollapse/tests/nextflow_SE.config +++ /dev/null @@ -1,10 +0,0 @@ -process { - - withName: UMITOOLS_EXTRACT { - ext.args = '--bc-pattern="NNNN"' - } - - withName: UMICOLLAPSE { - ext.prefix = { "${meta.id}.dedup" } - } -} From 15116a36db42214800daf677a800e39f30a901a1 Mon Sep 17 00:00:00 2001 From: Matthias Zepper Date: Fri, 29 Nov 2024 15:09:29 +0100 Subject: [PATCH 027/107] Update umi-tools dedup tests at well. --- modules.json | 2 +- .../nf-core/umitools/dedup/tests/main.nf.test | 33 ++++++++------- .../umitools/dedup/tests/main.nf.test.snap | 40 ++++++------------- modules/nf-core/umitools/dedup/tests/tags.yml | 2 + 4 files changed, 34 insertions(+), 43 deletions(-) create mode 100644 modules/nf-core/umitools/dedup/tests/tags.yml diff --git a/modules.json b/modules.json index 2aac9c739..27b9ecc67 100644 --- a/modules.json +++ b/modules.json @@ -274,7 +274,7 @@ }, "umitools/dedup": { "branch": "master", - "git_sha": "666652151335353eef2fcd58880bcef5bc2928e1", + "git_sha": "0b27602842d3d79fd0e8db79f4afa764967fc3d1", "installed_by": ["bam_dedup_stats_samtools_umitools"] }, "umitools/extract": { diff --git a/modules/nf-core/umitools/dedup/tests/main.nf.test b/modules/nf-core/umitools/dedup/tests/main.nf.test index 883e2d9d7..f00a8cbed 100644 --- a/modules/nf-core/umitools/dedup/tests/main.nf.test +++ b/modules/nf-core/umitools/dedup/tests/main.nf.test @@ -4,6 +4,11 @@ nextflow_process { script "../main.nf" process "UMITOOLS_DEDUP" + tag "modules" + tag "modules_nfcore" + tag "umitools" + tag "umitools/dedup" + test("se - no stats") { config "./nextflow.config" @@ -14,8 +19,8 @@ nextflow_process { input[0] = [ [ id:'test', single_end:true ], // meta map - file(params.modules_testdata_base_path + "genomics/sarscov2/illumina/bam/test.single_end.sorted.bam", checkIfExists: true), - file(params.modules_testdata_base_path + "genomics/sarscov2/illumina/bam/test.single_end.sorted.bam.bai", checkIfExists: true) + file(params.modules_testdata_base_path + "genomics/sarscov2/illumina/bam/test.single_end.umi.sorted.bam", checkIfExists: true), + file(params.modules_testdata_base_path + "genomics/sarscov2/illumina/bam/test.single_end.umi.sorted.bam.bai", checkIfExists: true) ] input[1] = get_output_stats """ @@ -43,8 +48,8 @@ nextflow_process { input[0] = [ [ id:'test', single_end:false ], // meta map - file(params.modules_testdata_base_path + "genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam", checkIfExists: true), - file(params.modules_testdata_base_path + "genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam.bai", checkIfExists: true) + file(params.modules_testdata_base_path + "genomics/sarscov2/illumina/bam/test.paired_end.umi.sorted.bam", checkIfExists: true), + file(params.modules_testdata_base_path + "genomics/sarscov2/illumina/bam/test.paired_end.umi.sorted.bam.bai", checkIfExists: true) ] input[1] = get_output_stats """ @@ -56,7 +61,7 @@ nextflow_process { { assert process.success }, { assert path("${process.out.log[0][1]}").exists() }, { assert snapshot( - process.out.bam, + bam(process.out.bam[0][1]).getSamLinesMD5(), process.out.versions).match() } ) } @@ -72,8 +77,8 @@ nextflow_process { input[0] = [ [ id:'test', single_end:false ], // meta map - file(params.modules_testdata_base_path + "genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam", checkIfExists: true), - file(params.modules_testdata_base_path + "genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam.bai", checkIfExists: true) + file(params.modules_testdata_base_path + "genomics/sarscov2/illumina/bam/test.paired_end.umi.sorted.bam", checkIfExists: true), + file(params.modules_testdata_base_path + "genomics/sarscov2/illumina/bam/test.paired_end.umi.sorted.bam.bai", checkIfExists: true) ] input[1] = get_output_stats """ @@ -85,7 +90,7 @@ nextflow_process { { assert process.success }, { assert path("${process.out.log[0][1]}").exists() }, { assert snapshot( - process.out.bam, + bam(process.out.bam[0][1]).getSamLinesMD5(), process.out.tsv_edit_distance, process.out.tsv_per_umi, process.out.tsv_umi_per_position, @@ -107,8 +112,8 @@ nextflow_process { input[0] = [ [ id:'test', single_end:true ], // meta map - file(params.modules_testdata_base_path + "genomics/sarscov2/illumina/bam/test.single_end.sorted.bam", checkIfExists: true), - file(params.modules_testdata_base_path + "genomics/sarscov2/illumina/bam/test.single_end.sorted.bam.bai", checkIfExists: true) + file(params.modules_testdata_base_path + "genomics/sarscov2/illumina/bam/test.single_end.umi.sorted.bam", checkIfExists: true), + file(params.modules_testdata_base_path + "genomics/sarscov2/illumina/bam/test.single_end.umi.sorted.bam.bai", checkIfExists: true) ] input[1] = get_output_stats """ @@ -136,8 +141,8 @@ nextflow_process { input[0] = [ [ id:'test', single_end:false ], // meta map - file(params.modules_testdata_base_path + "genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam", checkIfExists: true), - file(params.modules_testdata_base_path + "genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam.bai", checkIfExists: true) + file(params.modules_testdata_base_path + "genomics/sarscov2/illumina/bam/test.paired_end.umi.sorted.bam", checkIfExists: true), + file(params.modules_testdata_base_path + "genomics/sarscov2/illumina/bam/test.paired_end.umi.sorted.bam.bai", checkIfExists: true) ] input[1] = get_output_stats """ @@ -165,8 +170,8 @@ nextflow_process { input[0] = [ [ id:'test', single_end:false ], // meta map - file(params.modules_testdata_base_path + "genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam", checkIfExists: true), - file(params.modules_testdata_base_path + "genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam.bai", checkIfExists: true) + file(params.modules_testdata_base_path + "genomics/sarscov2/illumina/bam/test.paired_end.umi.sorted.bam", checkIfExists: true), + file(params.modules_testdata_base_path + "genomics/sarscov2/illumina/bam/test.paired_end.umi.sorted.bam.bai", checkIfExists: true) ] input[1] = get_output_stats """ diff --git a/modules/nf-core/umitools/dedup/tests/main.nf.test.snap b/modules/nf-core/umitools/dedup/tests/main.nf.test.snap index f7f4e94f1..04b81692a 100644 --- a/modules/nf-core/umitools/dedup/tests/main.nf.test.snap +++ b/modules/nf-core/umitools/dedup/tests/main.nf.test.snap @@ -37,13 +37,14 @@ }, "pe - with stats": { "content": [ + "b7be15ac7aae194b04bdbb56f3534495", [ [ { "id": "test", "single_end": false }, - "test.dedup.bam:md5,350e942a0d45e8356fa24bc8c47dc1ed" + "test.dedup_edit_distance.tsv:md5,c247a49b58768e6e2e86a6c08483e612" ] ], [ @@ -52,7 +53,7 @@ "id": "test", "single_end": false }, - "test.dedup_edit_distance.tsv:md5,65186b0964e2f8d970cc04d736d8b119" + "test.dedup_per_umi.tsv:md5,ced75f7bdbf38bf78f3137d5325a8773" ] ], [ @@ -61,16 +62,7 @@ "id": "test", "single_end": false }, - "test.dedup_per_umi.tsv:md5,8e6783a4a79437b095f095f2aefe7c01" - ] - ], - [ - [ - { - "id": "test", - "single_end": false - }, - "test.dedup_per_umi_per_position.tsv:md5,9386db4a104b8e4e32f3ca4a84efa4ac" + "test.dedup_per_umi_per_position.tsv:md5,2e1a12e6f720510880068deddeefe063" ] ], [ @@ -79,9 +71,9 @@ ], "meta": { "nf-test": "0.8.4", - "nextflow": "24.04.2" + "nextflow": "24.10.1" }, - "timestamp": "2024-07-03T11:27:24.231325" + "timestamp": "2024-11-25T17:25:28.939957" }, "se - no stats - stub": { "content": [ @@ -103,36 +95,28 @@ }, "se - no stats": { "content": [ - "a114abd9fccce6fe2869852b5cd18964", + "9158ea6e7a0e54819e25cbac5fbc5cc0", [ "versions.yml:md5,e2f5146464c09bf7ae98c85ea5410e50" ] ], "meta": { "nf-test": "0.8.4", - "nextflow": "24.04.2" + "nextflow": "24.10.1" }, - "timestamp": "2024-07-03T13:45:48.553561" + "timestamp": "2024-11-23T09:06:54.373171" }, "pe - no stats": { "content": [ - [ - [ - { - "id": "test", - "single_end": false - }, - "test.dedup.bam:md5,350e942a0d45e8356fa24bc8c47dc1ed" - ] - ], + "b7be15ac7aae194b04bdbb56f3534495", [ "versions.yml:md5,e2f5146464c09bf7ae98c85ea5410e50" ] ], "meta": { "nf-test": "0.8.4", - "nextflow": "24.04.2" + "nextflow": "24.10.1" }, - "timestamp": "2024-07-03T11:27:06.957467" + "timestamp": "2024-11-25T17:24:51.423637" } } \ No newline at end of file diff --git a/modules/nf-core/umitools/dedup/tests/tags.yml b/modules/nf-core/umitools/dedup/tests/tags.yml new file mode 100644 index 000000000..5934c5cb7 --- /dev/null +++ b/modules/nf-core/umitools/dedup/tests/tags.yml @@ -0,0 +1,2 @@ +umitools/dedup: + - "modules/nf-core/umitools/dedup/**" From 3bbd907933f914cf97c5810210b4828fed8e8f16 Mon Sep 17 00:00:00 2001 From: Matthias Zepper Date: Fri, 29 Nov 2024 18:29:03 +0100 Subject: [PATCH 028/107] Update tests in subworkflows as well. --- modules.json | 4 +- .../tests/main.nf.test | 60 ++--------------- .../tests/main.nf.test.snap | 64 +++++++++---------- .../tests/paired-end-umis.config | 6 +- .../tests/main.nf.test | 22 +++++-- .../tests/main.nf.test.snap | 13 ++-- .../tests/tags.yml | 2 + 7 files changed, 68 insertions(+), 103 deletions(-) create mode 100644 subworkflows/nf-core/bam_dedup_stats_samtools_umitools/tests/tags.yml diff --git a/modules.json b/modules.json index 27b9ecc67..e0b6cf9c7 100644 --- a/modules.json +++ b/modules.json @@ -298,12 +298,12 @@ "nf-core": { "bam_dedup_stats_samtools_umicollapse": { "branch": "master", - "git_sha": "78991a2306e7631c006498a372e87f135a4212db", + "git_sha": "0b27602842d3d79fd0e8db79f4afa764967fc3d1", "installed_by": ["subworkflows"] }, "bam_dedup_stats_samtools_umitools": { "branch": "master", - "git_sha": "763d4b5c05ffda3ac1ac969dc67f7458cfb2eb1d", + "git_sha": "0b27602842d3d79fd0e8db79f4afa764967fc3d1", "installed_by": ["subworkflows"] }, "bam_markduplicates_picard": { diff --git a/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/main.nf.test b/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/main.nf.test index dd7f23718..f4f14c71b 100644 --- a/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/main.nf.test +++ b/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/main.nf.test @@ -22,55 +22,6 @@ nextflow_workflow { test("sarscov2_bam_bai") { - setup{ - run("UMITOOLS_EXTRACT"){ - script "../../../../modules/nf-core/umitools/extract/main.nf" - config "./paired-end-umis.config" - process{ - """ - input[0] = [ - [ id:'test', single_end:false ], // meta map - [ - file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_1.fastq.gz', checkIfExists: true), - file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_2.fastq.gz', checkIfExists: true) - ] - ] - """ - } - } - - run("BWA_INDEX"){ - script "../../../../modules/nf-core/bwa/index/main.nf" - process{ - """ - input[0] = [ - [ id:'sarscov2'], - file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true) - ] - """ - } - } - run("BWA_MEM"){ - script "../../../../modules/nf-core/bwa/mem/main.nf" - process{ - """ - input[0] = UMITOOLS_EXTRACT.out.reads - input[1] = BWA_INDEX.out.index - input[2] = [[ id:'sarscov2'],file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)] - input[3] = true - """ - } - } - run("SAMTOOLS_INDEX"){ - script "../../../../modules/nf-core/samtools/index/main.nf" - process{ - """ - input[0] = BWA_MEM.out.bam - """ - } - } - } - when { config "./paired-end-umis.config" params { @@ -78,9 +29,11 @@ nextflow_workflow { } workflow { """ - - input[0] = BWA_MEM.out.bam.join(SAMTOOLS_INDEX.out.bai, by: [0]) - + input[0] = Channel.of([ + [ id:'test'], // meta map + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.umi.sorted.bam', checkIfExists: true), + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.umi.sorted.bam.bai', checkIfExists: true) + ]) """ } } @@ -88,7 +41,8 @@ nextflow_workflow { then { assertAll( { assert workflow.success}, - { assert snapshot(workflow.out.bam, workflow.out.versions).match() }, + { assert snapshot(bam(workflow.out.bam[0][1]).getSamLinesMD5()).match("test_bam_dedup_stats_samtools_umicollapse_bam")}, // separate, because of linting error otherwise + { assert snapshot(workflow.out.versions).match("test_bam_dedup_stats_samtools_umicollapse_versions") }, { assert workflow.out.bam.get(0).get(1) ==~ ".*.bam"}, { assert workflow.out.bai.get(0).get(1) ==~ ".*.bai"}, { assert workflow.out.dedup_stats.get(0).get(1) ==~ ".*_UMICollapse.log"}, diff --git a/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/main.nf.test.snap b/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/main.nf.test.snap index f2b1fb20d..9694c6d5f 100644 --- a/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/main.nf.test.snap +++ b/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/main.nf.test.snap @@ -4,69 +4,57 @@ [ [ { - "id": "test", - "single_end": false + "id": "test" }, "test.stats:md5,498621f92e86d55e4f7ae93170e6e733" ] ] ], "meta": { - "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nf-test": "0.8.4", + "nextflow": "24.10.1" }, - "timestamp": "2024-09-16T08:04:02.179870196" + "timestamp": "2024-11-24T13:57:02.323104" }, - "test_bam_dedup_stats_samtools_umicollapse_flagstats": { + "test_bam_dedup_stats_samtools_umicollapse_versions": { "content": [ [ - [ - { - "id": "test", - "single_end": false - }, - "test.flagstat:md5,18d602435a02a4d721b78d1812622159" - ] + "versions.yml:md5,20605eb79c410c0ed179ba660d82f75b", + "versions.yml:md5,23617661d2c899996bee2b05db027e25", + "versions.yml:md5,657bce03545b4c57f9c5fc4314bf85f7", + "versions.yml:md5,e02a62a393a833778e16542eeed0d148", + "versions.yml:md5,ef00762e264b99ac45713dc0dedf4060" ] ], "meta": { "nf-test": "0.8.4", - "nextflow": "23.10.1" + "nextflow": "24.10.1" }, - "timestamp": "2024-04-09T17:05:48.69612524" + "timestamp": "2024-11-25T18:39:15.637444" }, - "sarscov2_bam_bai": { + "test_bam_dedup_stats_samtools_umicollapse_flagstats": { "content": [ [ [ { - "id": "test", - "single_end": false + "id": "test" }, - "test.dedup.bam:md5,3e2ae4701e3d2ca074ea878a314a3e4f" + "test.flagstat:md5,18d602435a02a4d721b78d1812622159" ] - ], - [ - "versions.yml:md5,20605eb79c410c0ed179ba660d82f75b", - "versions.yml:md5,23617661d2c899996bee2b05db027e25", - "versions.yml:md5,657bce03545b4c57f9c5fc4314bf85f7", - "versions.yml:md5,e02a62a393a833778e16542eeed0d148", - "versions.yml:md5,ef00762e264b99ac45713dc0dedf4060" ] ], "meta": { - "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nf-test": "0.8.4", + "nextflow": "24.10.1" }, - "timestamp": "2024-10-22T10:44:38.266860983" + "timestamp": "2024-11-24T13:57:02.366866" }, "test_bam_dedup_stats_samtools_umicollapse_idxstats": { "content": [ [ [ { - "id": "test", - "single_end": false + "id": "test" }, "test.idxstats:md5,85d20a901eef23ca50c323638a2eb602" ] @@ -74,8 +62,18 @@ ], "meta": { "nf-test": "0.8.4", - "nextflow": "23.10.1" + "nextflow": "24.10.1" + }, + "timestamp": "2024-11-24T13:57:02.410712" + }, + "test_bam_dedup_stats_samtools_umicollapse_bam": { + "content": [ + "b7be15ac7aae194b04bdbb56f3534495" + ], + "meta": { + "nf-test": "0.8.4", + "nextflow": "24.10.1" }, - "timestamp": "2024-04-09T17:05:48.740441747" + "timestamp": "2024-11-25T18:39:15.613319" } } \ No newline at end of file diff --git a/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/paired-end-umis.config b/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/paired-end-umis.config index 602c026f0..8d58a861f 100644 --- a/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/paired-end-umis.config +++ b/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/paired-end-umis.config @@ -1,10 +1,6 @@ process { - withName: UMITOOLS_EXTRACT { - ext.args = '--bc-pattern="NNNN" --bc-pattern2="NNNN"' - } - withName: UMICOLLAPSE { ext.prefix = { "${meta.id}.dedup" } } -} \ No newline at end of file +} diff --git a/subworkflows/nf-core/bam_dedup_stats_samtools_umitools/tests/main.nf.test b/subworkflows/nf-core/bam_dedup_stats_samtools_umitools/tests/main.nf.test index ab541cb88..93e624857 100644 --- a/subworkflows/nf-core/bam_dedup_stats_samtools_umitools/tests/main.nf.test +++ b/subworkflows/nf-core/bam_dedup_stats_samtools_umitools/tests/main.nf.test @@ -3,6 +3,19 @@ nextflow_workflow { name "Test Workflow BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS" script "../main.nf" workflow "BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS" + tag "subworkflows" + tag "subworkflows_nfcore" + tag "subworkflows/bam_dedup_stats_samtools_umitools" + tag "subworkflows/bam_stats_samtools" + tag "bam_dedup_stats_samtools_umitools" + tag "bam_stats_samtools" + tag "samtools" + tag "samtools/index" + tag "samtools/stats" + tag "samtools/idxstats" + tag "samtools/flagstat" + tag "umitools" + tag "umitools/dedup" test("sarscov2_bam_bai") { @@ -13,8 +26,8 @@ nextflow_workflow { input[0] = Channel.of([ [ id:'test'], // meta map - file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam', checkIfExists: true), - file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam.bai', checkIfExists: true) + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.umi.sorted.bam', checkIfExists: true), + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.umi.sorted.bam.bai', checkIfExists: true) ]) input[1] = val_get_dedup_stats """ @@ -28,6 +41,7 @@ nextflow_workflow { { assert workflow.out.bam.get(0).get(1) ==~ ".*.bam"}, { assert workflow.out.bai.get(0).get(1) ==~ ".*.bai"}, { assert snapshot( + bam(workflow.out.bam[0][1]).getSamLinesMD5(), workflow.out.stats, workflow.out.flagstat, workflow.out.idxstats, @@ -48,8 +62,8 @@ nextflow_workflow { input[0] = Channel.of([ [ id:'test'], // meta map - file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam', checkIfExists: true), - file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam.bai', checkIfExists: true) + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.umi.sorted.bam', checkIfExists: true), + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.umi.sorted.bam.bai', checkIfExists: true) ]) input[1] = val_get_dedup_stats """ diff --git a/subworkflows/nf-core/bam_dedup_stats_samtools_umitools/tests/main.nf.test.snap b/subworkflows/nf-core/bam_dedup_stats_samtools_umitools/tests/main.nf.test.snap index d39f9129b..3b3613572 100644 --- a/subworkflows/nf-core/bam_dedup_stats_samtools_umitools/tests/main.nf.test.snap +++ b/subworkflows/nf-core/bam_dedup_stats_samtools_umitools/tests/main.nf.test.snap @@ -1,12 +1,13 @@ { "sarscov2_bam_bai": { "content": [ + "b7be15ac7aae194b04bdbb56f3534495", [ [ { "id": "test" }, - "test.stats:md5,84891a894010aeb882c4092db9248d2c" + "test.stats:md5,41ba57a9b90b54587e7d154e5405ea5e" ] ], [ @@ -14,7 +15,7 @@ { "id": "test" }, - "test.flagstat:md5,0bb716e40fae381b97484b58e0b16efe" + "test.flagstat:md5,18d602435a02a4d721b78d1812622159" ] ], [ @@ -22,7 +23,7 @@ { "id": "test" }, - "test.idxstats:md5,1adb27b52d4d64b826f48b59d61dcd4d" + "test.idxstats:md5,85d20a901eef23ca50c323638a2eb602" ] ], [ @@ -34,10 +35,10 @@ ] ], "meta": { - "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nf-test": "0.8.4", + "nextflow": "24.10.1" }, - "timestamp": "2024-09-16T08:04:23.444693448" + "timestamp": "2024-11-25T17:23:13.841219" }, "sarscov2_bam_bai - stub": { "content": [ diff --git a/subworkflows/nf-core/bam_dedup_stats_samtools_umitools/tests/tags.yml b/subworkflows/nf-core/bam_dedup_stats_samtools_umitools/tests/tags.yml new file mode 100644 index 000000000..bfd5e023e --- /dev/null +++ b/subworkflows/nf-core/bam_dedup_stats_samtools_umitools/tests/tags.yml @@ -0,0 +1,2 @@ +subworkflows/bam_dedup_stats_samtools_umitools: + - subworkflows/nf-core/bam_dedup_stats_samtools_umitools/** From c4fb14563a397151d925849a05650408faa63c1e Mon Sep 17 00:00:00 2001 From: Rob Syme Date: Fri, 29 Nov 2024 15:13:55 -0500 Subject: [PATCH 029/107] skip_sample_count --- assets/email_template.html | 19 ------------------- assets/email_template.txt | 7 ------- 2 files changed, 26 deletions(-) diff --git a/assets/email_template.html b/assets/email_template.html index dc46d39ee..e4639cdb6 100644 --- a/assets/email_template.html +++ b/assets/email_template.html @@ -34,25 +34,6 @@

    nf-core/rnaseq execution completed uns

    The full error message was:

    ${errorReport}
    - """ } else if(skip_sample_count > 0) { out << """ -
    -

    nf-core/rnaseq execution completed with warnings!

    -

    - The pipeline finished successfully, but samples were skipped. Please check warnings at the top of the MultiQC report. -

    -

    -
    - """ } else { out << """
    0) { - out << """################################################## -## nf-core/rnaseq execution completed with warnings ## -################################################## -The pipeline finished successfully, but samples were skipped. -Please check warnings at the top of the MultiQC report. -""" } else { out << "## nf-core/rnaseq execution completed successfully! ##" } From 2e4e98ce0d77b98cf4ef7abce45da4e06745e820 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Mon, 2 Dec 2024 16:25:11 +0000 Subject: [PATCH 030/107] Add linting-enabled preprocessing subworkflow and wire in --- modules.json | 2 +- nextflow.config | 3 + nextflow_schema.json | 13 +- .../main.nf | 59 ++++++- .../meta.yml | 165 ++++++++++++------ .../tests/main.nf.test | 45 ++++- .../tests/main.nf.test.snap | 76 ++++---- .../tests/nextflow.config | 13 ++ workflows/rnaseq/main.nf | 3 +- 9 files changed, 280 insertions(+), 99 deletions(-) diff --git a/modules.json b/modules.json index 9667d1b6b..bba180d79 100644 --- a/modules.json +++ b/modules.json @@ -341,7 +341,7 @@ }, "fastq_qc_trim_filter_setstrandedness": { "branch": "master", - "git_sha": "9082d6440bdffbb4f5d9bd9d753361933b3febcb", + "git_sha": "34774b3e29b5cdb70c1e08ac7860765b795648e2", "installed_by": ["subworkflows"] }, "fastq_subsample_fq_salmon": { diff --git a/nextflow.config b/nextflow.config index 468792d0f..d53633d23 100644 --- a/nextflow.config +++ b/nextflow.config @@ -38,6 +38,9 @@ params { umi_discard_read = null save_umi_intermeds = false + // Linting + skip_linting = false + // Trimming trimmer = 'trimgalore' min_trimmed_reads = 10000 diff --git a/nextflow_schema.json b/nextflow_schema.json index 802209b42..18adfa7d2 100644 --- a/nextflow_schema.json +++ b/nextflow_schema.json @@ -411,7 +411,7 @@ }, "min_mapped_reads": { "type": "number", - "default": 5.0, + "default": 5, "fa_icon": "fas fa-percentage", "description": "Minimum percentage of uniquely mapped reads below which samples are removed from further processing.", "help_text": "Some downstream steps in the pipeline will fail if this threshold is too low." @@ -456,14 +456,14 @@ "stranded_threshold": { "type": "number", "minimum": 0.5, - "maximum": 1.0, + "maximum": 1, "default": 0.8, "description": "The fraction of stranded reads that must be assigned to a strandedness for confident assignment. Must be at least 0.5." }, "unstranded_threshold": { "type": "number", - "minimum": 0.0, - "maximum": 1.0, + "minimum": 0, + "maximum": 1, "default": 0.1, "description": "The difference in fraction of stranded reads assigned to 'forward' and 'reverse' below which a sample is classified as 'unstranded'. By default the forward and reverse fractions must differ by less than 0.1 for the sample to be called as unstranded." } @@ -602,6 +602,11 @@ "fa_icon": "fas fa-compress-alt", "description": "Skip the UMI extraction from the read in case the UMIs have been moved to the headers in advance of the pipeline run." }, + "skip_linting": { + "type": "boolean", + "fa_icon": "fas fa-fast-forward", + "description": "Skip linting checks during FASTQ preprocessing and filtering." + }, "skip_trimming": { "type": "boolean", "description": "Skip the adapter trimming step.", diff --git a/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/main.nf b/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/main.nf index c655af415..f4cc1fa15 100644 --- a/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/main.nf +++ b/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/main.nf @@ -1,13 +1,17 @@ import groovy.json.JsonSlurper -include { BBMAP_BBSPLIT } from '../../../modules/nf-core/bbmap/bbsplit' -include { CAT_FASTQ } from '../../../modules/nf-core/cat/fastq/main' -include { SORTMERNA } from '../../../modules/nf-core/sortmerna/main' -include { SORTMERNA as SORTMERNA_INDEX } from '../../../modules/nf-core/sortmerna/main' - -include { FASTQ_SUBSAMPLE_FQ_SALMON } from '../fastq_subsample_fq_salmon' -include { FASTQ_FASTQC_UMITOOLS_TRIMGALORE } from '../fastq_fastqc_umitools_trimgalore' -include { FASTQ_FASTQC_UMITOOLS_FASTP } from '../fastq_fastqc_umitools_fastp' +include { BBMAP_BBSPLIT } from '../../../modules/nf-core/bbmap/bbsplit' +include { CAT_FASTQ } from '../../../modules/nf-core/cat/fastq/main' +include { SORTMERNA } from '../../../modules/nf-core/sortmerna/main' +include { SORTMERNA as SORTMERNA_INDEX } from '../../../modules/nf-core/sortmerna/main' +include { FQ_LINT } from '../../../modules/nf-core/fq/lint/main' +include { FQ_LINT as FQ_LINT_AFTER_TRIMMING } from '../../../modules/nf-core/fq/lint/main' +include { FQ_LINT as FQ_LINT_AFTER_BBMAP } from '../../../modules/nf-core/fq/lint/main' +include { FQ_LINT as FQ_LINT_AFTER_SORTMERNA } from '../../../modules/nf-core/fq/lint/main' + +include { FASTQ_SUBSAMPLE_FQ_SALMON } from '../fastq_subsample_fq_salmon' +include { FASTQ_FASTQC_UMITOOLS_TRIMGALORE } from '../fastq_fastqc_umitools_trimgalore' +include { FASTQ_FASTQC_UMITOOLS_FASTP } from '../fastq_fastqc_umitools_fastp' def pass_trimmed_reads = [:] @@ -106,6 +110,7 @@ workflow FASTQ_QC_TRIM_FILTER_SETSTRANDEDNESS { umi_discard_read // integer: 0, 1 or 2 stranded_threshold // float: The fraction of stranded reads that must be assigned to a strandedness for confident assignment. Must be at least 0.5 unstranded_threshold // float: The difference in fraction of stranded reads assigned to 'forward' and 'reverse' below which a sample is classified as 'unstranded' + skip_linting // boolean: true/false main: @@ -113,6 +118,19 @@ workflow FASTQ_QC_TRIM_FILTER_SETSTRANDEDNESS { ch_filtered_reads = Channel.empty() ch_trim_read_count = Channel.empty() ch_multiqc_files = Channel.empty() + ch_lint_log = Channel.empty() + + // + // MODULE: Lint FastQ files + // + if(!skip_linting) { + FQ_LINT ( + ch_reads.map{ meta, fastqs -> [meta, fastqs.flatten()] } + ) + ch_versions = ch_versions.mix(FQ_LINT.out.versions.first()) + ch_lint_log = ch_lint_log.mix(FQ_LINT.out.lint) + ch_reads = ch_reads.join(FQ_LINT.out.lint.map{it[0]}) + } ch_reads .branch { @@ -212,6 +230,14 @@ workflow FASTQ_QC_TRIM_FILTER_SETSTRANDEDNESS { .map { [[:], it] } ) + if((!skip_linting) && (!skip_trimming)) { + FQ_LINT_AFTER_TRIMMING ( + ch_filtered_reads + ) + ch_lint_log = ch_lint_log.mix(FQ_LINT_AFTER_TRIMMING.out.lint) + ch_filtered_reads = ch_filtered_reads.join(FQ_LINT_AFTER_TRIMMING.out.lint.map{it[0]}) + } + // // MODULE: Remove genome contaminant reads // @@ -228,6 +254,14 @@ workflow FASTQ_QC_TRIM_FILTER_SETSTRANDEDNESS { .set { ch_filtered_reads } ch_versions = ch_versions.mix(BBMAP_BBSPLIT.out.versions.first()) + + if(!skip_linting) { + FQ_LINT_AFTER_BBSPLIT ( + ch_filtered_reads + ) + ch_lint_log = ch_lint_log.mix(FQ_LINT_AFTER_BBSPLIT.out.lint) + ch_filtered_reads = ch_filtered_reads.join(FQ_LINT_AFTER_BBSPLIT.out.lint.map{it[0]}) + } } // @@ -260,6 +294,14 @@ workflow FASTQ_QC_TRIM_FILTER_SETSTRANDEDNESS { .mix(SORTMERNA.out.log) ch_versions = ch_versions.mix(SORTMERNA.out.versions.first()) + + if(!skip_linting) { + FQ_LINT_AFTER_SORTMERNA ( + ch_filtered_reads + ) + ch_lint_log = ch_lint_log.mix(FQ_LINT_AFTER_SORTMERNA.out.lint) + ch_filtered_reads = ch_filtered_reads.join(FQ_LINT_AFTER_SORTMERNA.out.lint.map{it[0]}) + } } // Branch FastQ channels if 'auto' specified to infer strandedness @@ -312,6 +354,7 @@ workflow FASTQ_QC_TRIM_FILTER_SETSTRANDEDNESS { emit: + lint_log = ch_lint_log reads = ch_strand_inferred_fastq trim_read_count = ch_trim_read_count diff --git a/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/meta.yml b/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/meta.yml index 6f92f56a0..433837d70 100644 --- a/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/meta.yml +++ b/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/meta.yml @@ -1,6 +1,5 @@ -# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/subworkflows/yaml-schema.json name: "fastq_qc_trim_filter_setstrandedness" -description: Basic FASTQ preprocessing for RNA-seq +description: Performs linting, quality control, trimming, filtering, and strandedness determination on RNA-seq FASTQ files, preparing them for downstream analysis. keywords: - fastq - rnaseq @@ -19,39 +18,84 @@ components: - fastq_fastqc_umitools_trimgalore - fastq_fastqc_umitools_fastp input: - - meta: - type: map - description: | - Groovy Map containing sample information - e.g. [ id:'test' ] - ch_reads: - type: file - description: | - Channel with input FastQ files of size 1 and 2 for single-end and - paired-end data, respectively. + description: Channel with input FastQ files + structure: + - meta: + type: map + description: Groovy Map containing sample information e.g. [ id:'test' ] + - reads: + type: file + description: FastQ files + pattern: "*.{fq,fastq},{,.gz}" - ch_fasta: - type: file description: Channel with genome sequence in fasta format + structure: + - meta: + type: map + description: Metadata for the fasta file + - fasta: + type: file + description: Genome fasta file + pattern: "*.{fa,fasta}" - ch_transcript_fasta: - type: file description: Channel with transcriptome sequence in fasta format + structure: + - meta: + type: map + description: Metadata for the transcript fasta file + - fasta: + type: file + description: Transcript fasta file + pattern: "*.{fa,fasta}" - ch_gtf: - type: file description: Channel with features in GTF format + structure: + - meta: + type: map + description: Metadata for the GTF file + - gtf: + type: file + description: GTF file + pattern: "*.gtf" - ch_salmon_index: - type: file description: Directory containing Salmon index + structure: + - meta: + type: map + description: Metadata for the Salmon index + - index: + type: directory + description: Salmon index directory - ch_sortmerna_index: - type: file description: Directory containing sortmerna index + structure: + - meta: + type: map + description: Metadata for the SortMeRNA index + - index: + type: directory + description: SortMeRNA index directory - ch_bbsplit_index: - type: file description: Path to directory or tar.gz archive for pre-built BBSplit index + structure: + - meta: + type: map + description: Metadata for the BBSplit index + - index: + type: file + description: BBSplit index directory or tar.gz archive + pattern: "{*,*.tar.gz}" - ch_rrna_fastas: - type: file - description: | - Channel containing one or more FASTA files containing rRNA sequences - for use with SortMeRNA + description: Channel containing one or more FASTA files containing rRNA sequences for use with SortMeRNA + structure: + - meta: + type: map + description: Metadata for the rRNA fasta files + - fasta: + type: file + description: rRNA fasta files + pattern: "*.{fa,fasta}" - skip_bbsplit: type: boolean description: Whether to skip BBSplit for removal of non-reference genome reads @@ -63,9 +107,7 @@ input: description: Whether to skip trimming - skip_umi_extract: type: boolean - description: | - Skip the UMI extraction from the read in case the UMIs have been moved - to the headers in advance of the pipeline run + description: Skip the UMI extraction from the read in case the UMIs have been moved to the headers in advance of the pipeline run - make_salmon_index: type: boolean description: Whether to create salmon index before running salmon quant @@ -74,14 +116,10 @@ input: description: Whether to create sortmerna index before running sortmerna - trimmer: type: string - description: | - Specifies the trimming tool to use - available options are 'trimgalore' - and 'fastp' + description: Specifies the trimming tool to use - available options are 'trimgalore' and 'fastp' - min_trimmed_reads: type: integer - description: | - Minimum number of trimmed reads below which samples are removed from - further processing + description: Minimum number of trimmed reads below which samples are removed from further processing - save_trimmed: type: boolean description: Save the trimmed FastQ files in the results directory? @@ -93,39 +131,66 @@ input: description: Enable UMI-based read deduplication - umi_discard_read: type: integer - description: | - After UMI barcode extraction discard either R1 or R2 by setting this - parameter to 1 or 2, respectively + description: After UMI barcode extraction discard either R1 or R2 by setting this parameter to 1 or 2, respectively - stranded_threshold: type: float min: 0.5 - description: | - The fraction of stranded reads that must be assigned to a strandedness - for confident assignment. Must be at least 0.5. + description: The fraction of stranded reads that must be assigned to a strandedness for confident assignment. Must be at least 0.5. - unstranded_threshold: type: float - description: | - The difference in fraction of stranded reads assigned to 'forward' and - 'reverse' below which a sample is classified as 'unstranded'. + description: The difference in fraction of stranded reads assigned to 'forward' and 'reverse' below which a sample is classified as 'unstranded'. + - skip_linting: + type: boolean + description: Whether to skip linting of FastQ files output: - reads: - type: file description: Preprocessed fastq reads - pattern: "*.{fq,fastq}{,.gz}" + structure: + - meta: + type: map + description: Metadata for the preprocessed reads + - reads: + type: file + description: Preprocessed FastQ files + pattern: "*.{fq,fastq},{,.gz}" - multiqc_files: - type: file - description: MultiQC-compatible output files from tools used in prepreocessing - pattern: "*" + description: MultiQC-compatible output files from tools used in preprocessing + structure: + - meta: + type: map + description: Metadata for the MultiQC files + - mqc: + type: file + description: MultiQC-compatible files + pattern: "*" - trim_read_count: - type: integer description: Number of reads remaining after trimming for all input samples + structure: + - meta: + type: map + description: Metadata for the trim read count + - count: + type: integer + description: Number of reads after trimming - versions: - type: file - description: | - File containing software versions - Structure: [ path(versions.yml) ] - pattern: "versions.yml" + description: File containing software versions + structure: + - versions: + type: file + description: File containing software versions + pattern: "versions.yml" + - lint_log: + description: Log files from FastQ linting + structure: + - meta: + type: map + description: Metadata for the lint log + - log: + type: file + description: FastQ lint log file + pattern: "*.log" + authors: - "@pinin4fjords" maintainers: diff --git a/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/tests/main.nf.test b/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/tests/main.nf.test index c752e176c..99c4d931a 100644 --- a/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/tests/main.nf.test +++ b/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/tests/main.nf.test @@ -5,6 +5,21 @@ nextflow_workflow { workflow "FASTQ_QC_TRIM_FILTER_SETSTRANDEDNESS" config "./nextflow.config" + tag "subworkflows" + tag "subworkflows_nfcore" + tag "subworkflows/fastq_qc_trim_filter_setstrandedness" + + tag "bbmap/bbsplit" + tag "cat" + tag "cat/fastq" + tag "fastqc" + tag "sortmerna" + tag "subworkflows/fastq_fastqc_umitools_trimgalore" + tag "subworkflows/fastq_fastqc_umitools_fastp" + tag "subworkflows/fastq_subsample_fq_salmon" + + + test("homo_sapiens paired-end [fastq] fastp") { when { @@ -25,7 +40,7 @@ nextflow_workflow { input[4] = [] // ch_salmon_index input[5] = [] // ch_sortmerna_index input[6] = [] // ch_bbsplit_index - input[7] = Channel.of(file('https://raw.githubusercontent.com/biocore/sortmerna/v4.3.4/data/rRNA_databases/rfam-5.8s-database-id98.fasta', checkIfExists: true)) // ch_rrna_fastas + input[7] = Channel.of(file('https://raw.githubusercontent.com/biocore/sortmerna/v4.3.4/data/rRNA_databases/rfam-5.8s-database-id98.fasta', checkIfExists: true)) // ch_rrna_fastas input[8] = true // skip_bbsplit input[9] = false // skip_fastqc input[10] = false // skip_trimming @@ -40,6 +55,7 @@ nextflow_workflow { input[19] = 0 // umi_discard_read input[20] = 0.8 // stranded_threshold input[21] = 0.1 // unstranded_threshold + input[22] = false // skip_linting """ } } @@ -47,13 +63,22 @@ nextflow_workflow { then { def pelines1 = path(workflow.out.reads[0][1][0]).linesGzip def pelines2 = path(workflow.out.reads[0][1][1]).linesGzip + + // First part of each fq lint report line is a timestamp, remove it before snapshotting + def processed_sortmerna_lint_report = path(workflow.out.lint_log.find { entry -> entry[1].contains('sortmerna')}?.getAt(1)) + .getText() + .readLines() + .collect { line -> line.split(' ', 2)[1] } // Split by the first space and take everything after it + .join('\n') // Join the processed lines back into a single text block + assertAll( { assert workflow.success}, { assert snapshot(pelines1).md5().match("fastp_test_pe_reads_1_lines") }, { assert snapshot(pelines1.size()).match("fastp_test_pe_reads_1_size") }, { assert snapshot(pelines2).md5().match("fastp_test_pe_reads_2_lines") }, { assert snapshot(pelines2.size()).match("fastp_test_pe_reads_2_size") }, - { assert snapshot(workflow.out.trim_read_count).match("fastp_read_count") } + { assert snapshot(workflow.out.trim_read_count).match("fastp_read_count") }, + { assert snapshot(processed_sortmerna_lint_report).md5().match("fastp_lint") } // This doesn't work- 'cat' changes between Conda and Docker - // leaving it here until we find a way to address that // { assert snapshot(workflow.out.versions).match("fastp_versions") } @@ -80,7 +105,7 @@ nextflow_workflow { input[4] = [] // ch_salmon_index input[5] = [] // ch_sortmerna_index input[6] = [] // ch_bbsplit_index - input[7] = Channel.of(file('https://raw.githubusercontent.com/biocore/sortmerna/v4.3.4/data/rRNA_databases/rfam-5.8s-database-id98.fasta', checkIfExists: true)) // ch_rrna_fastas + input[7] = Channel.of(file('https://raw.githubusercontent.com/biocore/sortmerna/v4.3.4/data/rRNA_databases/rfam-5.8s-database-id98.fasta', checkIfExists: true)) // ch_rrna_fastas input[8] = true // skip_bbsplit input[9] = false // skip_fastqc input[10] = false // skip_trimming @@ -95,6 +120,7 @@ nextflow_workflow { input[19] = 0 // umi_discard_read input[20] = 0.8 // stranded_threshold input[21] = 0.1 // unstranded_threshold + input[22] = false // skip_linting """ } } @@ -102,19 +128,28 @@ nextflow_workflow { then { def pelines1 = path(workflow.out.reads[0][1][0]).linesGzip def pelines2 = path(workflow.out.reads[0][1][1]).linesGzip + + // First part of each fq lint report line is a timestamp, remove it before snapshotting + def processed_sortmerna_lint_report = path(workflow.out.lint_log.find { entry -> entry[1].contains('sortmerna')}?.getAt(1)) + .getText() + .readLines() + .collect { line -> line.split(' ', 2)[1] } // Split by the first space and take everything after it + .join('\n') // Join the processed lines back into a single text block + assertAll( { assert workflow.success}, { assert snapshot(pelines1).md5().match("trimgalore_test_pe_reads_1_lines") }, { assert snapshot(pelines1.size()).match("trimgalore_test_pe_reads_1_size") }, { assert snapshot(pelines2).md5().match("trimgalore_test_pe_reads_2_lines") }, { assert snapshot(pelines2.size()).match("trimgalore_test_pe_reads_2_size") }, - { assert snapshot(workflow.out.trim_read_count).match("trimgalore_read_count") } + { assert snapshot(workflow.out.trim_read_count).match("trimgalore_read_count") }, + { assert snapshot(processed_sortmerna_lint_report).md5().match("trimgalore_lint") } // This doesn't work- 'cat' changes between Conda and Docker - // leaving it here until we find a way to address that //{ assert snapshot(workflow.out.versions).match("trimgalore_versions") } ) } } - + } diff --git a/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/tests/main.nf.test.snap b/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/tests/main.nf.test.snap index 985349317..b02450659 100644 --- a/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/tests/main.nf.test.snap +++ b/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/tests/main.nf.test.snap @@ -13,82 +13,98 @@ ] ], "meta": { - "nf-test": "0.8.4", - "nextflow": "24.04.3" + "nf-test": "0.9.2", + "nextflow": "24.10.2" }, - "timestamp": "2024-07-17T10:24:00.044553245" + "timestamp": "2024-12-02T12:56:09.941793" }, "trimgalore_test_pe_reads_2_lines": { "content": "eccf3e9e74589ff01c77fce7f4548e41", "meta": { - "nf-test": "0.8.4", - "nextflow": "24.04.3" + "nf-test": "0.9.2", + "nextflow": "24.10.2" }, - "timestamp": "2024-07-17T10:24:26.838793051" + "timestamp": "2024-12-02T12:56:34.948679" }, "fastp_test_pe_reads_1_size": { "content": [ 4508 ], "meta": { - "nf-test": "0.8.4", - "nextflow": "24.04.3" + "nf-test": "0.9.2", + "nextflow": "24.10.2" }, - "timestamp": "2024-07-17T10:23:59.889337984" + "timestamp": "2024-12-02T12:56:09.889501" }, "trimgalore_test_pe_reads_1_size": { "content": [ 4508 ], "meta": { - "nf-test": "0.8.4", - "nextflow": "24.04.3" + "nf-test": "0.9.2", + "nextflow": "24.10.2" }, - "timestamp": "2024-07-17T10:24:26.778599725" + "timestamp": "2024-12-02T12:56:34.91497" + }, + "trimgalore_lint": { + "content": "daec499818124330ef90e5af47383f00", + "meta": { + "nf-test": "0.9.2", + "nextflow": "24.10.2" + }, + "timestamp": "2024-12-02T12:56:34.952929" }, "trimgalore_test_pe_reads_1_lines": { "content": "3868fc1caf09367141d2bbf47e158823", "meta": { - "nf-test": "0.8.4", - "nextflow": "24.04.3" + "nf-test": "0.9.2", + "nextflow": "24.10.2" + }, + "timestamp": "2024-12-02T12:56:34.913312" + }, + "fastp_lint": { + "content": "daec499818124330ef90e5af47383f00", + "meta": { + "nf-test": "0.9.2", + "nextflow": "24.10.2" }, - "timestamp": "2024-07-17T10:24:26.774975135" + "timestamp": "2024-12-02T12:56:09.943788" }, "fastp_test_pe_reads_2_lines": { "content": "eccf3e9e74589ff01c77fce7f4548e41", "meta": { - "nf-test": "0.8.4", - "nextflow": "24.04.3" + "nf-test": "0.9.2", + "nextflow": "24.10.2" }, - "timestamp": "2024-07-17T10:23:59.997625278" + "timestamp": "2024-12-02T12:56:09.93962" }, "fastp_test_pe_reads_2_size": { "content": [ 4508 ], "meta": { - "nf-test": "0.8.4", - "nextflow": "24.04.3" + "nf-test": "0.9.2", + "nextflow": "24.10.2" }, - "timestamp": "2024-07-17T10:24:00.042449965" + "timestamp": "2024-12-02T12:56:09.940673" }, "trimgalore_test_pe_reads_2_size": { "content": [ 4508 ], "meta": { - "nf-test": "0.8.4", - "nextflow": "24.04.3" + "nf-test": "0.9.2", + "nextflow": "24.10.2" }, - "timestamp": "2024-07-17T10:24:26.841434261" + "timestamp": "2024-12-02T12:56:34.949981" }, "fastp_test_pe_reads_1_lines": { "content": "3868fc1caf09367141d2bbf47e158823", "meta": { - "nf-test": "0.8.4", - "nextflow": "24.04.3" + "nf-test": "0.9.2", + "nextflow": "24.10.2" }, - "timestamp": "2024-07-17T10:23:59.882844295" + "timestamp": "2024-12-02T12:56:09.887546" }, "trimgalore_read_count": { "content": [ @@ -104,9 +120,9 @@ ] ], "meta": { - "nf-test": "0.8.4", - "nextflow": "24.04.3" + "nf-test": "0.9.2", + "nextflow": "24.10.2" }, - "timestamp": "2024-07-17T10:24:26.84402498" + "timestamp": "2024-12-02T12:56:34.951137" } } \ No newline at end of file diff --git a/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/tests/nextflow.config b/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/tests/nextflow.config index 9e33e4b33..71f2d0d25 100644 --- a/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/tests/nextflow.config +++ b/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/tests/nextflow.config @@ -3,6 +3,19 @@ // process { + + withName: 'FQ_LINT_AFTER_TRIMMING' { + ext.prefix = { "${meta.id}.trimmed" } + } + + withName: 'FQ_LINT_AFTER_BBMAP' { + ext.prefix = { "${meta.id}.bbmap" } + } + + withName: 'FQ_LINT_AFTER_SORTMERNA' { + ext.prefix = { "${meta.id}.sortmerna" } + } + withName: 'FQ_SUBSAMPLE' { ext.args = '--record-count 1000000 --seed 1' ext.prefix = { "${meta.id}.subsampled" } diff --git a/workflows/rnaseq/main.nf b/workflows/rnaseq/main.nf index 84bedaeb6..d65912df0 100755 --- a/workflows/rnaseq/main.nf +++ b/workflows/rnaseq/main.nf @@ -156,7 +156,8 @@ workflow RNASEQ { params.with_umi, params.umi_discard_read, params.stranded_threshold, - params.unstranded_threshold + params.unstranded_threshold, + params.skip_linting ) ch_multiqc_files = ch_multiqc_files.mix(FASTQ_QC_TRIM_FILTER_SETSTRANDEDNESS.out.multiqc_files) From bacbecb581509f7e7faee4883a5faf6540f22921 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Mon, 2 Dec 2024 16:30:07 +0000 Subject: [PATCH 031/107] Update CHANGELOG --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 39e77a767..81ea91f1f 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -26,6 +26,7 @@ Special thanks to the following for their contributions to the release: - [PR #1437](https://github.com/nf-core/rnaseq/pull/1437) - Remove conda from test matrix - [PR #1440](https://github.com/nf-core/rnaseq/pull/1440) - Further reduce matrix - [PR #1441](https://github.com/nf-core/rnaseq/pull/1441) - Force some containers to be converted from docker to singularity to fix nf-core download tests +- [PR #1461](https://github.com/nf-core/rnaseq/pull/1461) - Add FASTQ linting during preprocessing ### Software dependencies From dd98da7939b007daab08407c27d44a20a0bbb230 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Mon, 2 Dec 2024 16:45:02 +0000 Subject: [PATCH 032/107] Add fq/lint --- modules.json | 5 ++ modules/nf-core/fq/lint/environment.yml | 5 ++ modules/nf-core/fq/lint/main.nf | 33 +++++++++++ modules/nf-core/fq/lint/meta.yml | 43 ++++++++++++++ modules/nf-core/fq/lint/tests/main.nf.test | 58 +++++++++++++++++++ .../nf-core/fq/lint/tests/main.nf.test.snap | 25 ++++++++ 6 files changed, 169 insertions(+) create mode 100644 modules/nf-core/fq/lint/environment.yml create mode 100644 modules/nf-core/fq/lint/main.nf create mode 100644 modules/nf-core/fq/lint/meta.yml create mode 100644 modules/nf-core/fq/lint/tests/main.nf.test create mode 100644 modules/nf-core/fq/lint/tests/main.nf.test.snap diff --git a/modules.json b/modules.json index bba180d79..7c45311e1 100644 --- a/modules.json +++ b/modules.json @@ -57,6 +57,11 @@ "git_sha": "666652151335353eef2fcd58880bcef5bc2928e1", "installed_by": ["fastq_fastqc_umitools_fastp", "fastq_fastqc_umitools_trimgalore"] }, + "fq/lint": { + "branch": "master", + "git_sha": "34774b3e29b5cdb70c1e08ac7860765b795648e2", + "installed_by": ["fastq_qc_trim_filter_setstrandedness", "modules"] + }, "fq/subsample": { "branch": "master", "git_sha": "a1abf90966a2a4016d3c3e41e228bfcbd4811ccc", diff --git a/modules/nf-core/fq/lint/environment.yml b/modules/nf-core/fq/lint/environment.yml new file mode 100644 index 000000000..74b146083 --- /dev/null +++ b/modules/nf-core/fq/lint/environment.yml @@ -0,0 +1,5 @@ +channels: + - conda-forge + - bioconda +dependencies: + - bioconda::fq=0.12.0 diff --git a/modules/nf-core/fq/lint/main.nf b/modules/nf-core/fq/lint/main.nf new file mode 100644 index 000000000..943314c87 --- /dev/null +++ b/modules/nf-core/fq/lint/main.nf @@ -0,0 +1,33 @@ +process FQ_LINT { + tag "$meta.id" + label 'process_low' + + conda "${moduleDir}/environment.yml" + container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container ? + 'https://depot.galaxyproject.org/singularity/fq:0.12.0--h9ee0642_0': + 'biocontainers/fq:0.12.0--h9ee0642_0' }" + + input: + tuple val(meta), path(fastq) + + output: + tuple val(meta), path("*.fq_lint.txt"), emit: lint + path "versions.yml" , emit: versions + + when: + task.ext.when == null || task.ext.when + + script: + def args = task.ext.args ?: '' + def prefix = task.ext.prefix ?: "${meta.id}" + """ + fq lint \\ + $args \\ + $fastq > ${prefix}.fq_lint.txt + + cat <<-END_VERSIONS > versions.yml + "${task.process}": + fq: \$(echo \$(fq lint --version | sed 's/fq-lint //g')) + END_VERSIONS + """ +} diff --git a/modules/nf-core/fq/lint/meta.yml b/modules/nf-core/fq/lint/meta.yml new file mode 100644 index 000000000..7240fb579 --- /dev/null +++ b/modules/nf-core/fq/lint/meta.yml @@ -0,0 +1,43 @@ +name: "fq_lint" +description: fq lint is a FASTQ file pair validator. +keywords: + - lint + - fastq + - validate +tools: + - "fq": + description: "fq is a library to generate and validate FASTQ file pairs." + homepage: "https://github.com/stjude-rust-labs/fq" + documentation: "https://github.com/stjude-rust-labs/fq" + tool_dev_url: "https://github.com/stjude-rust-labs/fq" + licence: ["MIT"] + identifier: "" +input: + - - meta: + type: map + description: | + Groovy Map containing sample information + e.g. [ id:'test', single_end:false ] + - fastq: + type: file + description: FASTQ file list + pattern: "*.fastq{,.gz}" +output: + - lint: + - meta: + type: file + description: Lint output + pattern: "*.fq_lint.txt" + - "*.fq_lint.txt": + type: file + description: Lint output + pattern: "*.fq_lint.txt" + - versions: + - versions.yml: + type: file + description: File containing software versions + pattern: "versions.yml" +authors: + - "@adamrtalbot" +maintainers: + - "@adamrtalbot" diff --git a/modules/nf-core/fq/lint/tests/main.nf.test b/modules/nf-core/fq/lint/tests/main.nf.test new file mode 100644 index 000000000..f96a7c21a --- /dev/null +++ b/modules/nf-core/fq/lint/tests/main.nf.test @@ -0,0 +1,58 @@ +nextflow_process { + + name "Test Process FQ_LINT" + script "../main.nf" + process "FQ_LINT" + + test("test_fq_lint_success") { + when { + params { + outdir = "$outputDir" + } + process { + """ + input[0] = [ [ id:'test', single_end:false ], // meta map + [ file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_1.fastq.gz', checkIfExists: true), + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_2.fastq.gz', checkIfExists: true) ] + ] + """ + } + } + + then { + assertAll ( + { assert process.success }, + { assert process.out.lint.get(0).get(1) ==~ ".*/test.fq_lint.txt" }, + { assert path(process.out.lint.get(0).get(1)).getText().contains("fq-lint start") }, + { assert path(process.out.lint.get(0).get(1)).getText().contains("read 100 records") }, + { assert path(process.out.lint.get(0).get(1)).getText().contains("fq-lint end") }, + ) + } + + } + + test("test_fq_lint_fail") { + when { + params { + outdir = "$outputDir" + } + process { + """ + input[0] = [ [ id:'test', single_end:false ], // meta map + [ file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_1.fastq.gz', checkIfExists: true), + file(params.modules_testdata_base_path + 'genomics/prokaryotes/candidatus_portiera_aleyrodidarum/illumina/fastq/test_2.fastq.gz', checkIfExists: true) ] + ] + """ + } + } + + then { + assertAll ( + { assert !process.success }, + { assert snapshot(process.out).match() }, + ) + } + + } + +} diff --git a/modules/nf-core/fq/lint/tests/main.nf.test.snap b/modules/nf-core/fq/lint/tests/main.nf.test.snap new file mode 100644 index 000000000..fec8e5243 --- /dev/null +++ b/modules/nf-core/fq/lint/tests/main.nf.test.snap @@ -0,0 +1,25 @@ +{ + "test_fq_lint_fail": { + "content": [ + { + "0": [ + + ], + "1": [ + + ], + "lint": [ + + ], + "versions": [ + + ] + } + ], + "meta": { + "nf-test": "0.9.0", + "nextflow": "24.04.4" + }, + "timestamp": "2024-10-19T16:37:02.133847389" + } +} \ No newline at end of file From de301d22e9082c1b681edd643447b68e65a74aec Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Mon, 2 Dec 2024 17:35:30 +0000 Subject: [PATCH 033/107] Add output previxes for linting --- .../nextflow.config | 23 +++++++++++++++++++ 1 file changed, 23 insertions(+) create mode 100644 subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/nextflow.config diff --git a/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/nextflow.config b/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/nextflow.config new file mode 100644 index 000000000..4f1d31fea --- /dev/null +++ b/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/nextflow.config @@ -0,0 +1,23 @@ +process { + + withName: 'FQ_LINT_AFTER_TRIMMING' { + ext.prefix = { "${meta.id}.trimmed" } + } + + withName: 'FQ_LINT_AFTER_BBMAP' { + ext.prefix = { "${meta.id}.bbmap" } + } + + withName: 'FQ_LINT_AFTER_SORTMERNA' { + ext.prefix = { "${meta.id}.sortmerna" } + } + + withName: 'FQ_SUBSAMPLE' { + ext.args = '--record-count 1000000 --seed 1' + ext.prefix = { "${meta.id}.subsampled" } + publishDir = [ + enabled: false + ] + } + +} \ No newline at end of file From d5a14a83330b6d67d794f9534a19f9f6e5cb6264 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Tue, 3 Dec 2024 09:14:19 +0000 Subject: [PATCH 034/107] Tweaks --- conf/test.config | 1 + nextflow.config | 1 + .../main.nf | 27 ++++++++++--------- .../nextflow.config | 12 +++------ 4 files changed, 20 insertions(+), 21 deletions(-) diff --git a/conf/test.config b/conf/test.config index 153200306..4495b5f22 100644 --- a/conf/test.config +++ b/conf/test.config @@ -41,6 +41,7 @@ params { skip_bbsplit = false pseudo_aligner = 'salmon' umitools_bc_pattern = 'NNNN' + extra_fqlint_args = "--disable-validator P001" } // When using RSEM, remove warning from STAR whilst building tiny indices diff --git a/nextflow.config b/nextflow.config index d53633d23..2963aeac4 100644 --- a/nextflow.config +++ b/nextflow.config @@ -40,6 +40,7 @@ params { // Linting skip_linting = false + extra_fqlint_args = null // Trimming trimmer = 'trimgalore' diff --git a/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/main.nf b/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/main.nf index f4cc1fa15..7a4041c25 100644 --- a/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/main.nf +++ b/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/main.nf @@ -6,7 +6,7 @@ include { SORTMERNA } from '../../../modules/nf-core/so include { SORTMERNA as SORTMERNA_INDEX } from '../../../modules/nf-core/sortmerna/main' include { FQ_LINT } from '../../../modules/nf-core/fq/lint/main' include { FQ_LINT as FQ_LINT_AFTER_TRIMMING } from '../../../modules/nf-core/fq/lint/main' -include { FQ_LINT as FQ_LINT_AFTER_BBMAP } from '../../../modules/nf-core/fq/lint/main' +include { FQ_LINT as FQ_LINT_AFTER_BBSPLIT } from '../../../modules/nf-core/fq/lint/main' include { FQ_LINT as FQ_LINT_AFTER_SORTMERNA } from '../../../modules/nf-core/fq/lint/main' include { FASTQ_SUBSAMPLE_FQ_SALMON } from '../fastq_subsample_fq_salmon' @@ -120,18 +120,6 @@ workflow FASTQ_QC_TRIM_FILTER_SETSTRANDEDNESS { ch_multiqc_files = Channel.empty() ch_lint_log = Channel.empty() - // - // MODULE: Lint FastQ files - // - if(!skip_linting) { - FQ_LINT ( - ch_reads.map{ meta, fastqs -> [meta, fastqs.flatten()] } - ) - ch_versions = ch_versions.mix(FQ_LINT.out.versions.first()) - ch_lint_log = ch_lint_log.mix(FQ_LINT.out.lint) - ch_reads = ch_reads.join(FQ_LINT.out.lint.map{it[0]}) - } - ch_reads .branch { meta, fastqs -> @@ -154,6 +142,19 @@ workflow FASTQ_QC_TRIM_FILTER_SETSTRANDEDNESS { ch_versions = ch_versions.mix(CAT_FASTQ.out.versions.first()) + // + // MODULE: Lint FastQ files + // + + if(!skip_linting) { + FQ_LINT ( + ch_filtered_reads + ) + ch_versions = ch_versions.mix(FQ_LINT.out.versions.first()) + ch_lint_log = ch_lint_log.mix(FQ_LINT.out.lint) + ch_reads = ch_reads.join(FQ_LINT.out.lint.map{it[0]}) + } + // // SUBWORKFLOW: Read QC, extract UMI and trim adapters with TrimGalore! // diff --git a/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/nextflow.config b/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/nextflow.config index 4f1d31fea..6b2084f81 100644 --- a/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/nextflow.config +++ b/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/nextflow.config @@ -1,5 +1,9 @@ process { + withName: 'FQ_LINT' { + ext.args = { params.extra_fqlint_args ?: '' } + } + withName: 'FQ_LINT_AFTER_TRIMMING' { ext.prefix = { "${meta.id}.trimmed" } } @@ -12,12 +16,4 @@ process { ext.prefix = { "${meta.id}.sortmerna" } } - withName: 'FQ_SUBSAMPLE' { - ext.args = '--record-count 1000000 --seed 1' - ext.prefix = { "${meta.id}.subsampled" } - publishDir = [ - enabled: false - ] - } - } \ No newline at end of file From 380f7604df7ce9b8584ca4e4be40a3d261fb3ffc Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Tue, 3 Dec 2024 09:46:34 +0000 Subject: [PATCH 035/107] Link in new subworkflow config --- workflows/rnaseq/nextflow.config | 1 + 1 file changed, 1 insertion(+) diff --git a/workflows/rnaseq/nextflow.config b/workflows/rnaseq/nextflow.config index 9cbf0cd30..726586d32 100644 --- a/workflows/rnaseq/nextflow.config +++ b/workflows/rnaseq/nextflow.config @@ -19,6 +19,7 @@ includeConfig "../../subworkflows/nf-core/fastq_align_hisat2/nextflow.config" includeConfig "../../subworkflows/nf-core/fastq_fastqc_umitools_fastp/nextflow.config" includeConfig "../../subworkflows/nf-core/fastq_fastqc_umitools_trimgalore/nextflow.config" includeConfig "../../subworkflows/nf-core/fastq_subsample_fq_salmon/nextflow.config" +includeConfig "../../subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/nextflow.config" // // STAR Salmon alignment options From 839e21ee21e376f891c5147f4eba600def96bff2 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Tue, 3 Dec 2024 09:47:12 +0000 Subject: [PATCH 036/107] Let's skip the name validation by default, it seems to be problematic --- conf/test.config | 1 - nextflow.config | 2 +- 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/conf/test.config b/conf/test.config index 4495b5f22..153200306 100644 --- a/conf/test.config +++ b/conf/test.config @@ -41,7 +41,6 @@ params { skip_bbsplit = false pseudo_aligner = 'salmon' umitools_bc_pattern = 'NNNN' - extra_fqlint_args = "--disable-validator P001" } // When using RSEM, remove warning from STAR whilst building tiny indices diff --git a/nextflow.config b/nextflow.config index 2963aeac4..e8fd1f913 100644 --- a/nextflow.config +++ b/nextflow.config @@ -40,7 +40,7 @@ params { // Linting skip_linting = false - extra_fqlint_args = null + extra_fqlint_args = '--disable-validator P001' // Trimming trimmer = 'trimgalore' From 611e22823ce96c858ac481fa97a386f8da618518 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Tue, 3 Dec 2024 10:42:33 +0000 Subject: [PATCH 037/107] Correct config --- .../fastq_qc_trim_filter_setstrandedness/nextflow.config | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/nextflow.config b/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/nextflow.config index 6b2084f81..aa6800b98 100644 --- a/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/nextflow.config +++ b/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/nextflow.config @@ -8,7 +8,7 @@ process { ext.prefix = { "${meta.id}.trimmed" } } - withName: 'FQ_LINT_AFTER_BBMAP' { + withName: 'FQ_LINT_AFTER_BBSPLIT' { ext.prefix = { "${meta.id}.bbmap" } } @@ -16,4 +16,4 @@ process { ext.prefix = { "${meta.id}.sortmerna" } } -} \ No newline at end of file +} From 5e1d4657400f019a8fd8997195f24dcd62ad8e56 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Tue, 3 Dec 2024 11:26:22 +0000 Subject: [PATCH 038/107] separate linting reports by directory rather than prefix --- .../nextflow.config | 23 ++++++++++++++++--- 1 file changed, 20 insertions(+), 3 deletions(-) diff --git a/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/nextflow.config b/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/nextflow.config index aa6800b98..05d9e8f94 100644 --- a/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/nextflow.config +++ b/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/nextflow.config @@ -2,18 +2,35 @@ process { withName: 'FQ_LINT' { ext.args = { params.extra_fqlint_args ?: '' } + publishDir = [ + path: { "${params.outdir}/fq_lint/raw" }, + mode: params.publish_dir_mode, + saveAs: { filename -> filename.equals('versions.yml') ? null : filename } + ] } withName: 'FQ_LINT_AFTER_TRIMMING' { - ext.prefix = { "${meta.id}.trimmed" } + publishDir = [ + path: { "${params.outdir}/fq_lint/trimmed" }, + mode: params.publish_dir_mode, + saveAs: { filename -> filename.equals('versions.yml') ? null : filename } + ] } withName: 'FQ_LINT_AFTER_BBSPLIT' { - ext.prefix = { "${meta.id}.bbmap" } + publishDir = [ + path: { "${params.outdir}/fq_lint/sortmerna" }, + mode: params.publish_dir_mode, + saveAs: { filename -> filename.equals('versions.yml') ? null : filename } + ] } withName: 'FQ_LINT_AFTER_SORTMERNA' { - ext.prefix = { "${meta.id}.sortmerna" } + publishDir = [ + path: { "${params.outdir}/fq_lint/trimmed" }, + mode: params.publish_dir_mode, + saveAs: { filename -> filename.equals('versions.yml') ? null : filename } + ] } } From b178bc0b84301446e52787fa60dc2174feb81c49 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Tue, 3 Dec 2024 11:49:51 +0000 Subject: [PATCH 039/107] update schema --- nextflow_schema.json | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/nextflow_schema.json b/nextflow_schema.json index 18adfa7d2..39dc46d32 100644 --- a/nextflow_schema.json +++ b/nextflow_schema.json @@ -539,6 +539,12 @@ "description": "Additional quality control options.", "default": "", "properties": { + "extra_fqlint_args": { + "type": "string", + "default": "--disable-validator P001", + "description": "Extra arguments to pass to the fq lint command.", + "fa_icon": "far fa-check-square" + }, "deseq2_vst": { "type": "boolean", "description": "Use vst transformation instead of rlog with DESeq2.", From 51a7238b87881b026920223ab3925e136c9d1a25 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Tue, 3 Dec 2024 12:27:48 +0000 Subject: [PATCH 040/107] Add stub to fq lint --- modules/nf-core/fq/lint/main.nf | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/modules/nf-core/fq/lint/main.nf b/modules/nf-core/fq/lint/main.nf index 943314c87..27107b465 100644 --- a/modules/nf-core/fq/lint/main.nf +++ b/modules/nf-core/fq/lint/main.nf @@ -30,4 +30,15 @@ process FQ_LINT { fq: \$(echo \$(fq lint --version | sed 's/fq-lint //g')) END_VERSIONS """ + + stub: + def prefix = task.ext.prefix ?: "${meta.id}" + """ + touch ${prefix}.fq_lint.txt + + cat <<-END_VERSIONS > versions.yml + "${task.process}": + fq: \$(echo \$(fq lint --version | sed 's/fq-lint //g')) + END_VERSIONS + """ } From 25e3f9de00bf42442b2e9d36923ea04cdf68350b Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Tue, 3 Dec 2024 18:14:11 +0000 Subject: [PATCH 041/107] update docs --- docs/output.md | 15 +++++++++++++++ docs/usage.md | 6 ++++++ 2 files changed, 21 insertions(+) diff --git a/docs/output.md b/docs/output.md index ca828e9fc..3203f2e64 100644 --- a/docs/output.md +++ b/docs/output.md @@ -19,6 +19,7 @@ The pipeline is built using [Nextflow](https://www.nextflow.io/) and processes d - [Pipeline overview](#pipeline-overview) - [Preprocessing](#preprocessing) - [cat](#cat) + [fq lint](#fq-lint) - [FastQC](#fastqc) - [UMI-tools extract](#umi-tools-extract) - [TrimGalore](#trimgalore) @@ -73,6 +74,20 @@ The pipeline is built using [Nextflow](https://www.nextflow.io/) and processes d If multiple libraries/runs have been provided for the same sample in the input samplesheet (e.g. to increase sequencing depth) then these will be merged at the very beginning of the pipeline in order to have consistent sample naming throughout the pipeline. Please refer to the [usage documentation](https://nf-co.re/rnaseq/usage#samplesheet-input) to see how to specify these samples in the input samplesheet. +# fq lint + +
    +Output files + +- `fq_lint/*` + - `*.fq_lint.txt`: Linting report per library from `fq lint`. + +> **NB:** You will see subdirectories here based on the stage of preprocessing for the files that have been linted, for example `raw`, `trimmed`. + +
    + +[fq lint](https://github.com/stjude-rust-labs/fq) runs several checks on input FASTQ files. It will fail with a non-zero error code when issues are found, which will terminate the workflow execution. In the absence of this, the successful linting produces the logs you will find here. + ### FastQC
    diff --git a/docs/usage.md b/docs/usage.md index 851e20c7d..48cafce85 100644 --- a/docs/usage.md +++ b/docs/usage.md @@ -27,6 +27,12 @@ CONTROL_REP1,AEG588A1_S1_L003_R1_001.fastq.gz,AEG588A1_S1_L003_R2_001.fastq.gz,a CONTROL_REP1,AEG588A1_S1_L004_R1_001.fastq.gz,AEG588A1_S1_L004_R2_001.fastq.gz,auto ``` +### Linting + +By default, the pipeline will run [fq lint](https://github.com/stjude-rust-labs/fq) on all input FASTQ files, both at the start of preprocessing and after each preprocessing step that manipulates FASTQ files. If errors are found, and error will be reported and the workflow will stop. + +The `extra_fqlint_args` parameter can be manipulated to disable [any validator](https://github.com/stjude-rust-labs/fq?tab=readme-ov-file#validators) from `fq` you wish. For example, we have found that checks on the names of paired reads are prone to failure, so that check is disabled by default (setting `extra_fqlint_args` to `--disable-validator P001`). + ### Strandedness Prediction If you set the strandedness value to `auto`, the pipeline will sub-sample the input FastQ files to 1 million reads, use Salmon Quant to automatically infer the strandedness, and then propagate this information through the rest of the pipeline. This behavior is controlled by the `--stranded_threshold` and `--unstranded_threshold` parameters, which are set to 0.8 and 0.1 by default, respectively. This means: From 97f241a3f2c549aa90abaa09e5e12f1378746840 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Tue, 3 Dec 2024 18:39:33 +0000 Subject: [PATCH 042/107] update snaps --- tests/default.nf.test.snap | 44 ++++++++++++++++++--- tests/featurecounts_group_type.nf.test.snap | 44 ++++++++++++++++++--- tests/hisat2.nf.test.snap | 44 ++++++++++++++++++--- tests/kallisto.nf.test.snap | 44 ++++++++++++++++++--- tests/min_mapped_reads.nf.test.snap | 44 ++++++++++++++++++--- tests/remove_ribo_rna.nf.test.snap | 44 ++++++++++++++++++--- tests/skip_qc.nf.test.snap | 44 ++++++++++++++++++--- tests/skip_trimming.nf.test.snap | 22 +++++++++-- tests/star_rsem.nf.test.snap | 44 ++++++++++++++++++--- 9 files changed, 323 insertions(+), 51 deletions(-) diff --git a/tests/default.nf.test.snap b/tests/default.nf.test.snap index 4165436ac..d8787c7df 100644 --- a/tests/default.nf.test.snap +++ b/tests/default.nf.test.snap @@ -1,7 +1,7 @@ { "Params: default - stub": { "content": [ - 22, + 27, { "BBMAP_BBSPLIT": { "bbmap": 39.1 @@ -18,6 +18,9 @@ "FASTQC": { "fastqc": "0.12.1" }, + "FQ_LINT": { + "fq": "0.12.0 (2024-07-08)" + }, "GTF2BED": { "perl": "5.26.2" }, @@ -64,6 +67,13 @@ "fastqc/raw/WT_REP2_raw.html", "fastqc/raw/WT_REP2_raw.zip", "fastqc/trim", + "fq_lint", + "fq_lint/raw", + "fq_lint/raw/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/raw/WT_REP1.fq_lint.txt", + "fq_lint/raw/WT_REP2.fq_lint.txt", "multiqc", "multiqc/star_salmon", "multiqc/star_salmon/multiqc_data", @@ -88,13 +98,13 @@ ], "meta": { "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nextflow": "24.10.2" }, - "timestamp": "2024-10-23T10:43:40.33664602" + "timestamp": "2024-12-03T12:26:32.825018567" }, "Params: default": { "content": [ - 198, + 213, { "BBMAP_BBSPLIT": { "bbmap": 39.1 @@ -128,6 +138,9 @@ "FASTQC": { "fastqc": "0.12.1" }, + "FQ_LINT": { + "fq": "0.12.0 (2024-07-08)" + }, "FQ_SUBSAMPLE": { "fq": "0.12.0 (2024-07-08)" }, @@ -276,6 +289,25 @@ "fastqc/trim/WT_REP2_trimmed_1_val_1_fastqc.zip", "fastqc/trim/WT_REP2_trimmed_2_val_2_fastqc.html", "fastqc/trim/WT_REP2_trimmed_2_val_2_fastqc.zip", + "fq_lint", + "fq_lint/raw", + "fq_lint/raw/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/raw/WT_REP1.fq_lint.txt", + "fq_lint/raw/WT_REP2.fq_lint.txt", + "fq_lint/sortmerna", + "fq_lint/sortmerna/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/sortmerna/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/sortmerna/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/sortmerna/WT_REP1.fq_lint.txt", + "fq_lint/sortmerna/WT_REP2.fq_lint.txt", + "fq_lint/trimmed", + "fq_lint/trimmed/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/trimmed/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/trimmed/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/trimmed/WT_REP1.fq_lint.txt", + "fq_lint/trimmed/WT_REP2.fq_lint.txt", "multiqc", "multiqc/star_salmon", "multiqc/star_salmon/multiqc_report.html", @@ -1457,8 +1489,8 @@ ], "meta": { "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nextflow": "24.10.2" }, - "timestamp": "2024-10-23T10:42:45.202573855" + "timestamp": "2024-12-03T12:39:04.406597796" } } \ No newline at end of file diff --git a/tests/featurecounts_group_type.nf.test.snap b/tests/featurecounts_group_type.nf.test.snap index dfd3f3d2c..ac1369ce2 100644 --- a/tests/featurecounts_group_type.nf.test.snap +++ b/tests/featurecounts_group_type.nf.test.snap @@ -1,7 +1,7 @@ { "Params: --featurecounts_group_type false - stub": { "content": [ - 22, + 27, { "BBMAP_BBSPLIT": { "bbmap": 39.1 @@ -18,6 +18,9 @@ "FASTQC": { "fastqc": "0.12.1" }, + "FQ_LINT": { + "fq": "0.12.0 (2024-07-08)" + }, "GTF2BED": { "perl": "5.26.2" }, @@ -64,6 +67,13 @@ "fastqc/raw/WT_REP2_raw.html", "fastqc/raw/WT_REP2_raw.zip", "fastqc/trim", + "fq_lint", + "fq_lint/raw", + "fq_lint/raw/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/raw/WT_REP1.fq_lint.txt", + "fq_lint/raw/WT_REP2.fq_lint.txt", "multiqc", "multiqc/star_salmon", "multiqc/star_salmon/multiqc_data", @@ -88,13 +98,13 @@ ], "meta": { "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nextflow": "24.10.2" }, - "timestamp": "2024-10-23T10:54:29.62339584" + "timestamp": "2024-12-03T17:26:42.742119846" }, "Params: --featurecounts_group_type false": { "content": [ - 188, + 203, { "BBMAP_BBSPLIT": { "bbmap": 39.1 @@ -128,6 +138,9 @@ "FASTQC": { "fastqc": "0.12.1" }, + "FQ_LINT": { + "fq": "0.12.0 (2024-07-08)" + }, "FQ_SUBSAMPLE": { "fq": "0.12.0 (2024-07-08)" }, @@ -270,6 +283,25 @@ "fastqc/trim/WT_REP2_trimmed_1_val_1_fastqc.zip", "fastqc/trim/WT_REP2_trimmed_2_val_2_fastqc.html", "fastqc/trim/WT_REP2_trimmed_2_val_2_fastqc.zip", + "fq_lint", + "fq_lint/raw", + "fq_lint/raw/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/raw/WT_REP1.fq_lint.txt", + "fq_lint/raw/WT_REP2.fq_lint.txt", + "fq_lint/sortmerna", + "fq_lint/sortmerna/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/sortmerna/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/sortmerna/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/sortmerna/WT_REP1.fq_lint.txt", + "fq_lint/sortmerna/WT_REP2.fq_lint.txt", + "fq_lint/trimmed", + "fq_lint/trimmed/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/trimmed/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/trimmed/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/trimmed/WT_REP1.fq_lint.txt", + "fq_lint/trimmed/WT_REP2.fq_lint.txt", "multiqc", "multiqc/star_salmon", "multiqc/star_salmon/multiqc_report.html", @@ -1407,8 +1439,8 @@ ], "meta": { "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nextflow": "24.10.2" }, - "timestamp": "2024-10-23T10:53:28.969150716" + "timestamp": "2024-12-03T17:25:47.179044158" } } \ No newline at end of file diff --git a/tests/hisat2.nf.test.snap b/tests/hisat2.nf.test.snap index 4b39b7703..c7f66af3f 100644 --- a/tests/hisat2.nf.test.snap +++ b/tests/hisat2.nf.test.snap @@ -1,7 +1,7 @@ { "Params: --aligner hisat2 - stub": { "content": [ - 23, + 28, { "BBMAP_BBSPLIT": { "bbmap": 39.1 @@ -18,6 +18,9 @@ "FASTQC": { "fastqc": "0.12.1" }, + "FQ_LINT": { + "fq": "0.12.0 (2024-07-08)" + }, "GTF2BED": { "perl": "5.26.2" }, @@ -65,6 +68,13 @@ "fastqc/raw/WT_REP2_raw.html", "fastqc/raw/WT_REP2_raw.zip", "fastqc/trim", + "fq_lint", + "fq_lint/raw", + "fq_lint/raw/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/raw/WT_REP1.fq_lint.txt", + "fq_lint/raw/WT_REP2.fq_lint.txt", "multiqc", "multiqc/hisat2", "multiqc/hisat2/multiqc_data", @@ -89,13 +99,13 @@ ], "meta": { "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nextflow": "24.10.2" }, - "timestamp": "2024-10-23T11:01:31.33505759" + "timestamp": "2024-12-03T17:39:53.585492305" }, "Params: --aligner hisat2": { "content": [ - 187, + 202, { "BBMAP_BBSPLIT": { "bbmap": 39.1 @@ -125,6 +135,9 @@ "FASTQC": { "fastqc": "0.12.1" }, + "FQ_LINT": { + "fq": "0.12.0 (2024-07-08)" + }, "FQ_SUBSAMPLE": { "fq": "0.12.0 (2024-07-08)" }, @@ -273,6 +286,25 @@ "fastqc/trim/WT_REP2_trimmed_1_val_1_fastqc.zip", "fastqc/trim/WT_REP2_trimmed_2_val_2_fastqc.html", "fastqc/trim/WT_REP2_trimmed_2_val_2_fastqc.zip", + "fq_lint", + "fq_lint/raw", + "fq_lint/raw/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/raw/WT_REP1.fq_lint.txt", + "fq_lint/raw/WT_REP2.fq_lint.txt", + "fq_lint/sortmerna", + "fq_lint/sortmerna/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/sortmerna/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/sortmerna/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/sortmerna/WT_REP1.fq_lint.txt", + "fq_lint/sortmerna/WT_REP2.fq_lint.txt", + "fq_lint/trimmed", + "fq_lint/trimmed/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/trimmed/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/trimmed/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/trimmed/WT_REP1.fq_lint.txt", + "fq_lint/trimmed/WT_REP2.fq_lint.txt", "hisat2", "hisat2/RAP1_IAA_30M_REP1.markdup.sorted.bam", "hisat2/RAP1_IAA_30M_REP1.markdup.sorted.bam.bai", @@ -1307,8 +1339,8 @@ ], "meta": { "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nextflow": "24.10.2" }, - "timestamp": "2024-10-23T11:00:43.422573373" + "timestamp": "2024-12-03T17:38:50.917761241" } } \ No newline at end of file diff --git a/tests/kallisto.nf.test.snap b/tests/kallisto.nf.test.snap index 8bd57f925..ac276eefd 100644 --- a/tests/kallisto.nf.test.snap +++ b/tests/kallisto.nf.test.snap @@ -1,7 +1,7 @@ { "Params: --pseudo_aligner kallisto --skip_qc --skip_alignment": { "content": [ - 35, + 50, { "BBMAP_BBSPLIT": { "bbmap": 39.1 @@ -18,6 +18,9 @@ "CUSTOM_TX2GENE": { "python": "3.10.4" }, + "FQ_LINT": { + "fq": "0.12.0 (2024-07-08)" + }, "FQ_SUBSAMPLE": { "fq": "0.12.0 (2024-07-08)" }, @@ -88,6 +91,25 @@ "fastqc/trim/WT_REP2_trimmed_1_val_1_fastqc.zip", "fastqc/trim/WT_REP2_trimmed_2_val_2_fastqc.html", "fastqc/trim/WT_REP2_trimmed_2_val_2_fastqc.zip", + "fq_lint", + "fq_lint/raw", + "fq_lint/raw/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/raw/WT_REP1.fq_lint.txt", + "fq_lint/raw/WT_REP2.fq_lint.txt", + "fq_lint/sortmerna", + "fq_lint/sortmerna/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/sortmerna/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/sortmerna/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/sortmerna/WT_REP1.fq_lint.txt", + "fq_lint/sortmerna/WT_REP2.fq_lint.txt", + "fq_lint/trimmed", + "fq_lint/trimmed/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/trimmed/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/trimmed/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/trimmed/WT_REP1.fq_lint.txt", + "fq_lint/trimmed/WT_REP2.fq_lint.txt", "kallisto", "kallisto/RAP1_IAA_30M_REP1", "kallisto/RAP1_IAA_30M_REP1/abundance.h5", @@ -251,13 +273,13 @@ ], "meta": { "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nextflow": "24.10.2" }, - "timestamp": "2024-10-23T11:04:57.579312846" + "timestamp": "2024-12-03T17:48:16.536464395" }, "Params: --pseudo_aligner kallisto --skip_qc --skip_alignment - stub": { "content": [ - 17, + 22, { "BBMAP_BBSPLIT": { "bbmap": 39.1 @@ -271,6 +293,9 @@ "CUSTOM_GETCHROMSIZES": { "getchromsizes": 1.21 }, + "FQ_LINT": { + "fq": "0.12.0 (2024-07-08)" + }, "GTF2BED": { "perl": "5.26.2" }, @@ -304,6 +329,13 @@ "custom/out/genome_transcriptome.gtf", "fastqc", "fastqc/trim", + "fq_lint", + "fq_lint/raw", + "fq_lint/raw/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/raw/WT_REP1.fq_lint.txt", + "fq_lint/raw/WT_REP2.fq_lint.txt", "multiqc", "multiqc/multiqc_data", "multiqc/multiqc_plots", @@ -327,8 +359,8 @@ ], "meta": { "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nextflow": "24.10.2" }, - "timestamp": "2024-10-23T11:05:54.052864085" + "timestamp": "2024-12-03T17:49:02.306667165" } } \ No newline at end of file diff --git a/tests/min_mapped_reads.nf.test.snap b/tests/min_mapped_reads.nf.test.snap index bc6454c98..c2d9df5a9 100644 --- a/tests/min_mapped_reads.nf.test.snap +++ b/tests/min_mapped_reads.nf.test.snap @@ -1,7 +1,7 @@ { "Params: --min_mapped_reads 90": { "content": [ - 152, + 167, { "BBMAP_BBSPLIT": { "bbmap": 39.1 @@ -35,6 +35,9 @@ "FASTQC": { "fastqc": "0.12.1" }, + "FQ_LINT": { + "fq": "0.12.0 (2024-07-08)" + }, "FQ_SUBSAMPLE": { "fq": "0.12.0 (2024-07-08)" }, @@ -183,6 +186,25 @@ "fastqc/trim/WT_REP2_trimmed_1_val_1_fastqc.zip", "fastqc/trim/WT_REP2_trimmed_2_val_2_fastqc.html", "fastqc/trim/WT_REP2_trimmed_2_val_2_fastqc.zip", + "fq_lint", + "fq_lint/raw", + "fq_lint/raw/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/raw/WT_REP1.fq_lint.txt", + "fq_lint/raw/WT_REP2.fq_lint.txt", + "fq_lint/sortmerna", + "fq_lint/sortmerna/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/sortmerna/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/sortmerna/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/sortmerna/WT_REP1.fq_lint.txt", + "fq_lint/sortmerna/WT_REP2.fq_lint.txt", + "fq_lint/trimmed", + "fq_lint/trimmed/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/trimmed/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/trimmed/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/trimmed/WT_REP1.fq_lint.txt", + "fq_lint/trimmed/WT_REP2.fq_lint.txt", "multiqc", "multiqc/star_salmon", "multiqc/star_salmon/multiqc_report.html", @@ -1173,13 +1195,13 @@ ], "meta": { "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nextflow": "24.10.2" }, - "timestamp": "2024-10-23T11:18:04.553985917" + "timestamp": "2024-12-03T17:57:05.836924025" }, "Params: --min_mapped_reads 90 - stub": { "content": [ - 22, + 27, { "BBMAP_BBSPLIT": { "bbmap": 39.1 @@ -1196,6 +1218,9 @@ "FASTQC": { "fastqc": "0.12.1" }, + "FQ_LINT": { + "fq": "0.12.0 (2024-07-08)" + }, "GTF2BED": { "perl": "5.26.2" }, @@ -1242,6 +1267,13 @@ "fastqc/raw/WT_REP2_raw.html", "fastqc/raw/WT_REP2_raw.zip", "fastqc/trim", + "fq_lint", + "fq_lint/raw", + "fq_lint/raw/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/raw/WT_REP1.fq_lint.txt", + "fq_lint/raw/WT_REP2.fq_lint.txt", "multiqc", "multiqc/star_salmon", "multiqc/star_salmon/multiqc_data", @@ -1266,8 +1298,8 @@ ], "meta": { "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nextflow": "24.10.2" }, - "timestamp": "2024-10-23T11:19:22.443482528" + "timestamp": "2024-12-03T17:57:59.273391037" } } \ No newline at end of file diff --git a/tests/remove_ribo_rna.nf.test.snap b/tests/remove_ribo_rna.nf.test.snap index 0a379aea9..6131451ee 100644 --- a/tests/remove_ribo_rna.nf.test.snap +++ b/tests/remove_ribo_rna.nf.test.snap @@ -1,7 +1,7 @@ { "Params: --remove_ribo_rna": { "content": [ - 205, + 225, { "BBMAP_BBSPLIT": { "bbmap": 39.1 @@ -35,6 +35,9 @@ "FASTQC": { "fastqc": "0.12.1" }, + "FQ_LINT": { + "fq": "0.12.0 (2024-07-08)" + }, "FQ_SUBSAMPLE": { "fq": "0.12.0 (2024-07-08)" }, @@ -189,6 +192,25 @@ "fastqc/trim/WT_REP2_trimmed_1_val_1_fastqc.zip", "fastqc/trim/WT_REP2_trimmed_2_val_2_fastqc.html", "fastqc/trim/WT_REP2_trimmed_2_val_2_fastqc.zip", + "fq_lint", + "fq_lint/raw", + "fq_lint/raw/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/raw/WT_REP1.fq_lint.txt", + "fq_lint/raw/WT_REP2.fq_lint.txt", + "fq_lint/sortmerna", + "fq_lint/sortmerna/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/sortmerna/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/sortmerna/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/sortmerna/WT_REP1.fq_lint.txt", + "fq_lint/sortmerna/WT_REP2.fq_lint.txt", + "fq_lint/trimmed", + "fq_lint/trimmed/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/trimmed/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/trimmed/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/trimmed/WT_REP1.fq_lint.txt", + "fq_lint/trimmed/WT_REP2.fq_lint.txt", "multiqc", "multiqc/star_salmon", "multiqc/star_salmon/multiqc_report.html", @@ -1384,13 +1406,13 @@ ], "meta": { "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nextflow": "24.10.2" }, - "timestamp": "2024-10-23T11:36:09.902195192" + "timestamp": "2024-12-03T18:13:03.469655405" }, "Params: --remove_ribo_rna - stub": { "content": [ - 24, + 29, { "BBMAP_BBSPLIT": { "bbmap": 39.1 @@ -1407,6 +1429,9 @@ "FASTQC": { "fastqc": "0.12.1" }, + "FQ_LINT": { + "fq": "0.12.0 (2024-07-08)" + }, "GTF2BED": { "perl": "5.26.2" }, @@ -1456,6 +1481,13 @@ "fastqc/raw/WT_REP2_raw.html", "fastqc/raw/WT_REP2_raw.zip", "fastqc/trim", + "fq_lint", + "fq_lint/raw", + "fq_lint/raw/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/raw/WT_REP1.fq_lint.txt", + "fq_lint/raw/WT_REP2.fq_lint.txt", "multiqc", "multiqc/star_salmon", "multiqc/star_salmon/multiqc_data", @@ -1480,8 +1512,8 @@ ], "meta": { "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nextflow": "24.10.2" }, - "timestamp": "2024-10-23T11:37:11.90905171" + "timestamp": "2024-12-03T18:13:56.979730403" } } \ No newline at end of file diff --git a/tests/skip_qc.nf.test.snap b/tests/skip_qc.nf.test.snap index a07a6dd43..6cd87efb9 100644 --- a/tests/skip_qc.nf.test.snap +++ b/tests/skip_qc.nf.test.snap @@ -1,7 +1,7 @@ { "Params: --skip_qc - stub": { "content": [ - 17, + 22, { "BBMAP_BBSPLIT": { "bbmap": 39.1 @@ -15,6 +15,9 @@ "CUSTOM_GETCHROMSIZES": { "getchromsizes": 1.21 }, + "FQ_LINT": { + "fq": "0.12.0 (2024-07-08)" + }, "GTF2BED": { "perl": "5.26.2" }, @@ -50,6 +53,13 @@ "custom/out/genome_transcriptome.gtf", "fastqc", "fastqc/trim", + "fq_lint", + "fq_lint/raw", + "fq_lint/raw/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/raw/WT_REP1.fq_lint.txt", + "fq_lint/raw/WT_REP2.fq_lint.txt", "multiqc", "multiqc/star_salmon", "multiqc/star_salmon/multiqc_data", @@ -74,13 +84,13 @@ ], "meta": { "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nextflow": "24.10.2" }, - "timestamp": "2024-10-23T11:50:42.323505833" + "timestamp": "2024-12-03T18:20:52.010478721" }, "Params: --skip_qc": { "content": [ - 136, + 151, { "BBMAP_BBSPLIT": { "bbmap": 39.1 @@ -100,6 +110,9 @@ "CUSTOM_TX2GENE": { "python": "3.10.4" }, + "FQ_LINT": { + "fq": "0.12.0 (2024-07-08)" + }, "FQ_SUBSAMPLE": { "fq": "0.12.0 (2024-07-08)" }, @@ -201,6 +214,25 @@ "fastqc/trim/WT_REP2_trimmed_1_val_1_fastqc.zip", "fastqc/trim/WT_REP2_trimmed_2_val_2_fastqc.html", "fastqc/trim/WT_REP2_trimmed_2_val_2_fastqc.zip", + "fq_lint", + "fq_lint/raw", + "fq_lint/raw/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/raw/WT_REP1.fq_lint.txt", + "fq_lint/raw/WT_REP2.fq_lint.txt", + "fq_lint/sortmerna", + "fq_lint/sortmerna/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/sortmerna/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/sortmerna/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/sortmerna/WT_REP1.fq_lint.txt", + "fq_lint/sortmerna/WT_REP2.fq_lint.txt", + "fq_lint/trimmed", + "fq_lint/trimmed/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/trimmed/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/trimmed/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/trimmed/WT_REP1.fq_lint.txt", + "fq_lint/trimmed/WT_REP2.fq_lint.txt", "multiqc", "multiqc/star_salmon", "multiqc/star_salmon/multiqc_report.html", @@ -785,8 +817,8 @@ ], "meta": { "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nextflow": "24.10.2" }, - "timestamp": "2024-10-23T11:49:49.131172185" + "timestamp": "2024-12-03T18:20:07.800214285" } } \ No newline at end of file diff --git a/tests/skip_trimming.nf.test.snap b/tests/skip_trimming.nf.test.snap index a0bfa97e4..0f728a3b6 100644 --- a/tests/skip_trimming.nf.test.snap +++ b/tests/skip_trimming.nf.test.snap @@ -1,7 +1,7 @@ { "Params: --skip_trimming": { "content": [ - 193, + 203, { "BBMAP_BBSPLIT": { "bbmap": 39.1 @@ -35,6 +35,9 @@ "FASTQC": { "fastqc": "0.12.1" }, + "FQ_LINT": { + "fq": "0.12.0 (2024-07-08)" + }, "FQ_SUBSAMPLE": { "fq": "0.12.0 (2024-07-08)" }, @@ -162,6 +165,19 @@ "fastqc/raw/WT_REP2_raw_1_fastqc.zip", "fastqc/raw/WT_REP2_raw_2_fastqc.html", "fastqc/raw/WT_REP2_raw_2_fastqc.zip", + "fq_lint", + "fq_lint/raw", + "fq_lint/raw/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/raw/WT_REP1.fq_lint.txt", + "fq_lint/raw/WT_REP2.fq_lint.txt", + "fq_lint/sortmerna", + "fq_lint/sortmerna/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/sortmerna/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/sortmerna/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/sortmerna/WT_REP1.fq_lint.txt", + "fq_lint/sortmerna/WT_REP2.fq_lint.txt", "multiqc", "multiqc/star_salmon", "multiqc/star_salmon/multiqc_report.html", @@ -1255,8 +1271,8 @@ ], "meta": { "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nextflow": "24.10.2" }, - "timestamp": "2024-10-21T20:23:07.88998048" + "timestamp": "2024-12-03T18:28:59.906095781" } } \ No newline at end of file diff --git a/tests/star_rsem.nf.test.snap b/tests/star_rsem.nf.test.snap index 31e9150d8..9e95dad0f 100644 --- a/tests/star_rsem.nf.test.snap +++ b/tests/star_rsem.nf.test.snap @@ -1,7 +1,7 @@ { "Params: --aligner star_rsem": { "content": [ - 188, + 203, { "BBMAP_BBSPLIT": { "bbmap": 39.1 @@ -35,6 +35,9 @@ "FASTQC": { "fastqc": "0.12.1" }, + "FQ_LINT": { + "fq": "0.12.0 (2024-07-08)" + }, "FQ_SUBSAMPLE": { "fq": "0.12.0 (2024-07-08)" }, @@ -183,6 +186,25 @@ "fastqc/trim/WT_REP2_trimmed_1_val_1_fastqc.zip", "fastqc/trim/WT_REP2_trimmed_2_val_2_fastqc.html", "fastqc/trim/WT_REP2_trimmed_2_val_2_fastqc.zip", + "fq_lint", + "fq_lint/raw", + "fq_lint/raw/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/raw/WT_REP1.fq_lint.txt", + "fq_lint/raw/WT_REP2.fq_lint.txt", + "fq_lint/sortmerna", + "fq_lint/sortmerna/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/sortmerna/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/sortmerna/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/sortmerna/WT_REP1.fq_lint.txt", + "fq_lint/sortmerna/WT_REP2.fq_lint.txt", + "fq_lint/trimmed", + "fq_lint/trimmed/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/trimmed/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/trimmed/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/trimmed/WT_REP1.fq_lint.txt", + "fq_lint/trimmed/WT_REP2.fq_lint.txt", "multiqc", "multiqc/star_rsem", "multiqc/star_rsem/multiqc_report.html", @@ -1268,13 +1290,13 @@ ], "meta": { "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nextflow": "24.10.2" }, - "timestamp": "2024-10-23T12:16:02.205058346" + "timestamp": "2024-12-03T18:37:23.242230621" }, "Params: --aligner star_rsem - stub": { "content": [ - 22, + 27, { "BBMAP_BBSPLIT": { "bbmap": 39.1 @@ -1291,6 +1313,9 @@ "FASTQC": { "fastqc": "0.12.1" }, + "FQ_LINT": { + "fq": "0.12.0 (2024-07-08)" + }, "GTF2BED": { "perl": "5.26.2" }, @@ -1335,6 +1360,13 @@ "fastqc/raw/WT_REP2_raw.html", "fastqc/raw/WT_REP2_raw.zip", "fastqc/trim", + "fq_lint", + "fq_lint/raw", + "fq_lint/raw/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/raw/WT_REP1.fq_lint.txt", + "fq_lint/raw/WT_REP2.fq_lint.txt", "multiqc", "multiqc/star_rsem", "multiqc/star_rsem/multiqc_data", @@ -1359,8 +1391,8 @@ ], "meta": { "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nextflow": "24.10.2" }, - "timestamp": "2024-10-23T12:17:33.292700445" + "timestamp": "2024-12-03T18:38:21.04782292" } } \ No newline at end of file From ae4203232805cfdb2a7d1c2347c8944e74525c58 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Tue, 3 Dec 2024 18:40:41 +0000 Subject: [PATCH 043/107] exclude lint reports from snapshots --- tests/.nftignore | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/.nftignore b/tests/.nftignore index 6d2f0b8a8..a01770b1e 100644 --- a/tests/.nftignore +++ b/tests/.nftignore @@ -1,3 +1,4 @@ +fq_lint/*/*.fq_lint.txt bbsplit/*.stats.txt fastqc/*/*.{html,zip} hisat2/log/*.hisat2.summary.log From 83967a746f7ba207b25e8bf0d25eef3c73ef8d81 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Tue, 3 Dec 2024 18:41:03 +0000 Subject: [PATCH 044/107] update module/ subworkflow --- modules.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules.json b/modules.json index 7c45311e1..d03e252c6 100644 --- a/modules.json +++ b/modules.json @@ -59,7 +59,7 @@ }, "fq/lint": { "branch": "master", - "git_sha": "34774b3e29b5cdb70c1e08ac7860765b795648e2", + "git_sha": "2c0260ed80daeca9c6dfa477a4daf04ff336dc37", "installed_by": ["fastq_qc_trim_filter_setstrandedness", "modules"] }, "fq/subsample": { @@ -346,7 +346,7 @@ }, "fastq_qc_trim_filter_setstrandedness": { "branch": "master", - "git_sha": "34774b3e29b5cdb70c1e08ac7860765b795648e2", + "git_sha": "2c0260ed80daeca9c6dfa477a4daf04ff336dc37", "installed_by": ["subworkflows"] }, "fastq_subsample_fq_salmon": { From e69971ad7f76db8e12bd28dca094c9a58311a325 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Tue, 3 Dec 2024 18:48:47 +0000 Subject: [PATCH 045/107] Add CI Java fix --- .github/workflows/ci.yml | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 2f4646244..7a620df22 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -68,6 +68,11 @@ jobs: - name: Check out pipeline code uses: actions/checkout@0ad4b8fadaa221de15dcec353f45205ec38ea70b # v4 + - uses: actions/setup-java@8df1039502a15bceb9433410b1a100fbe190c53b # v4 + with: + distribution: "temurin" + java-version: "17" + - name: Set up Nextflow uses: nf-core/setup-nextflow@v2 with: From 6a6e946af4162197cf0445afa201ee2ef762aaf8 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Tue, 3 Dec 2024 18:50:17 +0000 Subject: [PATCH 046/107] Fix java ci issue --- .github/workflows/ci.yml | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 2f4646244..7a620df22 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -68,6 +68,11 @@ jobs: - name: Check out pipeline code uses: actions/checkout@0ad4b8fadaa221de15dcec353f45205ec38ea70b # v4 + - uses: actions/setup-java@8df1039502a15bceb9433410b1a100fbe190c53b # v4 + with: + distribution: "temurin" + java-version: "17" + - name: Set up Nextflow uses: nf-core/setup-nextflow@v2 with: From 376296b7520856fbef089f60db53a77069e4f0d9 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Tue, 3 Dec 2024 19:36:49 +0000 Subject: [PATCH 047/107] Fix subworkflow test --- tests/nextflow.config | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/tests/nextflow.config b/tests/nextflow.config index 86b7ac5b4..942f65737 100644 --- a/tests/nextflow.config +++ b/tests/nextflow.config @@ -25,6 +25,10 @@ process { memory = 3.GB time = 2.h } + + withName: 'FASTQ_QC_TRIM_FILTER_SETSTRANDEDNESS:FQ_LINT_AFTER_SORTMERNA' { + ext.args = null + } } // Impose same minimum Nextflow version as the pipeline for testing From 2cf53abd9bbe751036844fd9a2e6d90bdcbd04b0 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Tue, 3 Dec 2024 19:48:18 +0000 Subject: [PATCH 048/107] Fix salmon test --- tests/salmon.nf.test.snap | 44 +++++++++++++++++++++++++++++++++------ 1 file changed, 38 insertions(+), 6 deletions(-) diff --git a/tests/salmon.nf.test.snap b/tests/salmon.nf.test.snap index 1e6827546..2ae620ba3 100644 --- a/tests/salmon.nf.test.snap +++ b/tests/salmon.nf.test.snap @@ -1,7 +1,7 @@ { "Params: --pseudo_aligner salmon --skip_qc --skip_alignment": { "content": [ - 34, + 49, { "BBMAP_BBSPLIT": { "bbmap": 39.1 @@ -18,6 +18,9 @@ "CUSTOM_TX2GENE": { "python": "3.10.4" }, + "FQ_LINT": { + "fq": "0.12.0 (2024-07-08)" + }, "FQ_SUBSAMPLE": { "fq": "0.12.0 (2024-07-08)" }, @@ -82,6 +85,25 @@ "fastqc/trim/WT_REP2_trimmed_1_val_1_fastqc.zip", "fastqc/trim/WT_REP2_trimmed_2_val_2_fastqc.html", "fastqc/trim/WT_REP2_trimmed_2_val_2_fastqc.zip", + "fq_lint", + "fq_lint/raw", + "fq_lint/raw/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/raw/WT_REP1.fq_lint.txt", + "fq_lint/raw/WT_REP2.fq_lint.txt", + "fq_lint/sortmerna", + "fq_lint/sortmerna/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/sortmerna/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/sortmerna/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/sortmerna/WT_REP1.fq_lint.txt", + "fq_lint/sortmerna/WT_REP2.fq_lint.txt", + "fq_lint/trimmed", + "fq_lint/trimmed/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/trimmed/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/trimmed/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/trimmed/WT_REP1.fq_lint.txt", + "fq_lint/trimmed/WT_REP2.fq_lint.txt", "multiqc", "multiqc/multiqc_report.html", "multiqc/multiqc_report_data", @@ -327,13 +349,13 @@ ], "meta": { "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nextflow": "24.10.2" }, - "timestamp": "2024-10-23T11:41:47.859384178" + "timestamp": "2024-12-03T19:45:33.529844191" }, "Params: --pseudo_aligner salmon --skip_qc --skip_alignment - stub": { "content": [ - 16, + 21, { "BBMAP_BBSPLIT": { "bbmap": 39.1 @@ -347,6 +369,9 @@ "CUSTOM_GETCHROMSIZES": { "getchromsizes": 1.21 }, + "FQ_LINT": { + "fq": "0.12.0 (2024-07-08)" + }, "GTF2BED": { "perl": "5.26.2" }, @@ -377,6 +402,13 @@ "custom/out/genome_transcriptome.gtf", "fastqc", "fastqc/trim", + "fq_lint", + "fq_lint/raw", + "fq_lint/raw/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/raw/WT_REP1.fq_lint.txt", + "fq_lint/raw/WT_REP2.fq_lint.txt", "multiqc", "multiqc/multiqc_data", "multiqc/multiqc_plots", @@ -400,8 +432,8 @@ ], "meta": { "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nextflow": "24.10.2" }, - "timestamp": "2024-10-23T11:42:36.34114827" + "timestamp": "2024-12-03T19:46:22.63337267" } } \ No newline at end of file From 7ec665d48fcd08633bf86ab61f99f1d50910fa13 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Tue, 3 Dec 2024 20:36:43 +0000 Subject: [PATCH 049/107] fix changelog and versions --- CHANGELOG.md | 13 ++++++++++++- nextflow.config | 2 +- tests/default.nf.test.snap | 4 ++-- tests/featurecounts_group_type.nf.test.snap | 4 ++-- tests/hisat2.nf.test.snap | 4 ++-- tests/kallisto.nf.test.snap | 4 ++-- tests/min_mapped_reads.nf.test.snap | 4 ++-- tests/remove_ribo_rna.nf.test.snap | 4 ++-- tests/salmon.nf.test.snap | 4 ++-- tests/skip_qc.nf.test.snap | 4 ++-- tests/skip_trimming.nf.test.snap | 2 +- tests/star_rsem.nf.test.snap | 4 ++-- workflows/rnaseq/assets/multiqc/multiqc_config.yml | 2 +- 13 files changed, 33 insertions(+), 22 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 81ea91f1f..3d150c281 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -3,6 +3,18 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). +# 3.18.0dev - xxxx-xx-xx + +### Credits + +Special thanks to the following for their contributions to the release: + +- [Caitlin Winkler](oligomyeggo) + +### Enhancements & fixes + +- [PR #1461](https://github.com/nf-core/rnaseq/pull/1461) - Add FASTQ linting during preprocessing + ## [[3.17.0](https://github.com/nf-core/rnaseq/releases/tag/3.17.0)] - 2024-10-23 ### Credits @@ -26,7 +38,6 @@ Special thanks to the following for their contributions to the release: - [PR #1437](https://github.com/nf-core/rnaseq/pull/1437) - Remove conda from test matrix - [PR #1440](https://github.com/nf-core/rnaseq/pull/1440) - Further reduce matrix - [PR #1441](https://github.com/nf-core/rnaseq/pull/1441) - Force some containers to be converted from docker to singularity to fix nf-core download tests -- [PR #1461](https://github.com/nf-core/rnaseq/pull/1461) - Add FASTQ linting during preprocessing ### Software dependencies diff --git a/nextflow.config b/nextflow.config index e8fd1f913..0af7db8c2 100644 --- a/nextflow.config +++ b/nextflow.config @@ -332,7 +332,7 @@ manifest { description = """RNA sequencing analysis pipeline for gene/isoform quantification and extensive quality control.""" mainScript = 'main.nf' nextflowVersion = '!>=24.04.2' - version = '3.17.0' + version = '3.18.0dev' doi = 'https://doi.org/10.5281/zenodo.1400710' } diff --git a/tests/default.nf.test.snap b/tests/default.nf.test.snap index d8787c7df..804bdb86e 100644 --- a/tests/default.nf.test.snap +++ b/tests/default.nf.test.snap @@ -46,7 +46,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.17.0" + "nf-core/rnaseq": "v3.18.0" } }, [ @@ -240,7 +240,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.17.0" + "nf-core/rnaseq": "v3.18.0" } }, [ diff --git a/tests/featurecounts_group_type.nf.test.snap b/tests/featurecounts_group_type.nf.test.snap index ac1369ce2..b3db0267d 100644 --- a/tests/featurecounts_group_type.nf.test.snap +++ b/tests/featurecounts_group_type.nf.test.snap @@ -46,7 +46,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.17.0" + "nf-core/rnaseq": "v3.18.0" } }, [ @@ -234,7 +234,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.17.0" + "nf-core/rnaseq": "v3.18.0" } }, [ diff --git a/tests/hisat2.nf.test.snap b/tests/hisat2.nf.test.snap index c7f66af3f..e765de22a 100644 --- a/tests/hisat2.nf.test.snap +++ b/tests/hisat2.nf.test.snap @@ -47,7 +47,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.17.0" + "nf-core/rnaseq": "v3.18.0" } }, [ @@ -237,7 +237,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.17.0" + "nf-core/rnaseq": "v3.18.0" } }, [ diff --git a/tests/kallisto.nf.test.snap b/tests/kallisto.nf.test.snap index ac276eefd..192ddaedc 100644 --- a/tests/kallisto.nf.test.snap +++ b/tests/kallisto.nf.test.snap @@ -59,7 +59,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.17.0" + "nf-core/rnaseq": "v3.18.0" } }, [ @@ -319,7 +319,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.17.0" + "nf-core/rnaseq": "v3.18.0" } }, [ diff --git a/tests/min_mapped_reads.nf.test.snap b/tests/min_mapped_reads.nf.test.snap index c2d9df5a9..6a7b61e2f 100644 --- a/tests/min_mapped_reads.nf.test.snap +++ b/tests/min_mapped_reads.nf.test.snap @@ -137,7 +137,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.17.0" + "nf-core/rnaseq": "v3.18.0" } }, [ @@ -1246,7 +1246,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.17.0" + "nf-core/rnaseq": "v3.18.0" } }, [ diff --git a/tests/remove_ribo_rna.nf.test.snap b/tests/remove_ribo_rna.nf.test.snap index 6131451ee..f3ed59ee6 100644 --- a/tests/remove_ribo_rna.nf.test.snap +++ b/tests/remove_ribo_rna.nf.test.snap @@ -143,7 +143,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.17.0" + "nf-core/rnaseq": "v3.18.0" } }, [ @@ -1460,7 +1460,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.17.0" + "nf-core/rnaseq": "v3.18.0" } }, [ diff --git a/tests/salmon.nf.test.snap b/tests/salmon.nf.test.snap index 2ae620ba3..87a9a3e14 100644 --- a/tests/salmon.nf.test.snap +++ b/tests/salmon.nf.test.snap @@ -53,7 +53,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.17.0" + "nf-core/rnaseq": "v3.18.0" } }, [ @@ -392,7 +392,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.17.0" + "nf-core/rnaseq": "v3.18.0" } }, [ diff --git a/tests/skip_qc.nf.test.snap b/tests/skip_qc.nf.test.snap index 6cd87efb9..9188a3502 100644 --- a/tests/skip_qc.nf.test.snap +++ b/tests/skip_qc.nf.test.snap @@ -43,7 +43,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.17.0" + "nf-core/rnaseq": "v3.18.0" } }, [ @@ -182,7 +182,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.17.0" + "nf-core/rnaseq": "v3.18.0" } }, [ diff --git a/tests/skip_trimming.nf.test.snap b/tests/skip_trimming.nf.test.snap index 0f728a3b6..e60a971b1 100644 --- a/tests/skip_trimming.nf.test.snap +++ b/tests/skip_trimming.nf.test.snap @@ -133,7 +133,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.17.0" + "nf-core/rnaseq": "v3.18.0" } }, [ diff --git a/tests/star_rsem.nf.test.snap b/tests/star_rsem.nf.test.snap index 9e95dad0f..ab6ed8d6c 100644 --- a/tests/star_rsem.nf.test.snap +++ b/tests/star_rsem.nf.test.snap @@ -137,7 +137,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.17.0" + "nf-core/rnaseq": "v3.18.0" } }, [ @@ -1339,7 +1339,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.17.0" + "nf-core/rnaseq": "v3.18.0" } }, [ diff --git a/workflows/rnaseq/assets/multiqc/multiqc_config.yml b/workflows/rnaseq/assets/multiqc/multiqc_config.yml index 08360f58c..9b8ee731a 100644 --- a/workflows/rnaseq/assets/multiqc/multiqc_config.yml +++ b/workflows/rnaseq/assets/multiqc/multiqc_config.yml @@ -1,5 +1,5 @@ report_comment: > - This report has been generated by the nf-core/rnaseq analysis pipeline. For information about how to interpret these results, please see the documentation. + This report has been generated by the nf-core/rnaseq analysis pipeline. For information about how to interpret these results, please see the documentation. report_section_order: # Important checks and failures sample-status: From c0f027a792a9b11e79f1c03bee82942502134960 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Tue, 3 Dec 2024 20:40:21 +0000 Subject: [PATCH 050/107] Update CHANGELOG.md --- CHANGELOG.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 3d150c281..1d94d52ef 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -9,7 +9,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 Special thanks to the following for their contributions to the release: -- [Caitlin Winkler](oligomyeggo) +- [Caitlin Winkler](https://github.com/oligomyeggo) ### Enhancements & fixes From 803c4245b4b69ee851396092b6fb4a8360593999 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Tue, 3 Dec 2024 20:56:37 +0000 Subject: [PATCH 051/107] fix version snap --- tests/default.nf.test.snap | 4 ++-- tests/featurecounts_group_type.nf.test.snap | 4 ++-- tests/hisat2.nf.test.snap | 4 ++-- tests/kallisto.nf.test.snap | 4 ++-- tests/min_mapped_reads.nf.test.snap | 4 ++-- tests/remove_ribo_rna.nf.test.snap | 4 ++-- tests/salmon.nf.test.snap | 4 ++-- tests/skip_qc.nf.test.snap | 4 ++-- tests/skip_trimming.nf.test.snap | 2 +- tests/star_rsem.nf.test.snap | 4 ++-- 10 files changed, 19 insertions(+), 19 deletions(-) diff --git a/tests/default.nf.test.snap b/tests/default.nf.test.snap index 804bdb86e..0fde0cc42 100644 --- a/tests/default.nf.test.snap +++ b/tests/default.nf.test.snap @@ -46,7 +46,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0" + "nf-core/rnaseq": "v3.18.0dev" } }, [ @@ -240,7 +240,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0" + "nf-core/rnaseq": "v3.18.0dev" } }, [ diff --git a/tests/featurecounts_group_type.nf.test.snap b/tests/featurecounts_group_type.nf.test.snap index b3db0267d..c1d285eb5 100644 --- a/tests/featurecounts_group_type.nf.test.snap +++ b/tests/featurecounts_group_type.nf.test.snap @@ -46,7 +46,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0" + "nf-core/rnaseq": "v3.18.0dev" } }, [ @@ -234,7 +234,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0" + "nf-core/rnaseq": "v3.18.0dev" } }, [ diff --git a/tests/hisat2.nf.test.snap b/tests/hisat2.nf.test.snap index e765de22a..3c943fb61 100644 --- a/tests/hisat2.nf.test.snap +++ b/tests/hisat2.nf.test.snap @@ -47,7 +47,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0" + "nf-core/rnaseq": "v3.18.0dev" } }, [ @@ -237,7 +237,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0" + "nf-core/rnaseq": "v3.18.0dev" } }, [ diff --git a/tests/kallisto.nf.test.snap b/tests/kallisto.nf.test.snap index 192ddaedc..d3a458f5d 100644 --- a/tests/kallisto.nf.test.snap +++ b/tests/kallisto.nf.test.snap @@ -59,7 +59,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0" + "nf-core/rnaseq": "v3.18.0dev" } }, [ @@ -319,7 +319,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0" + "nf-core/rnaseq": "v3.18.0dev" } }, [ diff --git a/tests/min_mapped_reads.nf.test.snap b/tests/min_mapped_reads.nf.test.snap index 6a7b61e2f..e9a85eb85 100644 --- a/tests/min_mapped_reads.nf.test.snap +++ b/tests/min_mapped_reads.nf.test.snap @@ -137,7 +137,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0" + "nf-core/rnaseq": "v3.18.0dev" } }, [ @@ -1246,7 +1246,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0" + "nf-core/rnaseq": "v3.18.0dev" } }, [ diff --git a/tests/remove_ribo_rna.nf.test.snap b/tests/remove_ribo_rna.nf.test.snap index f3ed59ee6..86a609166 100644 --- a/tests/remove_ribo_rna.nf.test.snap +++ b/tests/remove_ribo_rna.nf.test.snap @@ -143,7 +143,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0" + "nf-core/rnaseq": "v3.18.0dev" } }, [ @@ -1460,7 +1460,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0" + "nf-core/rnaseq": "v3.18.0dev" } }, [ diff --git a/tests/salmon.nf.test.snap b/tests/salmon.nf.test.snap index 87a9a3e14..fd9043460 100644 --- a/tests/salmon.nf.test.snap +++ b/tests/salmon.nf.test.snap @@ -53,7 +53,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0" + "nf-core/rnaseq": "v3.18.0dev" } }, [ @@ -392,7 +392,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0" + "nf-core/rnaseq": "v3.18.0dev" } }, [ diff --git a/tests/skip_qc.nf.test.snap b/tests/skip_qc.nf.test.snap index 9188a3502..ca881d057 100644 --- a/tests/skip_qc.nf.test.snap +++ b/tests/skip_qc.nf.test.snap @@ -43,7 +43,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0" + "nf-core/rnaseq": "v3.18.0dev" } }, [ @@ -182,7 +182,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0" + "nf-core/rnaseq": "v3.18.0dev" } }, [ diff --git a/tests/skip_trimming.nf.test.snap b/tests/skip_trimming.nf.test.snap index e60a971b1..35c14b87d 100644 --- a/tests/skip_trimming.nf.test.snap +++ b/tests/skip_trimming.nf.test.snap @@ -133,7 +133,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0" + "nf-core/rnaseq": "v3.18.0dev" } }, [ diff --git a/tests/star_rsem.nf.test.snap b/tests/star_rsem.nf.test.snap index ab6ed8d6c..2b1b7eca7 100644 --- a/tests/star_rsem.nf.test.snap +++ b/tests/star_rsem.nf.test.snap @@ -137,7 +137,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0" + "nf-core/rnaseq": "v3.18.0dev" } }, [ @@ -1339,7 +1339,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0" + "nf-core/rnaseq": "v3.18.0dev" } }, [ From 450b5b9a815f70a4d8a30eddf6da9354871717c7 Mon Sep 17 00:00:00 2001 From: Matthias Zepper Date: Wed, 4 Dec 2024 14:45:11 +0100 Subject: [PATCH 052/107] Restore truncated CHANGELOG.md with 3.0 and prior releases. --- CHANGELOG.md | 427 ++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 426 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 90c82ee19..3ec05c64e 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1007,4 +1007,429 @@ Note, since the pipeline is now using Nextflow DSL2, each process will be run wi ### :warning: Major enhancements - You will need to install Nextflow `>=20.11.0-edge` to run the pipeline. If you are using Singularity, then features introduced in that release now enable the pipeline to directly download Singularity images hosted by Biocontainers as opposed to performing a conversion from Docker images (see [#496](https://github.com/nf-core/rnaseq/issues/496)). -- The previous default of aligning BAM files using STAR and quantifying using fe +- The previous default of aligning BAM files using STAR and quantifying using featureCounts (`--aligner star`) has been removed. The new default is to align with STAR and quantify using Salmon (`--aligner star_salmon`). + - This decision was made primarily because of the limitations of featureCounts to appropriately quantify gene expression data. Please see [Zhao et al., 2015](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0141910#pone-0141910-t001) and [Soneson et al., 2015](https://f1000research.com/articles/4-1521/v1)). +- For similar reasons, **quantification will not be performed** if using `--aligner hisat2` due to the lack of an appropriate option to calculate accurate expression estimates from HISAT2 derived genomic alignments. + - This pipeline option is still available for those who have a preference for the alignment, QC and other types of downstream analysis compatible with the output of HISAT2. No gene-level quantification results will be generated. + - In a future release we hope to add back quantitation for HISAT2 using different tools. + +### Enhancements & fixes + +- Updated pipeline template to nf-core/tools `1.12.1` +- Bumped Nextflow version `20.07.1` -> `20.11.0-edge` +- Added UCSC `bedClip` module to restrict bedGraph file coordinates to chromosome boundaries +- Check if Bioconda and conda-forge channels are set-up correctly when running with `-profile conda` +- Use `rsem-prepare-reference` and not `gffread` to create transcriptome fasta file +- [[#494](https://github.com/nf-core/rnaseq/issues/494)] - Issue running rnaseq v2.0 (DSL2) with test profile +- [[#496](https://github.com/nf-core/rnaseq/issues/496)] - Direct download of Singularity images via HTTPS +- [[#498](https://github.com/nf-core/rnaseq/issues/498)] - Significantly different versions of STAR in star_rsem (2.7.6a) and star (2.6.1d) +- [[#499](https://github.com/nf-core/rnaseq/issues/499)] - Use of salmon counts for DESeq2 +- [[#500](https://github.com/nf-core/rnaseq/issues/500), [#509](https://github.com/nf-core/rnaseq/issues/509)] - Error with AWS batch params +- [[#511](https://github.com/nf-core/rnaseq/issues/511)] - rsem/star index fails with large genome +- [[#515](https://github.com/nf-core/rnaseq/issues/515)] - Add decoy-aware indexing for salmon +- [[#516](https://github.com/nf-core/rnaseq/issues/516)] - Unexpected error [InvocationTargetException] +- [[#525](https://github.com/nf-core/rnaseq/issues/525)] - sra_ids_to_runinfo.py UnicodeEncodeError +- [[#550](https://github.com/nf-core/rnaseq/issues/525)] - handle samplesheets with replicate=0 + +### Parameters + +| Old parameter | New parameter | +| --------------------------- | -------------------------------------- | +| `--fc_extra_attributes` | `--gtf_extra_attributes` | +| `--fc_group_features` | `--gtf_group_features` | +| `--fc_count_type` | `--gtf_count_type` | +| `--fc_group_features_type` | `--gtf_group_features_type` | +| | `--singularity_pull_docker_container` | +| `--skip_featurecounts` | | + +> **NB:** Parameter has been **updated** if both old and new parameter information is present. +> **NB:** Parameter has been **added** if just the new parameter information is present. +> **NB:** Parameter has been **removed** if parameter information isn't present. + +### Software dependencies + +Note, since the pipeline is now using Nextflow DSL2, each process will be run with its own [Biocontainer](https://biocontainers.pro/#/registry). This means that on occasion it is entirely possible for the pipeline to be using different versions of the same tool. However, the overall software dependency changes compared to the last release have been listed below for reference. + +| Dependency | Old version | New version | +| ----------------------------------- | ----------- | ----------- | +| `bioconductor-summarizedexperiment` | 1.18.1 | 1.20.0 | +| `bioconductor-tximeta` | 1.6.3 | 1.8.0 | +| `picard` | 2.23.8 | 2.23.9 | +| `requests` | | 2.24.0 | +| `salmon` | 1.3.0 | 1.4.0 | +| `ucsc-bedclip` | | 377 | +| `umi_tools` | 1.0.1 | 1.1.1 | + +> **NB:** Dependency has been **updated** if both old and new version information is present. +> **NB:** Dependency has been **added** if just the new version information is present. +> **NB:** Dependency has been **removed** if version information isn't present. + +## [[2.0](https://github.com/nf-core/rnaseq/releases/tag/2.0)] - 2020-11-12 + +### Major enhancements + +- Pipeline has been re-implemented in [Nextflow DSL2](https://www.nextflow.io/docs/latest/dsl2.html) +- All software containers are now exclusively obtained from [Biocontainers](https://biocontainers.pro/#/registry) +- Added a separate workflow to download FastQ files via SRA, ENA or GEO ids and to auto-create the input samplesheet ([`ENA FTP`](https://ena-docs.readthedocs.io/en/latest/retrieval/file-download.html); see [`--public_data_ids`](https://nf-co.re/rnaseq/parameters#public_data_ids) parameter) +- Added and refined a Groovy `lib/` of functions that include the automatic rendering of parameters defined in the JSON schema for the help and summary log information +- Replace [edgeR](https://bioconductor.org/packages/release/bioc/html/edgeR.html) with [DESeq2](https://bioconductor.org/packages/release/bioc/html/DESeq2.html) for the generation of PCA and heatmaps (also included in the MultiQC report) +- Creation of bigWig coverage files using [BEDTools](https://github.com/arq5x/bedtools2/) and [bedGraphToBigWig](http://hgdownload.soe.ucsc.edu/admin/exe/) +- [[#70](https://github.com/nf-core/rnaseq/issues/70)] - Added new genome mapping and quantification route with [RSEM](https://github.com/deweylab/RSEM) via the `--aligner star_rsem` parameter +- [[#72](https://github.com/nf-core/rnaseq/issues/72)] - Samples skipped due to low alignment reported in the MultiQC report +- [[#73](https://github.com/nf-core/rnaseq/issues/73), [#435](https://github.com/nf-core/rnaseq/pull/435)] - UMI barcode support +- [[#91](https://github.com/nf-core/rnaseq/issues/91)] - Ability to concatenate multiple runs of the same samples via the input samplesheet +- [[#123](https://github.com/nf-core/rnaseq/issues/123)] - The primary input for the pipeline has changed from `--reads` glob to samplesheet `--input`. See [usage docs](https://nf-co.re/rnaseq/docs/usage#introduction). +- [[#197](https://github.com/nf-core/rnaseq/issues/197)] - Samples failing strand-specificity checks reported in the MultiQC report +- [[#227](https://github.com/nf-core/rnaseq/issues/227)] - Removal of ribosomal RNA via [SortMeRNA](https://github.com/biocore/sortmerna) +- [[#419](https://github.com/nf-core/rnaseq/pull/419)] - Add `--additional_fasta` parameter to provide ERCC spike-ins, transgenes such as GFP or CAR-T as additional sequences to align to + +### Other enhancements & fixes + +- Updated pipeline template to nf-core/tools `1.11` +- Optimise MultiQC configuration for faster run-time on huge sample numbers +- Add information about SILVA licensing when removing rRNA to `usage.md` +- Fixed ansi colours for pipeline summary, added summary logs of alignment results +- [[#281](https://github.com/nf-core/rnaseq/issues/281)] - Add nag to cite the pipeline in summary +- [[#302](https://github.com/nf-core/rnaseq/issues/302)] - Fixed MDS plot axis labels +- [[#338](https://github.com/nf-core/rnaseq/issues/338)] - Add option for turning on/off STAR command line option (--sjdbGTFfile) +- [[#344](https://github.com/nf-core/rnaseq/issues/344)] - Added multi-core TrimGalore support +- [[#351](https://github.com/nf-core/rnaseq/issues/351)] - Fixes missing Qualimap parameter `-p` +- [[#353](https://github.com/nf-core/rnaseq/issues/353)] - Fixes an issue where MultiQC fails to run with `--skip_biotype_qc` option +- [[#357](https://github.com/nf-core/rnaseq/issues/357)] - Fixes broken links +- [[#362](https://github.com/nf-core/rnaseq/issues/362)] - Fix error with gzipped annotation file +- [[#384](https://github.com/nf-core/rnaseq/issues/384)] - Changed SortMeRNA reference dbs path to use stable URLs (v4.2.0) +- [[#396](https://github.com/nf-core/rnaseq/issues/396)] - Deterministic mapping for STAR aligner +- [[#412](https://github.com/nf-core/rnaseq/issues/412)] - Fix Qualimap not being passed on correct strand-specificity parameter +- [[#413](https://github.com/nf-core/rnaseq/issues/413)] - Fix STAR unmapped reads not output +- [[#434](https://github.com/nf-core/rnaseq/issues/434)] - Fix typo reported for work-dir +- [[#437](https://github.com/nf-core/rnaseq/issues/434)] - FastQC uses correct number of threads now +- [[#440](https://github.com/nf-core/rnaseq/issues/440)] - Fixed issue where featureCounts process fails when setting `--fc_count_type` to gene +- [[#452](https://github.com/nf-core/rnaseq/issues/452)] - Fix `--gff` input bug +- [[#345](https://github.com/nf-core/rnaseq/pull/345)] - Fixes label name in FastQC process +- [[#391](https://github.com/nf-core/rnaseq/pull/391)] - Make publishDir mode configurable +- [[#431](https://github.com/nf-core/rnaseq/pull/431)] - Update AWS GitHub actions workflow with organization level secrets +- [[#435](https://github.com/nf-core/rnaseq/pull/435)] - Fix a bug where gzipped references were not extracted when `--additional_fasta` was not specified +- [[#435](https://github.com/nf-core/rnaseq/pull/435)] - Fix a bug where merging of RSEM output would fail if only one fastq provided as input +- [[#435](https://github.com/nf-core/rnaseq/pull/435)] - Correct RSEM output name (was saving counts but calling them TPMs; now saving both properly labelled) +- [[#436](https://github.com/nf-core/rnaseq/pull/436)] - Fix a bug where the RSEM reference could not be built +- [[#458](https://github.com/nf-core/rnaseq/pull/458)] - Fix `TMP_DIR` for process MarkDuplicates and Qualimap + +### Parameters + +#### Updated + +| Old parameter | New parameter | +| ----------------------------- | --------------------------- | +| `--reads` | `--input` | +| `--igenomesIgnore` | `--igenomes_ignore` | +| `--removeRiboRNA` | `--remove_ribo_rna` | +| `--rRNA_database_manifest` | `--ribo_database_manifest` | +| `--save_nonrRNA_reads` | `--save_non_ribo_reads` | +| `--saveAlignedIntermediates` | `--save_align_intermeds` | +| `--saveReference` | `--save_reference` | +| `--saveTrimmed` | `--save_trimmed` | +| `--saveUnaligned` | `--save_unaligned` | +| `--skipAlignment` | `--skip_alignment` | +| `--skipBiotypeQC` | `--skip_biotype_qc` | +| `--skipDupRadar` | `--skip_dupradar` | +| `--skipFastQC` | `--skip_fastqc` | +| `--skipMultiQC` | `--skip_multiqc` | +| `--skipPreseq` | `--skip_preseq` | +| `--skipQC` | `--skip_qc` | +| `--skipQualimap` | `--skip_qualimap` | +| `--skipRseQC` | `--skip_rseqc` | +| `--skipTrimming` | `--skip_trimming` | +| `--stringTieIgnoreGTF` | `--stringtie_ignore_gtf` | + +#### Added + +- `--additional_fasta` - FASTA file to concatenate to genome FASTA file e.g. containing spike-in sequences +- `--deseq2_vst` - Use vst transformation instead of rlog with DESeq2 +- `--enable_conda` - Run this workflow with Conda. You can also use '-profile conda' instead of providing this parameter +- `--min_mapped_reads` - Minimum percentage of uniquely mapped reads below which samples are removed from further processing +- `--multiqc_title` - MultiQC report title. Printed as page header, used for filename if not otherwise specified +- `--public_data_ids` - File containing SRA/ENA/GEO identifiers one per line in order to download their associated FastQ files +- `--publish_dir_mode` - Method used to save pipeline results to output directory +- `--rsem_index` - Path to directory or tar.gz archive for pre-built RSEM index +- `--rseqc_modules` - Specify the RSeQC modules to run +- `--save_merged_fastq` - Save FastQ files after merging re-sequenced libraries in the results directory +- `--save_umi_intermeds` - If this option is specified, intermediate FastQ and BAM files produced by UMI-tools are also saved in the results directory +- `--skip_bigwig` - Skip bigWig file creation +- `--skip_deseq2_qc` - Skip DESeq2 PCA and heatmap plotting +- `--skip_featurecounts` - Skip featureCounts +- `--skip_markduplicates` - Skip picard MarkDuplicates step +- `--skip_sra_fastq_download` - Only download metadata for public data database ids and don't download the FastQ files +- `--skip_stringtie` - Skip StringTie +- `--star_ignore_sjdbgtf` - See [#338](https://github.com/nf-core/rnaseq/issues/338) +- `--umitools_bc_pattern` - The UMI barcode pattern to use e.g. 'NNNNNN' indicates that the first 6 nucleotides of the read are from the UMI +- `--umitools_extract_method` - UMI pattern to use. Can be either 'string' (default) or 'regex' +- `--with_umi` - Enable UMI-based read deduplication + +#### Removed + +- `--awsqueue` can now be provided via nf-core/configs if using AWS +- `--awsregion` can now be provided via nf-core/configs if using AWS +- `--compressedReference` now auto-detected +- `--markdup_java_options` in favour of updating centrally on nf-core/modules +- `--project` parameter from old NGI template +- `--readPaths` is not required since these are provided from the input samplesheet +- `--sampleLevel` not required +- `--singleEnd` is now auto-detected from the input samplesheet +- `--skipEdgeR` qc not performed by DESeq2 instead +- `--star_memory` in favour of updating centrally on nf-core/modules if required +- Strandedness is now specified at the sample-level via the input samplesheet + - `--forwardStranded` + - `--reverseStranded` + - `--unStranded` + - `--pico` + +### Software dependencies + +Note, since the pipeline is now using Nextflow DSL2, each process will be run with its own [Biocontainer](https://biocontainers.pro/#/registry). This means that on occasion it is entirely possible for the pipeline to be using different versions of the same tool. However, the overall software dependency changes compared to the last release have been listed below for reference. + +| Dependency | Old version | New version | +| ----------------------------------- | ----------- | ----------- | +| `bioconductor-dupradar` | 1.14.0 | 1.18.0 | +| `bioconductor-summarizedexperiment` | 1.14.0 | 1.18.1 | +| `bioconductor-tximeta` | 1.2.2 | 1.6.3 | +| `fastqc` | 0.11.8 | 0.11.9 | +| `gffread` | 0.11.4 | 0.12.1 | +| `hisat2` | 2.1.0 | 2.2.0 | +| `multiqc` | 1.7 | 1.9 | +| `picard` | 2.21.1 | 2.23.8 | +| `qualimap` | 2.2.2c | 2.2.2d | +| `r-base` | 3.6.1 | 4.0.3 | +| `salmon` | 0.14.2 | 1.3.0 | +| `samtools` | 1.9 | 1.10 | +| `sortmerna` | 2.1b | 4.2.0 | +| `stringtie` | 2.0 | 2.1.4 | +| `subread` | 1.6.4 | 2.0.1 | +| `trim-galore` | 0.6.4 | 0.6.6 | +| `bedtools` | - | 2.29.2 | +| `bioconductor-biocparallel` | - | 1.22.0 | +| `bioconductor-complexheatmap` | - | 2.4.2 | +| `bioconductor-deseq2` | - | 1.28.0 | +| `bioconductor-tximport` | - | 1.16.0 | +| `perl` | - | 5.26.2 | +| `python` | - | 3.8.3 | +| `r-ggplot2` | - | 3.3.2 | +| `r-optparse` | - | 1.6.6 | +| `r-pheatmap` | - | 1.0.12 | +| `r-rcolorbrewer` | - | 1.1_2 | +| `rsem` | - | 1.3.3 | +| `ucsc-bedgraphtobigwig` | - | 377 | +| `umi_tools` | - | 1.0.1 | +| `bioconductor-edger` | - | - | +| `deeptools` | - | - | +| `matplotlib` | - | - | +| `r-data.table` | - | - | +| `r-gplots` | - | - | +| `r-markdown` | - | - | + +> **NB:** Dependency has been **updated** if both old and new version information is present. +> **NB:** Dependency has been **added** if just the new version information is present. +> **NB:** Dependency has been **removed** if version information isn't present. + +## [[1.4.2](https://github.com/nf-core/rnaseq/releases/tag/1.4.2)] - 2019-10-18 + +- Minor version release for keeping Git History in sync +- No changes with respect to 1.4.1 on pipeline level + +## [[1.4.1](https://github.com/nf-core/rnaseq/releases/tag/1.4.1)] - 2019-10-17 + +Major novel changes include: + +- Update `igenomes.config` with NCBI `GRCh38` and most recent UCSC genomes +- Set `autoMounts = true` by default for `singularity` profile + +### Pipeline enhancements & fixes + +- Fixed parameter warnings [#316](https://github.com/nf-core/rnaseq/issues/316) and [318](https://github.com/nf-core/rnaseq/issues/318) +- Fixed [#307](https://github.com/nf-core/rnaseq/issues/307) - Confusing Info Printout about GFF and GTF + +## [[1.4](https://github.com/nf-core/rnaseq/releases/tag/1.4)] - 2019-10-15 + +Major novel changes include: + +- Support for Salmon as an alternative method to STAR and HISAT2 +- Several improvements in `featureCounts` handling of types other than `exon`. It is possible now to handle nuclearRNAseq data. Nuclear RNA has un-spliced RNA, and the whole transcript, including the introns, needs to be counted, e.g. by specifying `--fc_count_type transcript`. +- Support for [outputting unaligned data](https://github.com/nf-core/rnaseq/issues/277) to results folders. +- Added options to skip several steps + - Skip trimming using `--skipTrimming` + - Skip BiotypeQC using `--skipBiotypeQC` + - Skip Alignment using `--skipAlignment` to only use pseudoalignment using Salmon + +### Documentation updates + +- Adjust wording of skipped samples [in pipeline output](https://github.com/nf-core/rnaseq/issues/290) +- Fixed link to guidelines [#203](https://github.com/nf-core/rnaseq/issues/203) +- Add `Citation` and `Quick Start` section to `README.md` +- Add in documentation of the `--gff` parameter + +### Reporting Updates + +- Generate MultiQC plots in the results directory [#200](https://github.com/nf-core/rnaseq/issues/200) +- Get MultiQC to save plots as [standalone files](https://github.com/nf-core/rnaseq/issues/183) +- Get MultiQC to write out the software versions in a `.csv` file [#185](https://github.com/nf-core/rnaseq/issues/185) +- Use `file` instead of `new File` to create `pipeline_report.{html,txt}` files, and properly create subfolders + +### Pipeline enhancements & fixes + +- Restore `SummarizedExperimment` object creation in the salmon_merge process avoiding increasing memory with sample size. +- Fix sample names in feature counts and dupRadar to remove suffixes added in other processes +- Removed `genebody_coverage` process [#195](https://github.com/nf-core/rnaseq/issues/195) +- Implemented Pearsons correlation instead of Euclidean distance [#146](https://github.com/nf-core/rnaseq/issues/146) +- Add `--stringTieIgnoreGTF` parameter [#206](https://github.com/nf-core/rnaseq/issues/206) +- Removed unused `stringtie` channels for `MultiQC` +- Integrate changes in `nf-core/tools v1.6` template which resolved [#90](https://github.com/nf-core/rnaseq/issues/90) +- Moved process `convertGFFtoGTF` before `makeSTARindex` [#215](https://github.com/nf-core/rnaseq/issues/215) +- Change all boolean parameters from `snake_case` to `camelCase` and vice versa for value parameters +- Add SM ReadGroup info for QualiMap compatibility[#238](https://github.com/nf-core/rnaseq/issues/238) +- Obtain edgeR + dupRadar version information [#198](https://github.com/nf-core/rnaseq/issues/198) and [#112](https://github.com/nf-core/rnaseq/issues/112) +- Add `--gencode` option for compatibility of Salmon and featureCounts biotypes with GENCODE gene annotations +- Added functionality to accept compressed reference data in the pipeline +- Check that gtf features are on chromosomes that exist in the genome fasta file [#274](https://github.com/nf-core/rnaseq/pull/274) +- Maintain all gff features upon gtf conversion (keeps `gene_biotype` or `gene_type` to make `featureCounts` happy) +- Add SortMeRNA as an optional step to allow rRNA removal [#280](https://github.com/nf-core/rnaseq/issues/280) +- Minimal adjustment of memory and CPU constraints for clusters with locked memory / CPU relation +- Cleaned up usage, `parameters.settings.json` and the `nextflow.config` + +### Dependency Updates + +- Dependency list is now sorted appropriately +- Force matplotlib=3.0.3 + +#### Updated Packages + +- Picard 2.20.0 -> 2.21.1 +- bioconductor-dupradar 1.12.1 -> 1.14.0 +- bioconductor-edger 3.24.3 -> 3.26.5 +- gffread 0.9.12 -> 0.11.4 +- trim-galore 0.6.1 -> 0.6.4 +- gffread 0.9.12 -> 0.11.4 +- rseqc 3.0.0 -> 3.0.1 +- R-Base 3.5 -> 3.6.1 + +#### Added / Removed Packages + +- Dropped CSVtk in favor of Unix's simple `cut` and `paste` utilities +- Added Salmon 0.14.2 +- Added TXIMeta 1.2.2 +- Added SummarizedExperiment 1.14.0 +- Added SortMeRNA 2.1b +- Add tximport and summarizedexperiment dependency [#171](https://github.com/nf-core/rnaseq/issues/171) +- Add Qualimap dependency [#202](https://github.com/nf-core/rnaseq/issues/202) + +## [[1.3](https://github.com/nf-core/rnaseq/releases/tag/1.3)] - 2019-03-26 + +### Pipeline Updates + +- Added configurable options to specify group attributes for featureCounts [#144](https://github.com/nf-core/rnaseq/issues/144) +- Added support for RSeqC 3.0 [#148](https://github.com/nf-core/rnaseq/issues/148) +- Added a `parameters.settings.json` file for use with the new `nf-core launch` helper tool. +- Centralized all configuration profiles using [nf-core/configs](https://github.com/nf-core/configs) +- Fixed all centralized configs [for offline usage](https://github.com/nf-core/rnaseq/issues/163) +- Hide %dup in [multiqc report](https://github.com/nf-core/rnaseq/issues/150) +- Add option for Trimming NextSeq data properly ([@jburos work](https://github.com/jburos)) + +### Bug fixes + +- Fixing HISAT2 Index Building for large reference genomes [#153](https://github.com/nf-core/rnaseq/issues/153) +- Fixing HISAT2 BAM sorting using more memory than available on the system +- Fixing MarkDuplicates memory consumption issues following [#179](https://github.com/nf-core/rnaseq/pull/179) +- Use `file` instead of `new File` to create the `pipeline_report.{html,txt}` files to avoid creating local directories when outputting to AWS S3 folders +- Fix SortMeRNA default rRNA db paths specified in assets/rrna-db-defaults.txt + +### Dependency Updates + +- RSeQC 2.6.4 -> 3.0.0 +- Picard 2.18.15 -> 2.20.0 +- r-data.table 1.11.4 -> 1.12.2 +- bioconductor-edger 3.24.1 -> 3.24.3 +- r-markdown 0.8 -> 0.9 +- csvtk 0.15.0 -> 0.17.0 +- stringtie 1.3.4 -> 1.3.6 +- subread 1.6.2 -> 1.6.4 +- gffread 0.9.9 -> 0.9.12 +- multiqc 1.6 -> 1.7 +- deeptools 3.2.0 -> 3.2.1 +- trim-galore 0.5.0 -> 0.6.1 +- qualimap 2.2.2b +- matplotlib 3.0.3 +- r-base 3.5.1 + +## [[1.2](https://github.com/nf-core/rnaseq/releases/tag/1.2)] - 2018-12-12 + +### Pipeline updates + +- Removed some outdated documentation about non-existent features +- Config refactoring and code cleaning +- Added a `--fcExtraAttributes` option to specify more than ENSEMBL gene names in `featureCounts` +- Remove legacy rseqc `strandRule` config code. [#119](https://github.com/nf-core/rnaseq/issues/119) +- Added STRINGTIE ballgown output to results folder [#125](https://github.com/nf-core/rnaseq/issues/125) +- HiSAT index build now requests `200GB` memory, enough to use the exons / splice junction option for building. + - Added documentation about the `--hisatBuildMemory` option. +- BAM indices are stored and re-used between processes [#71](https://github.com/nf-core/rnaseq/issues/71) + +### Bug Fixes + +- Fixed conda bug which caused problems with environment resolution due to changes in bioconda [#113](https://github.com/nf-core/rnaseq/issues/113) +- Fixed wrong gffread command line [#117](https://github.com/nf-core/rnaseq/issues/117) +- Added `cpus = 1` to `workflow summary process` [#130](https://github.com/nf-core/rnaseq/issues/130) + +## [[1.1](https://github.com/nf-core/rnaseq/releases/tag/1.1)] - 2018-10-05 + +### Pipeline updates + +- Wrote docs and made minor tweaks to the `--skip_qc` and associated options +- Removed the depreciated `uppmax-modules` config profile +- Updated the `hebbe` config profile to use the new `withName` syntax too +- Use new `workflow.manifest` variables in the pipeline script +- Updated minimum nextflow version to `0.32.0` + +### Bug Fixes + +- [#77](https://github.com/nf-core/rnaseq/issues/77): Added back `executor = 'local'` for the `workflow_summary_mqc` +- [#95](https://github.com/nf-core/rnaseq/issues/95): Check if task.memory is false instead of null +- [#97](https://github.com/nf-core/rnaseq/issues/97): Resolved edge-case where numeric sample IDs are parsed as numbers causing some samples to be incorrectly overwritten. + +## [[1.0](https://github.com/nf-core/rnaseq/releases/tag/1.0)] - 2018-08-20 + +This release marks the point where the pipeline was moved from [SciLifeLab/NGI-RNAseq](https://github.com/SciLifeLab/NGI-RNAseq) +over to the new [nf-core](http://nf-co.re/) community, at [nf-core/rnaseq](https://github.com/nf-core/rnaseq). + +View the previous changelog at [SciLifeLab/NGI-RNAseq/CHANGELOG.md](https://github.com/SciLifeLab/NGI-RNAseq/blob/master/CHANGELOG.md) + +In addition to porting to the new nf-core community, the pipeline has had a number of major changes in this version. +There have been 157 commits by 16 different contributors covering 70 different files in the pipeline: 7,357 additions and 8,236 deletions! + +In summary, the main changes are: + +- Rebranding and renaming throughout the pipeline to nf-core +- Updating many parts of the pipeline config and style to meet nf-core standards +- Support for GFF files in addition to GTF files + - Just use `--gff` instead of `--gtf` when specifying a file path +- New command line options to skip various quality control steps +- More safety checks when launching a pipeline + - Several new sanity checks - for example, that the specified reference genome exists +- Improved performance with memory usage (especially STAR and Picard) +- New BigWig file outputs for plotting coverage across the genome +- Refactored gene body coverage calculation, now much faster and using much less memory +- Bugfixes in the MultiQC process to avoid edge cases where it wouldn't run +- MultiQC report now automatically attached to the email sent when the pipeline completes +- New testing method, with data on GitHub + - Now run pipeline with `-profile test` instead of using bash scripts +- Rewritten continuous integration tests with Travis CI +- New explicit support for Singularity containers +- Improved MultiQC support for DupRadar and featureCounts + - Now works for all users instead of just NGI Stockholm +- New configuration for use on AWS batch +- Updated config syntax to support latest versions of Nextflow +- Built-in support for a number of new local HPC systems + - CCGA, GIS, UCT HEX, updates to UPPMAX, CFC, BINAC, Hebbe, c3se +- Slightly improved documentation (more updates to come) +- Updated software packages + +...and many more minor tweaks. + +Thanks to everyone who has worked on this release! From a2dac872257e44df27c75e571fb205501a94a07d Mon Sep 17 00:00:00 2001 From: nf-core-bot Date: Wed, 4 Dec 2024 14:03:23 +0000 Subject: [PATCH 053/107] [automated] Fix code linting --- CHANGELOG.md | 60 ++++++++++++++++++++++++++-------------------------- 1 file changed, 30 insertions(+), 30 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 3ec05c64e..1fa69957a 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1033,14 +1033,14 @@ Note, since the pipeline is now using Nextflow DSL2, each process will be run wi ### Parameters -| Old parameter | New parameter | -| --------------------------- | -------------------------------------- | -| `--fc_extra_attributes` | `--gtf_extra_attributes` | -| `--fc_group_features` | `--gtf_group_features` | -| `--fc_count_type` | `--gtf_count_type` | -| `--fc_group_features_type` | `--gtf_group_features_type` | -| | `--singularity_pull_docker_container` | -| `--skip_featurecounts` | | +| Old parameter | New parameter | +| -------------------------- | ------------------------------------- | +| `--fc_extra_attributes` | `--gtf_extra_attributes` | +| `--fc_group_features` | `--gtf_group_features` | +| `--fc_count_type` | `--gtf_count_type` | +| `--fc_group_features_type` | `--gtf_group_features_type` | +| | `--singularity_pull_docker_container` | +| `--skip_featurecounts` | | > **NB:** Parameter has been **updated** if both old and new parameter information is present. > **NB:** Parameter has been **added** if just the new parameter information is present. @@ -1118,28 +1118,28 @@ Note, since the pipeline is now using Nextflow DSL2, each process will be run wi #### Updated -| Old parameter | New parameter | -| ----------------------------- | --------------------------- | -| `--reads` | `--input` | -| `--igenomesIgnore` | `--igenomes_ignore` | -| `--removeRiboRNA` | `--remove_ribo_rna` | -| `--rRNA_database_manifest` | `--ribo_database_manifest` | -| `--save_nonrRNA_reads` | `--save_non_ribo_reads` | -| `--saveAlignedIntermediates` | `--save_align_intermeds` | -| `--saveReference` | `--save_reference` | -| `--saveTrimmed` | `--save_trimmed` | -| `--saveUnaligned` | `--save_unaligned` | -| `--skipAlignment` | `--skip_alignment` | -| `--skipBiotypeQC` | `--skip_biotype_qc` | -| `--skipDupRadar` | `--skip_dupradar` | -| `--skipFastQC` | `--skip_fastqc` | -| `--skipMultiQC` | `--skip_multiqc` | -| `--skipPreseq` | `--skip_preseq` | -| `--skipQC` | `--skip_qc` | -| `--skipQualimap` | `--skip_qualimap` | -| `--skipRseQC` | `--skip_rseqc` | -| `--skipTrimming` | `--skip_trimming` | -| `--stringTieIgnoreGTF` | `--stringtie_ignore_gtf` | +| Old parameter | New parameter | +| ---------------------------- | -------------------------- | +| `--reads` | `--input` | +| `--igenomesIgnore` | `--igenomes_ignore` | +| `--removeRiboRNA` | `--remove_ribo_rna` | +| `--rRNA_database_manifest` | `--ribo_database_manifest` | +| `--save_nonrRNA_reads` | `--save_non_ribo_reads` | +| `--saveAlignedIntermediates` | `--save_align_intermeds` | +| `--saveReference` | `--save_reference` | +| `--saveTrimmed` | `--save_trimmed` | +| `--saveUnaligned` | `--save_unaligned` | +| `--skipAlignment` | `--skip_alignment` | +| `--skipBiotypeQC` | `--skip_biotype_qc` | +| `--skipDupRadar` | `--skip_dupradar` | +| `--skipFastQC` | `--skip_fastqc` | +| `--skipMultiQC` | `--skip_multiqc` | +| `--skipPreseq` | `--skip_preseq` | +| `--skipQC` | `--skip_qc` | +| `--skipQualimap` | `--skip_qualimap` | +| `--skipRseQC` | `--skip_rseqc` | +| `--skipTrimming` | `--skip_trimming` | +| `--stringTieIgnoreGTF` | `--stringtie_ignore_gtf` | #### Added From 9b62f064d0cb74a01995233079b7ed0bddbcf495 Mon Sep 17 00:00:00 2001 From: Rob Syme Date: Thu, 5 Dec 2024 18:55:29 -0500 Subject: [PATCH 054/107] Move channel operations outside of the onComplete() block --- subworkflows/local/utils_nfcore_rnaseq_pipeline/main.nf | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/subworkflows/local/utils_nfcore_rnaseq_pipeline/main.nf b/subworkflows/local/utils_nfcore_rnaseq_pipeline/main.nf index 1745f8be6..5e8820ddc 100644 --- a/subworkflows/local/utils_nfcore_rnaseq_pipeline/main.nf +++ b/subworkflows/local/utils_nfcore_rnaseq_pipeline/main.nf @@ -116,6 +116,8 @@ workflow PIPELINE_COMPLETION { id, status -> pass_strand_check[id] = status } + def multiqc_report_list = multiqc_report.toList() + // // Completion email and summary // @@ -128,7 +130,7 @@ workflow PIPELINE_COMPLETION { plaintext_email, outdir, monochrome_logs, - multiqc_report.toList() + multiqc_report_list.getVal() ) } From 532497b0d861fd31b8e8a88337ba168d6b858964 Mon Sep 17 00:00:00 2001 From: Rob Syme Date: Thu, 5 Dec 2024 19:00:48 -0500 Subject: [PATCH 055/107] Add changelog entry --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 1fa69957a..c5c417dd3 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -16,6 +16,7 @@ Special thanks to the following for their contributions to the release: - [PR #1369](https://github.com/nf-core/rnaseq/pull/1369) - Add umicollapse as an alternative to umi-tools - [PR #1461](https://github.com/nf-core/rnaseq/pull/1461) - Add FASTQ linting during preprocessing +- [PR #1463](https://github.com/nf-core/rnaseq/pull/1463) - Move channel operations outside of the onComplete() block ### Software dependencies From f4b76b1207bb35defeca30b5210e3704cd41bffb Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Tue, 10 Dec 2024 11:35:20 +0000 Subject: [PATCH 056/107] Starting complement for umi factor-out --- subworkflows/local/bam_dedup_umi/main.nf | 118 +++++++++++++++++++++++ workflows/rnaseq/main.nf | 97 ++++--------------- workflows/rnaseq/nextflow.config | 10 +- 3 files changed, 142 insertions(+), 83 deletions(-) create mode 100644 subworkflows/local/bam_dedup_umi/main.nf diff --git a/subworkflows/local/bam_dedup_umi/main.nf b/subworkflows/local/bam_dedup_umi/main.nf new file mode 100644 index 000000000..3359fa7b6 --- /dev/null +++ b/subworkflows/local/bam_dedup_umi/main.nf @@ -0,0 +1,118 @@ +// +// BAM deduplication with UMI processing +// + +include { BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE } from '../../../subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse' +include { BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS } from '../../../subworkflows/nf-core/bam_dedup_stats_samtools_umitools' +include { BAM_SORT_STATS_SAMTOOLS } from '../../../subworkflows/nf-core/bam_sort_stats_samtools' +include { UMITOOLS_PREPAREFORRSEM } from '../../../modules/nf-core/umitools/prepareforrsem' +include { SAMTOOLS_SORT } from '../../../modules/nf-core/samtools/sort/main' + +workflow BAM_DEDUP_UMI { + take: + ch_genome_bam // channel: [ val(meta), path(bam), path(bai) ] + ch_fasta // channel: [ path(fasta) ] + umi_dedup_tool // string: 'umicollapse' or 'umitools' + umitools_dedup_stats // boolean: whether to generate UMI-tools dedup stats + bam_csi_index // boolean: whether to generate CSI index + ch_transcriptome_bam // + ch_transcript_fasta + + main: + ch_versions = Channel.empty() + + if (umi_dedup_tool == "umicollapse" && umi_dedup_tool != "umitools"){ + error("Unknown umi_dedup_tool '${umi_dedup_tool}'") + } + + // Genome BAM deduplication + if (umi_dedup_tool == "umicollapse") { + BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE ( + ch_genome_bam + ) + UMI_DEDUP_GENOME = BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE + ch_dedup_log = UMI_DEDUP_GENOME.out.dedup_stats + + } else if (umi_dedup_tool == "umitools") { + BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS ( + ch_genome_bam, + umitools_dedup_stats + ) + UMI_DEDUP_GENOME = BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS + ch_dedup_log = UMI_DEDUP_GENOME.out.deduplog + } + + // Co-ordinate sort, index and run stats on transcriptome BAM. This takes + // some preparation- we have to coordinate sort the BAM, run the + // deduplication, then restore name sorting and run a script from umitools + // to prepare for rsem or salmon + + // 1. Coordinate sort + + BAM_SORT_STATS_SAMTOOLS ( + ch_transcriptome_bam, + ch_transcript_fasta.map { [ [:], it ] } + ) + ch_sorted_transcriptome_bam = BAM_SORT_STATS_SAMTOOLS.out.bam + .join(BAM_SORT_STATS_SAMTOOLS.out.bai) + + // 2. Transcriptome BAM deduplication + if (umi_dedup_tool == "umicollapse") { + BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE ( + ch_sorted_transcriptome_bam + ) + UMI_DEDUP_GENOME = BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE + ch_dedup_log = UMI_DEDUP_GENOME.out.dedup_stats + + } else if (umi_dedup_tool == "umitools") { + BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS ( + ch_sorted_transcriptome_bam, + umitools_dedup_stats + ) + UMI_DEDUP_GENOME = BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS + ch_dedup_log = UMI_DEDUP_GENOME.out.deduplog + } + + // 3. Restore name sorting + SAMTOOLS_SORT ( + UMI_DEDUP_TRANSCRIPTOME.out.bam, + ch_fasta.map { [ [:], it ] } + ) + + // 4. Run prepare_for_rsem.py on paired-end BAM files + // This fixes paired-end reads in name sorted BAM files + // See: https://github.com/nf-core/rnaseq/issues/828 + ended_transcriptome_dedup_bam = SAMTOOLS_SORT.out.bam + .branch { + meta, bam -> + single_end: meta.single_end + return [ meta, bam ] + paired_end: !meta.single_end + return [ meta, bam ] + } + + UMITOOLS_PREPAREFORSALMON ( + ended_transcriptome_dedup_bam.paired_end + .map { meta, bam -> [ meta, bam, [] ] } + ) + + ch_dedup_transcriptome_bam = ch_transcriptome_bam + .single_end + .mix(UMITOOLS_PREPAREFORSALMON.out.bam) + + // Record versions + + ch_versions = UMI_DEDUP_GENOME.out.versions + .mix(BAM_SORT_STATS_SAMTOOLS.out.versions) + .mix(UMITOOLS_PREPAREFORSALMON.out.versions) + + emit: + bam = UMI_DEDUP_GENOME.out.bam // channel: [ val(meta), path(bam) ] + bam_index = bam_csi_index ? UMI_DEDUP_GENOME.out.csi : UMI_DEDUP_GENOME.out.bai // channel: [ val(meta), path(bai) ] + dedup_log = ch_dedup_log // channel: [ val(meta), path(log) ] + stats = UMI_DEDUP_GENOME.out.stats + flagstat = UMI_DEDUP_GENOME.out.flagstat + idxstats = UMI_DEDUP_GENOME.out.idxstats + transcriptome_bam = ch_dedup_transcriptome_bam // channel: [ val(meta), path(bam) ] + versions = ch_versions // channel: [ path(versions.yml) ] +} diff --git a/workflows/rnaseq/main.nf b/workflows/rnaseq/main.nf index 2e0d7d161..4328ec3a7 100755 --- a/workflows/rnaseq/main.nf +++ b/workflows/rnaseq/main.nf @@ -17,6 +17,7 @@ include { MULTIQC_CUSTOM_BIOTYPE } from '../../modules/local/multiqc // include { ALIGN_STAR } from '../../subworkflows/local/align_star' include { QUANTIFY_RSEM } from '../../subworkflows/local/quantify_rsem' +include { BAM_DEDUP_UMI } from '../../subworkflows/local/bam_dedup_umi' include { checkSamplesAfterGrouping } from '../../subworkflows/local/utils_nfcore_rnaseq_pipeline' include { multiqcTsvFromList } from '../../subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness' include { getStarPercentMapped } from '../../subworkflows/local/utils_nfcore_rnaseq_pipeline' @@ -218,88 +219,28 @@ workflow RNASEQ { // SUBWORKFLOW: Remove duplicate reads from BAM file based on UMIs // if (params.with_umi) { - // Deduplicate genome BAM file before downstream analysis - if (params.umi_dedup_tool == "umicollapse") { - BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_GENOME ( - ch_genome_bam.join(ch_genome_bam_index, by: [0]) - ) - UMI_DEDUP_GENOME = BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_GENOME - ch_multiqc_files = ch_multiqc_files.mix(UMI_DEDUP_GENOME.out.dedup_stats.collect{it[1]}.ifEmpty([])) - } else if (params.umi_dedup_tool == "umitools") { - BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME ( - ch_genome_bam.join(ch_genome_bam_index, by: [0]), - params.umitools_dedup_stats - ) - UMI_DEDUP_GENOME = BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME - ch_multiqc_files = ch_multiqc_files.mix(UMI_DEDUP_GENOME.out.deduplog.collect{it[1]}) - } else { - error("Unknown umi_dedup_tool '${params.umi_dedup_tool}'") - } - ch_genome_bam = UMI_DEDUP_GENOME.out.bam - ch_genome_bam_index = UMI_DEDUP_GENOME.out.bai - ch_multiqc_files = ch_multiqc_files.mix(UMI_DEDUP_GENOME.out.stats.collect{it[1]}) - ch_multiqc_files = ch_multiqc_files.mix(UMI_DEDUP_GENOME.out.flagstat.collect{it[1]}) - ch_multiqc_files = ch_multiqc_files.mix(UMI_DEDUP_GENOME.out.idxstats.collect{it[1]}) - if (params.bam_csi_index) { - ch_genome_bam_index = UMI_DEDUP_GENOME.out.csi - } - ch_versions = ch_versions.mix(UMI_DEDUP_GENOME.out.versions) - - // Co-ordinate sort, index and run stats on transcriptome BAM - BAM_SORT_STATS_SAMTOOLS ( - ch_transcriptome_bam, - ch_transcript_fasta.map { [ [:], it ] } + BAM_DEDUP_UMI( + ch_genome_bam.join(ch_genome_bam_index, by: [0]), + ch_fasta, + params.umi_dedup_tool, + params.umitools_dedup_stats, + params.bam_csi_index, + BAM_SORT_STATS_SAMTOOLS.out.bam.join(BAM_SORT_STATS_SAMTOOLS.out.bai, by: [0]) ) - ch_transcriptome_sorted_bam = BAM_SORT_STATS_SAMTOOLS.out.bam - ch_transcriptome_sorted_bai = BAM_SORT_STATS_SAMTOOLS.out.bai - // Deduplicate transcriptome BAM file before read counting with Salmon - if (params.umi_dedup_tool == "umicollapse") { - BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_TRANSCRIPTOME ( - ch_transcriptome_sorted_bam.join(ch_transcriptome_sorted_bai, by: [0]) + ch_genome_bam = BAM_DEDUP_UMI.out.bam + ch_transcriptome_bam = BAM_DEDUP_UMI.out.bam + ch_genome_bam_index = BAM_DEDUP_UMI.out.bai + ch_versions = BAM_DEDUP_UMI.out.versions + + ch_multiqc_files = ch_multiqc_files + .mix( + BAM_DEDUP_UMI.dedup_log + .concat(BAM_DEDUP_UMI.out.stats) + .concat(BAM_DEDUP_UMI.out.flagstat) + .concat(BAM_DEDUP_UMI.out.idxstats) ) - UMI_DEDUP_TRANSCRIPTOME = BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_TRANSCRIPTOME - } else if (params.umi_dedup_tool == "umitools") { - BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_TRANSCRIPTOME ( - ch_transcriptome_sorted_bam.join(ch_transcriptome_sorted_bai, by: [0]), - params.umitools_dedup_stats - ) - UMI_DEDUP_TRANSCRIPTOME = BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_TRANSCRIPTOME - } else { - error("Unknown umi_dedup_tool '${params.umi_dedup_tool}'") - } - - // Name sort BAM before passing to Salmon - SAMTOOLS_SORT ( - UMI_DEDUP_TRANSCRIPTOME.out.bam, - ch_fasta.map { [ [:], it ] } - ) - - // Only run prepare_for_rsem.py on paired-end BAM files - SAMTOOLS_SORT - .out - .bam - .branch { - meta, bam -> - single_end: meta.single_end - return [ meta, bam ] - paired_end: !meta.single_end - return [ meta, bam ] - } - .set { ch_dedup_bam } - - // Fix paired-end reads in name sorted BAM file - // See: https://github.com/nf-core/rnaseq/issues/828 - UMITOOLS_PREPAREFORSALMON ( - ch_dedup_bam.paired_end.map { meta, bam -> [ meta, bam, [] ] } - ) - ch_versions = ch_versions.mix(UMITOOLS_PREPAREFORSALMON.out.versions.first()) - - ch_dedup_bam - .single_end - .mix(UMITOOLS_PREPAREFORSALMON.out.bam) - .set { ch_transcriptome_bam } } // diff --git a/workflows/rnaseq/nextflow.config b/workflows/rnaseq/nextflow.config index 5c51aa2a9..02d60e7e3 100644 --- a/workflows/rnaseq/nextflow.config +++ b/workflows/rnaseq/nextflow.config @@ -102,7 +102,7 @@ if (!params.skip_alignment && params.aligner == 'star_salmon') { if (params.with_umi) { process { - withName: 'NFCORE_RNASEQ:RNASEQ:SAMTOOLS_SORT' { + withName: 'NFCORE_RNASEQ:RNASEQ:BAM_DEDUP_UMI:SAMTOOLS_SORT' { ext.args = '-n' ext.prefix = { "${meta.id}.umi_dedup.transcriptome" } publishDir = [ @@ -113,7 +113,7 @@ if (!params.skip_alignment && params.aligner == 'star_salmon') { ] } - withName: 'NFCORE_RNASEQ:RNASEQ:UMITOOLS_PREPAREFORSALMON' { + withName: 'NFCORE_RNASEQ:RNASEQ:BAM_DEDUP_UMI:UMITOOLS_PREPAREFORSALMON' { ext.prefix = { "${meta.id}.umi_dedup.transcriptome.filtered" } publishDir = [ [ @@ -130,7 +130,7 @@ if (!params.skip_alignment && params.aligner == 'star_salmon') { ] } - withName: 'NFCORE_RNASEQ:RNASEQ:BAM_SORT_STATS_SAMTOOLS:SAMTOOLS_SORT' { + withName: 'NFCORE_RNASEQ:RNASEQ:BAM_DEDUP_UMI:BAM_SORT_STATS_SAMTOOLS:SAMTOOLS_SORT' { ext.prefix = { "${meta.id}.transcriptome.sorted" } publishDir = [ path: { params.save_align_intermeds || params.save_umi_intermeds ? "${params.outdir}/${params.aligner}" : params.outdir }, @@ -140,7 +140,7 @@ if (!params.skip_alignment && params.aligner == 'star_salmon') { ] } - withName: 'NFCORE_RNASEQ:RNASEQ:BAM_SORT_STATS_SAMTOOLS:SAMTOOLS_INDEX' { + withName: 'NFCORE_RNASEQ:RNASEQ:BAM_DEDUP_UMI:BAM_SORT_STATS_SAMTOOLS:SAMTOOLS_INDEX' { publishDir = [ path: { params.save_align_intermeds || params.save_umi_intermeds ? "${params.outdir}/${params.aligner}" : params.outdir }, mode: params.publish_dir_mode, @@ -149,7 +149,7 @@ if (!params.skip_alignment && params.aligner == 'star_salmon') { ] } - withName: 'NFCORE_RNASEQ:RNASEQ:BAM_SORT_STATS_SAMTOOLS:BAM_STATS_SAMTOOLS:.*' { + withName: 'NFCORE_RNASEQ:RNASEQ:BAM_DEDUP_UMI:BAM_SORT_STATS_SAMTOOLS:BAM_STATS_SAMTOOLS:.*' { ext.prefix = { "${meta.id}.transcriptome.sorted.bam" } publishDir = [ path: { params.save_align_intermeds || params.save_umi_intermeds ? "${params.outdir}/${params.aligner}/samtools_stats" : params.outdir }, From 9603f3da79fd8cc63da18d31f8479e304c8b8819 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Tue, 10 Dec 2024 16:32:20 +0000 Subject: [PATCH 057/107] Fixes with local subworkflow --- subworkflows/local/bam_dedup_umi/main.nf | 78 ++++++++++++++--------- workflows/rnaseq/main.nf | 79 ++++++++++-------------- workflows/rnaseq/nextflow.config | 10 +-- 3 files changed, 88 insertions(+), 79 deletions(-) diff --git a/subworkflows/local/bam_dedup_umi/main.nf b/subworkflows/local/bam_dedup_umi/main.nf index 3359fa7b6..a7f76f9a3 100644 --- a/subworkflows/local/bam_dedup_umi/main.nf +++ b/subworkflows/local/bam_dedup_umi/main.nf @@ -2,21 +2,23 @@ // BAM deduplication with UMI processing // -include { BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE } from '../../../subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse' -include { BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS } from '../../../subworkflows/nf-core/bam_dedup_stats_samtools_umitools' -include { BAM_SORT_STATS_SAMTOOLS } from '../../../subworkflows/nf-core/bam_sort_stats_samtools' -include { UMITOOLS_PREPAREFORRSEM } from '../../../modules/nf-core/umitools/prepareforrsem' -include { SAMTOOLS_SORT } from '../../../modules/nf-core/samtools/sort/main' +include { BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE as BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_TRANSCRIPTOME } from '../../../subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse' +include { BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS as BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_TRANSCRIPTOME } from '../../../subworkflows/nf-core/bam_dedup_stats_samtools_umitools' +include { BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE as BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_GENOME } from '../../../subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse' +include { BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS as BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME } from '../../../subworkflows/nf-core/bam_dedup_stats_samtools_umitools' +include { BAM_SORT_STATS_SAMTOOLS } from '../../../subworkflows/nf-core/bam_sort_stats_samtools' +include { UMITOOLS_PREPAREFORRSEM } from '../../../modules/nf-core/umitools/prepareforrsem' +include { SAMTOOLS_SORT } from '../../../modules/nf-core/samtools/sort/main' workflow BAM_DEDUP_UMI { take: ch_genome_bam // channel: [ val(meta), path(bam), path(bai) ] - ch_fasta // channel: [ path(fasta) ] + ch_fasta // channel: [ val(meta), path(fasta) ] umi_dedup_tool // string: 'umicollapse' or 'umitools' umitools_dedup_stats // boolean: whether to generate UMI-tools dedup stats bam_csi_index // boolean: whether to generate CSI index - ch_transcriptome_bam // - ch_transcript_fasta + ch_transcriptome_bam // channel: [ val(meta), path(bam) ] + ch_transcript_fasta // channel: [ val(meta), path(fasta) ] main: ch_versions = Channel.empty() @@ -27,18 +29,18 @@ workflow BAM_DEDUP_UMI { // Genome BAM deduplication if (umi_dedup_tool == "umicollapse") { - BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE ( + BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_GENOME ( ch_genome_bam ) - UMI_DEDUP_GENOME = BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE + UMI_DEDUP_GENOME = BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_GENOME ch_dedup_log = UMI_DEDUP_GENOME.out.dedup_stats } else if (umi_dedup_tool == "umitools") { - BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS ( + BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME ( ch_genome_bam, umitools_dedup_stats ) - UMI_DEDUP_GENOME = BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS + UMI_DEDUP_GENOME = BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME ch_dedup_log = UMI_DEDUP_GENOME.out.deduplog } @@ -51,32 +53,32 @@ workflow BAM_DEDUP_UMI { BAM_SORT_STATS_SAMTOOLS ( ch_transcriptome_bam, - ch_transcript_fasta.map { [ [:], it ] } + ch_transcript_fasta ) ch_sorted_transcriptome_bam = BAM_SORT_STATS_SAMTOOLS.out.bam .join(BAM_SORT_STATS_SAMTOOLS.out.bai) // 2. Transcriptome BAM deduplication if (umi_dedup_tool == "umicollapse") { - BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE ( + BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_TRANSCRIPTOME ( ch_sorted_transcriptome_bam ) - UMI_DEDUP_GENOME = BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE - ch_dedup_log = UMI_DEDUP_GENOME.out.dedup_stats + UMI_DEDUP_TRANSCRIPTOME = BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_TRANSCRIPTOME + ch_dedup_log = dedup_log.mix(UMI_DEDUP_GENOME.out.dedup_stats) } else if (umi_dedup_tool == "umitools") { - BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS ( + BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_TRANSCRIPTOME ( ch_sorted_transcriptome_bam, umitools_dedup_stats ) - UMI_DEDUP_GENOME = BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS - ch_dedup_log = UMI_DEDUP_GENOME.out.deduplog + UMI_DEDUP_TRANSCRIPTOME = BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_TRANSCRIPTOME + ch_dedup_log = ch_dedup_log.mix(UMI_DEDUP_GENOME.out.deduplog) } // 3. Restore name sorting SAMTOOLS_SORT ( UMI_DEDUP_TRANSCRIPTOME.out.bam, - ch_fasta.map { [ [:], it ] } + ch_fasta ) // 4. Run prepare_for_rsem.py on paired-end BAM files @@ -91,28 +93,46 @@ workflow BAM_DEDUP_UMI { return [ meta, bam ] } - UMITOOLS_PREPAREFORSALMON ( + UMITOOLS_PREPAREFORRSEM ( ended_transcriptome_dedup_bam.paired_end .map { meta, bam -> [ meta, bam, [] ] } ) - ch_dedup_transcriptome_bam = ch_transcriptome_bam - .single_end - .mix(UMITOOLS_PREPAREFORSALMON.out.bam) + ch_dedup_transcriptome_bam = ended_transcriptome_dedup_bam.single_end + .mix(UMITOOLS_PREPAREFORRSEM.out.bam) + + // Collect files useful for MultiQC into one helpful emission + + ch_stats = UMI_DEDUP_GENOME.out.stats + .mix(UMI_DEDUP_TRANSCRIPTOME.out.stats) + + ch_flagstat = UMI_DEDUP_GENOME.out.flagstat + .mix(UMI_DEDUP_TRANSCRIPTOME.out.flagstat) + + ch_idxstats = UMI_DEDUP_GENOME.out.idxstats + .mix(UMI_DEDUP_TRANSCRIPTOME.out.idxstats) + + ch_multiqc_files = ch_dedup_log + .mix(ch_stats) + .mix(ch_flagstat) + .mix(ch_idxstats) + .transpose() + .map{it[1]} // Record versions ch_versions = UMI_DEDUP_GENOME.out.versions .mix(BAM_SORT_STATS_SAMTOOLS.out.versions) - .mix(UMITOOLS_PREPAREFORSALMON.out.versions) + .mix(UMITOOLS_PREPAREFORRSEM.out.versions) emit: bam = UMI_DEDUP_GENOME.out.bam // channel: [ val(meta), path(bam) ] - bam_index = bam_csi_index ? UMI_DEDUP_GENOME.out.csi : UMI_DEDUP_GENOME.out.bai // channel: [ val(meta), path(bai) ] + bai = bam_csi_index ? UMI_DEDUP_GENOME.out.csi : UMI_DEDUP_GENOME.out.bai // channel: [ val(meta), path(bai) ] dedup_log = ch_dedup_log // channel: [ val(meta), path(log) ] - stats = UMI_DEDUP_GENOME.out.stats - flagstat = UMI_DEDUP_GENOME.out.flagstat - idxstats = UMI_DEDUP_GENOME.out.idxstats + stats = ch_stats + flagstat = ch_flagstat + idxstats = ch_idxstats + multiqc_files = ch_multiqc_files transcriptome_bam = ch_dedup_transcriptome_bam // channel: [ val(meta), path(bam) ] versions = ch_versions // channel: [ path(versions.yml) ] } diff --git a/workflows/rnaseq/main.nf b/workflows/rnaseq/main.nf index 4328ec3a7..6db5dfa15 100755 --- a/workflows/rnaseq/main.nf +++ b/workflows/rnaseq/main.nf @@ -15,9 +15,11 @@ include { MULTIQC_CUSTOM_BIOTYPE } from '../../modules/local/multiqc // // SUBWORKFLOW: Consisting of a mix of local and nf-core/modules // -include { ALIGN_STAR } from '../../subworkflows/local/align_star' -include { QUANTIFY_RSEM } from '../../subworkflows/local/quantify_rsem' -include { BAM_DEDUP_UMI } from '../../subworkflows/local/bam_dedup_umi' +include { ALIGN_STAR } from '../../subworkflows/local/align_star' +include { QUANTIFY_RSEM } from '../../subworkflows/local/quantify_rsem' +include { BAM_DEDUP_UMI as BAM_DEDUP_UMI_STAR } from '../../subworkflows/local/bam_dedup_umi' +include { BAM_DEDUP_UMI as BAM_DEDUP_UMI_HISAT } from '../../subworkflows/local/bam_dedup_umi' + include { checkSamplesAfterGrouping } from '../../subworkflows/local/utils_nfcore_rnaseq_pipeline' include { multiqcTsvFromList } from '../../subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness' include { getStarPercentMapped } from '../../subworkflows/local/utils_nfcore_rnaseq_pipeline' @@ -35,7 +37,6 @@ include { methodsDescriptionText } from '../../subworkflows/local/utils_ // MODULE: Installed directly from nf-core/modules // include { DUPRADAR } from '../../modules/nf-core/dupradar' -include { SAMTOOLS_SORT } from '../../modules/nf-core/samtools/sort' include { PRESEQ_LCEXTRAP } from '../../modules/nf-core/preseq/lcextrap' include { QUALIMAP_RNASEQ } from '../../modules/nf-core/qualimap/rnaseq' include { STRINGTIE_STRINGTIE } from '../../modules/nf-core/stringtie/stringtie' @@ -43,7 +44,6 @@ include { SUBREAD_FEATURECOUNTS } from '../../modules/nf-core/subread/featu include { KRAKEN2_KRAKEN2 as KRAKEN2 } from '../../modules/nf-core/kraken2/kraken2/main' include { BRACKEN_BRACKEN as BRACKEN } from '../../modules/nf-core/bracken/bracken/main' include { MULTIQC } from '../../modules/nf-core/multiqc' -include { UMITOOLS_PREPAREFORRSEM as UMITOOLS_PREPAREFORSALMON } from '../../modules/nf-core/umitools/prepareforrsem' include { BEDTOOLS_GENOMECOV as BEDTOOLS_GENOMECOV_FW } from '../../modules/nf-core/bedtools/genomecov' include { BEDTOOLS_GENOMECOV as BEDTOOLS_GENOMECOV_REV } from '../../modules/nf-core/bedtools/genomecov' @@ -220,27 +220,23 @@ workflow RNASEQ { // if (params.with_umi) { - BAM_DEDUP_UMI( + BAM_DEDUP_UMI_STAR( ch_genome_bam.join(ch_genome_bam_index, by: [0]), - ch_fasta, + ch_fasta.map { [ [:], it ] }, params.umi_dedup_tool, params.umitools_dedup_stats, params.bam_csi_index, - BAM_SORT_STATS_SAMTOOLS.out.bam.join(BAM_SORT_STATS_SAMTOOLS.out.bai, by: [0]) + ch_transcriptome_bam, + ch_transcript_fasta.map { [ [:], it ] } ) - ch_genome_bam = BAM_DEDUP_UMI.out.bam - ch_transcriptome_bam = BAM_DEDUP_UMI.out.bam - ch_genome_bam_index = BAM_DEDUP_UMI.out.bai - ch_versions = BAM_DEDUP_UMI.out.versions - - ch_multiqc_files = ch_multiqc_files - .mix( - BAM_DEDUP_UMI.dedup_log - .concat(BAM_DEDUP_UMI.out.stats) - .concat(BAM_DEDUP_UMI.out.flagstat) - .concat(BAM_DEDUP_UMI.out.idxstats) - ) + ch_genome_bam = BAM_DEDUP_UMI_STAR.out.bam + ch_transcriptome_bam = BAM_DEDUP_UMI_STAR.out.transcriptome_bam + ch_genome_bam_index = BAM_DEDUP_UMI_STAR.out.bai + ch_versions = BAM_DEDUP_UMI_STAR.out.versions + + ch_multiqc_files = ch_multiqc_files + .mix(BAM_DEDUP_UMI_STAR.out.multiqc_files) } // @@ -335,32 +331,25 @@ workflow RNASEQ { // // SUBWORKFLOW: Remove duplicate reads from BAM file based on UMIs // + if (params.with_umi) { - if (params.umi_dedup_tool == "umicollapse") { - BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_GENOME ( - ch_genome_bam.join(ch_genome_bam_index, by: [0]), - ) - UMI_DEDUP_GENOME = BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_GENOME - ch_multiqc_files = ch_multiqc_files.mix(UMI_DEDUP_GENOME.out.dedup_stats.collect{it[1]}.ifEmpty([])) - } else if (params.umi_dedup_tool == "umitools") { - BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME ( - ch_genome_bam.join(ch_genome_bam_index, by: [0]), - params.umitools_dedup_stats - ) - UMI_DEDUP_GENOME = BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME - ch_multiqc_files = ch_multiqc_files.mix(UMI_DEDUP_GENOME.out.deduplog.collect{it[1]}) - } else { - error("Unknown umi_dedup_tool '${params.umi_dedup_tool}'") - } - ch_genome_bam = UMI_DEDUP_GENOME.out.bam - ch_genome_bam_index = UMI_DEDUP_GENOME.out.bai - ch_multiqc_files = ch_multiqc_files.mix(UMI_DEDUP_GENOME.out.stats.collect{it[1]}) - ch_multiqc_files = ch_multiqc_files.mix(UMI_DEDUP_GENOME.out.flagstat.collect{it[1]}) - ch_multiqc_files = ch_multiqc_files.mix(UMI_DEDUP_GENOME.out.idxstats.collect{it[1]}) - if (params.bam_csi_index) { - ch_genome_bam_index = UMI_DEDUP_GENOME.out.csi - } - ch_versions = ch_versions.mix(UMI_DEDUP_GENOME.out.versions) + + BAM_DEDUP_UMI_HISAT2( + ch_genome_bam.join(ch_genome_bam_index, by: [0]), + ch_fasta.map { [ [:], it ] }, + params.umi_dedup_tool, + params.umitools_dedup_stats, + params.bam_csi_index, + [[],[]], + [[],[]] + ) + + ch_genome_bam = BAM_DEDUP_UMI_HISAT2.out.bam + ch_genome_bam_index = BAM_DEDUP_UMI_HISAT2.out.bai + ch_versions = BAM_DEDUP_UMI_HISAT2.out.versions + + ch_multiqc_files = ch_multiqc_files + .mix(BAM_DEDUP_UMI_HISAT2.out.multiqc_files) } } diff --git a/workflows/rnaseq/nextflow.config b/workflows/rnaseq/nextflow.config index 02d60e7e3..2eef40849 100644 --- a/workflows/rnaseq/nextflow.config +++ b/workflows/rnaseq/nextflow.config @@ -102,7 +102,7 @@ if (!params.skip_alignment && params.aligner == 'star_salmon') { if (params.with_umi) { process { - withName: 'NFCORE_RNASEQ:RNASEQ:BAM_DEDUP_UMI:SAMTOOLS_SORT' { + withName: 'NFCORE_RNASEQ:RNASEQ:BAM_DEDUP_UMI_(STAR|HISAT2):SAMTOOLS_SORT' { ext.args = '-n' ext.prefix = { "${meta.id}.umi_dedup.transcriptome" } publishDir = [ @@ -113,7 +113,7 @@ if (!params.skip_alignment && params.aligner == 'star_salmon') { ] } - withName: 'NFCORE_RNASEQ:RNASEQ:BAM_DEDUP_UMI:UMITOOLS_PREPAREFORSALMON' { + withName: 'NFCORE_RNASEQ:RNASEQ:BAM_DEDUP_UMI_(STAR|HISAT2):UMITOOLS_PREPAREFORRSEM' { ext.prefix = { "${meta.id}.umi_dedup.transcriptome.filtered" } publishDir = [ [ @@ -130,7 +130,7 @@ if (!params.skip_alignment && params.aligner == 'star_salmon') { ] } - withName: 'NFCORE_RNASEQ:RNASEQ:BAM_DEDUP_UMI:BAM_SORT_STATS_SAMTOOLS:SAMTOOLS_SORT' { + withName: 'NFCORE_RNASEQ:RNASEQ:BAM_DEDUP_UMI_(STAR|HISAT2):BAM_SORT_STATS_SAMTOOLS:SAMTOOLS_SORT' { ext.prefix = { "${meta.id}.transcriptome.sorted" } publishDir = [ path: { params.save_align_intermeds || params.save_umi_intermeds ? "${params.outdir}/${params.aligner}" : params.outdir }, @@ -140,7 +140,7 @@ if (!params.skip_alignment && params.aligner == 'star_salmon') { ] } - withName: 'NFCORE_RNASEQ:RNASEQ:BAM_DEDUP_UMI:BAM_SORT_STATS_SAMTOOLS:SAMTOOLS_INDEX' { + withName: 'NFCORE_RNASEQ:RNASEQ:BAM_DEDUP_UMI_(STAR|HISAT2):BAM_SORT_STATS_SAMTOOLS:SAMTOOLS_INDEX' { publishDir = [ path: { params.save_align_intermeds || params.save_umi_intermeds ? "${params.outdir}/${params.aligner}" : params.outdir }, mode: params.publish_dir_mode, @@ -149,7 +149,7 @@ if (!params.skip_alignment && params.aligner == 'star_salmon') { ] } - withName: 'NFCORE_RNASEQ:RNASEQ:BAM_DEDUP_UMI:BAM_SORT_STATS_SAMTOOLS:BAM_STATS_SAMTOOLS:.*' { + withName: 'NFCORE_RNASEQ:RNASEQ:BAM_DEDUP_UMI_(STAR|HISAT2):BAM_SORT_STATS_SAMTOOLS:BAM_STATS_SAMTOOLS:.*' { ext.prefix = { "${meta.id}.transcriptome.sorted.bam" } publishDir = [ path: { params.save_align_intermeds || params.save_umi_intermeds ? "${params.outdir}/${params.aligner}/samtools_stats" : params.outdir }, From d2fd88515d70ab1736b06f2382688628951cbcaa Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Tue, 10 Dec 2024 16:34:05 +0000 Subject: [PATCH 058/107] update changelog --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index c5c417dd3..488ab2da7 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -17,6 +17,7 @@ Special thanks to the following for their contributions to the release: - [PR #1369](https://github.com/nf-core/rnaseq/pull/1369) - Add umicollapse as an alternative to umi-tools - [PR #1461](https://github.com/nf-core/rnaseq/pull/1461) - Add FASTQ linting during preprocessing - [PR #1463](https://github.com/nf-core/rnaseq/pull/1463) - Move channel operations outside of the onComplete() block +- [PR #1466](https://github.com/nf-core/rnaseq/pull/1466) - Factor out UMI handling ### Software dependencies From e0244fe3a6f8247a722cab674b7b09d3d8dfb4bf Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Tue, 10 Dec 2024 18:40:33 +0000 Subject: [PATCH 059/107] Add UMI tests --- tests/umi.nf.test | 73 ++ tests/umi.nf.test.snap | 1629 ++++++++++++++++++++++++++++++++++++++++ 2 files changed, 1702 insertions(+) create mode 100644 tests/umi.nf.test create mode 100644 tests/umi.nf.test.snap diff --git a/tests/umi.nf.test b/tests/umi.nf.test new file mode 100644 index 000000000..3764ac8bd --- /dev/null +++ b/tests/umi.nf.test @@ -0,0 +1,73 @@ +nextflow_pipeline { + + name "Test pipeline with dummy UMI settings" + script "../main.nf" + + test("Params: Dummy UMI setting") { + + when { + params { + with_umi = true + umitools_extract_method = "regex" + umitools_bc_pattern = "^(?PCGA.{8}){s<=2}.*" + umitools_dedup_stats = true + outdir = "$outputDir" + } + } + + then { + // stable_name: All files + folders in ${params.outdir}/ with a stable name + def stable_name = getAllFilesFromDir(params.outdir, relative: true, includeDir: true, ignore: ['pipeline_info/*.{html,json,txt}']) + // stable_path: All files in ${params.outdir}/ with stable content + def stable_path = getAllFilesFromDir(params.outdir, ignoreFile: 'tests/.nftignore') + assertAll( + { assert workflow.success}, + { assert snapshot( + // Number of successful tasks + workflow.trace.succeeded().size(), + // pipeline versions.yml file for multiqc from which Nextflow version is removed because we tests pipelines on multiple Nextflow versions + removeNextflowVersion("$outputDir/pipeline_info/nf_core_rnaseq_software_mqc_versions.yml"), + // All stable path name, with a relative path + stable_name, + // All files with stable contents + stable_path + ).match() } + ) + } + } + + test("Params: Dummy UMI settings - stub") { + + options "-stub" + + when { + params { + with_umi = true + umitools_extract_method = "regex" + umitools_bc_pattern = "^(?PCGA.{8}){s<=2}.*" + umitools_dedup_stats = true + outdir = "$outputDir" + } + } + + then { + // stable_name: All files + folders in ${params.outdir}/ with a stable name + def stable_name = getAllFilesFromDir(params.outdir, relative: true, includeDir: true, ignore: ['pipeline_info/*.{html,json,txt}']) + // stable_path: All files in ${params.outdir}/ with stable content + def stable_path = getAllFilesFromDir(params.outdir, ignoreFile: 'tests/.nftignore') + assertAll( + { assert workflow.success}, + { assert snapshot( + // Number of successful tasks + workflow.trace.succeeded().size(), + // pipeline versions.yml file for multiqc from which Nextflow version is removed because we tests pipelines on multiple Nextflow versions + removeNextflowVersion("$outputDir/pipeline_info/nf_core_rnaseq_software_mqc_versions.yml"), + // All stable path name, with a relative path + stable_name, + // All files with stable contents + stable_path + ).match() } + ) + } + } +} diff --git a/tests/umi.nf.test.snap b/tests/umi.nf.test.snap new file mode 100644 index 000000000..407127426 --- /dev/null +++ b/tests/umi.nf.test.snap @@ -0,0 +1,1629 @@ +{ + "Params: Dummy UMI settings - stub": { + "content": [ + 32, + { + "BBMAP_BBSPLIT": { + "bbmap": 39.1 + }, + "CAT_FASTQ": { + "cat": 9.5 + }, + "CUSTOM_CATADDITIONALFASTA": { + "python": null + }, + "CUSTOM_GETCHROMSIZES": { + "getchromsizes": 1.21 + }, + "FASTQC": { + "fastqc": "0.12.1" + }, + "FQ_LINT": { + "fq": "0.12.0 (2024-07-08)" + }, + "GTF2BED": { + "perl": "5.26.2" + }, + "GTF_FILTER": { + "python": "3.9.5" + }, + "GUNZIP_ADDITIONAL_FASTA": { + "gunzip": 1.1 + }, + "GUNZIP_GTF": { + "gunzip": 1.1 + }, + "STAR_GENOMEGENERATE": { + "star": "2.7.11b", + "samtools": 1.2, + "gawk": "5.1.0" + }, + "TRIMGALORE": { + "trimgalore": "0.6.10", + "cutadapt": 4.9 + }, + "UMITOOLS_EXTRACT": { + "umitools": "1.1.5" + }, + "UNTAR_SALMON_INDEX": { + "untar": 1.34 + }, + "Workflow": { + "nf-core/rnaseq": "v3.18.0dev" + } + }, + [ + "custom", + "custom/out", + "custom/out/genome_transcriptome.fasta", + "custom/out/genome_transcriptome.gtf", + "fastqc", + "fastqc/raw", + "fastqc/raw/RAP1_IAA_30M_REP1_raw.html", + "fastqc/raw/RAP1_IAA_30M_REP1_raw.zip", + "fastqc/raw/RAP1_UNINDUCED_REP1_raw.html", + "fastqc/raw/RAP1_UNINDUCED_REP1_raw.zip", + "fastqc/raw/RAP1_UNINDUCED_REP2_raw.html", + "fastqc/raw/RAP1_UNINDUCED_REP2_raw.zip", + "fastqc/raw/WT_REP1_raw.html", + "fastqc/raw/WT_REP1_raw.zip", + "fastqc/raw/WT_REP2_raw.html", + "fastqc/raw/WT_REP2_raw.zip", + "fastqc/trim", + "fq_lint", + "fq_lint/raw", + "fq_lint/raw/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/raw/WT_REP1.fq_lint.txt", + "fq_lint/raw/WT_REP2.fq_lint.txt", + "multiqc", + "multiqc/star_salmon", + "multiqc/star_salmon/multiqc_data", + "multiqc/star_salmon/multiqc_plots", + "multiqc/star_salmon/multiqc_report.html", + "pipeline_info", + "pipeline_info/nf_core_rnaseq_software_mqc_versions.yml", + "trimgalore", + "trimgalore/RAP1_IAA_30M_REP1_trimmed_1.fastq.gz_trimming_report.txt", + "trimgalore/RAP1_IAA_30M_REP1_trimmed_2.fastq.gz_trimming_report.txt", + "trimgalore/RAP1_UNINDUCED_REP1_trimmed.fastq.gz_trimming_report.txt", + "trimgalore/RAP1_UNINDUCED_REP2_trimmed.fastq.gz_trimming_report.txt", + "trimgalore/WT_REP1_trimmed_1.fastq.gz_trimming_report.txt", + "trimgalore/WT_REP1_trimmed_2.fastq.gz_trimming_report.txt", + "trimgalore/WT_REP2_trimmed_1.fastq.gz_trimming_report.txt", + "trimgalore/WT_REP2_trimmed_2.fastq.gz_trimming_report.txt", + "umitools", + "umitools/RAP1_IAA_30M_REP1.umi_extract.log", + "umitools/RAP1_UNINDUCED_REP1.umi_extract.log", + "umitools/RAP1_UNINDUCED_REP2.umi_extract.log", + "umitools/WT_REP1.umi_extract.log", + "umitools/WT_REP2.umi_extract.log" + ], + [ + "genome_transcriptome.fasta:md5,d41d8cd98f00b204e9800998ecf8427e", + "genome_transcriptome.gtf:md5,d41d8cd98f00b204e9800998ecf8427e", + "RAP1_IAA_30M_REP1.umi_extract.log:md5,d41d8cd98f00b204e9800998ecf8427e", + "RAP1_UNINDUCED_REP1.umi_extract.log:md5,d41d8cd98f00b204e9800998ecf8427e", + "RAP1_UNINDUCED_REP2.umi_extract.log:md5,d41d8cd98f00b204e9800998ecf8427e", + "WT_REP1.umi_extract.log:md5,d41d8cd98f00b204e9800998ecf8427e", + "WT_REP2.umi_extract.log:md5,d41d8cd98f00b204e9800998ecf8427e" + ] + ], + "meta": { + "nf-test": "0.9.0", + "nextflow": "24.10.2" + }, + "timestamp": "2024-12-10T18:39:20.510781783" + }, + "Params: Dummy UMI setting": { + "content": [ + 276, + { + "BBMAP_BBSPLIT": { + "bbmap": 39.1 + }, + "BEDTOOLS_GENOMECOV_FW": { + "bedtools": "2.31.1" + }, + "CAT_FASTQ": { + "cat": 9.5 + }, + "CUSTOM_CATADDITIONALFASTA": { + "python": "3.12.2" + }, + "CUSTOM_GETCHROMSIZES": { + "getchromsizes": 1.21 + }, + "CUSTOM_TX2GENE": { + "python": "3.10.4" + }, + "DESEQ2_QC_PSEUDO": { + "r-base": "4.0.3", + "bioconductor-deseq2": "1.28.0" + }, + "DESEQ2_QC_STAR_SALMON": { + "r-base": "4.0.3", + "bioconductor-deseq2": "1.28.0" + }, + "DUPRADAR": { + "bioconductor-dupradar": "1.32.0" + }, + "FASTQC": { + "fastqc": "0.12.1" + }, + "FQ_LINT": { + "fq": "0.12.0 (2024-07-08)" + }, + "FQ_SUBSAMPLE": { + "fq": "0.12.0 (2024-07-08)" + }, + "GTF2BED": { + "perl": "5.26.2" + }, + "GTF_FILTER": { + "python": "3.9.5" + }, + "GUNZIP_ADDITIONAL_FASTA": { + "gunzip": 1.1 + }, + "GUNZIP_GTF": { + "gunzip": 1.1 + }, + "MULTIQC_CUSTOM_BIOTYPE": { + "python": "3.9.5" + }, + "QUALIMAP_RNASEQ": { + "qualimap": 2.3 + }, + "RSEQC_BAMSTAT": { + "rseqc": "5.0.2" + }, + "RSEQC_INFEREXPERIMENT": { + "rseqc": "5.0.2" + }, + "RSEQC_INNERDISTANCE": { + "rseqc": "5.0.2" + }, + "RSEQC_JUNCTIONANNOTATION": { + "rseqc": "5.0.2" + }, + "RSEQC_JUNCTIONSATURATION": { + "rseqc": "5.0.2" + }, + "RSEQC_READDISTRIBUTION": { + "rseqc": "5.0.2" + }, + "RSEQC_READDUPLICATION": { + "rseqc": "5.0.2" + }, + "SALMON_QUANT": { + "salmon": "1.10.3" + }, + "SAMTOOLS_FLAGSTAT": { + "samtools": 1.21 + }, + "SAMTOOLS_IDXSTATS": { + "samtools": 1.21 + }, + "SAMTOOLS_INDEX": { + "samtools": 1.21 + }, + "SAMTOOLS_SORT": { + "samtools": 1.21 + }, + "SAMTOOLS_STATS": { + "samtools": 1.21 + }, + "SE_GENE": { + "bioconductor-summarizedexperiment": "1.32.0" + }, + "STAR_ALIGN": { + "star": "2.7.11b", + "samtools": 1.2, + "gawk": "5.1.0" + }, + "STAR_GENOMEGENERATE": { + "star": "2.7.11b", + "samtools": 1.2, + "gawk": "5.1.0" + }, + "STRINGTIE_STRINGTIE": { + "stringtie": "2.2.3" + }, + "SUBREAD_FEATURECOUNTS": { + "subread": "2.0.6" + }, + "TRIMGALORE": { + "trimgalore": "0.6.10", + "cutadapt": 4.9 + }, + "TXIMETA_TXIMPORT": { + "bioconductor-tximeta": "1.20.1" + }, + "UCSC_BEDCLIP": { + "ucsc": 377 + }, + "UCSC_BEDGRAPHTOBIGWIG": { + "ucsc": 469 + }, + "UMITOOLS_DEDUP": { + "umitools": "1.1.5" + }, + "UMITOOLS_EXTRACT": { + "umitools": "1.1.5" + }, + "UMITOOLS_PREPAREFORSALMON": { + "umitools": "1.1.5" + }, + "UNTAR_SALMON_INDEX": { + "untar": 1.34 + }, + "Workflow": { + "nf-core/rnaseq": "v3.18.0dev" + } + }, + [ + "bbsplit", + "bbsplit/RAP1_IAA_30M_REP1.stats.txt", + "bbsplit/RAP1_UNINDUCED_REP1.stats.txt", + "bbsplit/RAP1_UNINDUCED_REP2.stats.txt", + "bbsplit/WT_REP1.stats.txt", + "bbsplit/WT_REP2.stats.txt", + "custom", + "custom/out", + "custom/out/genome_gfp.fasta", + "custom/out/genome_gfp.gtf", + "fastqc", + "fastqc/raw", + "fastqc/raw/RAP1_IAA_30M_REP1_raw_1_fastqc.html", + "fastqc/raw/RAP1_IAA_30M_REP1_raw_1_fastqc.zip", + "fastqc/raw/RAP1_IAA_30M_REP1_raw_2_fastqc.html", + "fastqc/raw/RAP1_IAA_30M_REP1_raw_2_fastqc.zip", + "fastqc/raw/RAP1_UNINDUCED_REP1_raw_fastqc.html", + "fastqc/raw/RAP1_UNINDUCED_REP1_raw_fastqc.zip", + "fastqc/raw/RAP1_UNINDUCED_REP2_raw_fastqc.html", + "fastqc/raw/RAP1_UNINDUCED_REP2_raw_fastqc.zip", + "fastqc/raw/WT_REP1_raw_1_fastqc.html", + "fastqc/raw/WT_REP1_raw_1_fastqc.zip", + "fastqc/raw/WT_REP1_raw_2_fastqc.html", + "fastqc/raw/WT_REP1_raw_2_fastqc.zip", + "fastqc/raw/WT_REP2_raw_1_fastqc.html", + "fastqc/raw/WT_REP2_raw_1_fastqc.zip", + "fastqc/raw/WT_REP2_raw_2_fastqc.html", + "fastqc/raw/WT_REP2_raw_2_fastqc.zip", + "fastqc/trim", + "fastqc/trim/RAP1_IAA_30M_REP1_trimmed_1_val_1_fastqc.html", + "fastqc/trim/RAP1_IAA_30M_REP1_trimmed_1_val_1_fastqc.zip", + "fastqc/trim/RAP1_IAA_30M_REP1_trimmed_2_val_2_fastqc.html", + "fastqc/trim/RAP1_IAA_30M_REP1_trimmed_2_val_2_fastqc.zip", + "fastqc/trim/RAP1_UNINDUCED_REP1_trimmed_trimmed_fastqc.html", + "fastqc/trim/RAP1_UNINDUCED_REP1_trimmed_trimmed_fastqc.zip", + "fastqc/trim/RAP1_UNINDUCED_REP2_trimmed_trimmed_fastqc.html", + "fastqc/trim/RAP1_UNINDUCED_REP2_trimmed_trimmed_fastqc.zip", + "fastqc/trim/WT_REP1_trimmed_1_val_1_fastqc.html", + "fastqc/trim/WT_REP1_trimmed_1_val_1_fastqc.zip", + "fastqc/trim/WT_REP1_trimmed_2_val_2_fastqc.html", + "fastqc/trim/WT_REP1_trimmed_2_val_2_fastqc.zip", + "fastqc/trim/WT_REP2_trimmed_1_val_1_fastqc.html", + "fastqc/trim/WT_REP2_trimmed_1_val_1_fastqc.zip", + "fastqc/trim/WT_REP2_trimmed_2_val_2_fastqc.html", + "fastqc/trim/WT_REP2_trimmed_2_val_2_fastqc.zip", + "fq_lint", + "fq_lint/raw", + "fq_lint/raw/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/raw/WT_REP1.fq_lint.txt", + "fq_lint/raw/WT_REP2.fq_lint.txt", + "fq_lint/sortmerna", + "fq_lint/sortmerna/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/sortmerna/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/sortmerna/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/sortmerna/WT_REP1.fq_lint.txt", + "fq_lint/sortmerna/WT_REP2.fq_lint.txt", + "fq_lint/trimmed", + "fq_lint/trimmed/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/trimmed/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/trimmed/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/trimmed/WT_REP1.fq_lint.txt", + "fq_lint/trimmed/WT_REP2.fq_lint.txt", + "multiqc", + "multiqc/star_salmon", + "multiqc/star_salmon/multiqc_report.html", + "multiqc/star_salmon/multiqc_report_data", + "multiqc/star_salmon/multiqc_report_data/cutadapt_filtered_reads_plot.txt", + "multiqc/star_salmon/multiqc_report_data/cutadapt_trimmed_sequences_plot_3_Counts.txt", + "multiqc/star_salmon/multiqc_report_data/cutadapt_trimmed_sequences_plot_3_Obs_Exp.txt", + "multiqc/star_salmon/multiqc_report_data/fastqc_raw-status-check-heatmap.txt", + "multiqc/star_salmon/multiqc_report_data/fastqc_raw_adapter_content_plot.txt", + "multiqc/star_salmon/multiqc_report_data/fastqc_raw_overrepresented_sequences_plot.txt", + "multiqc/star_salmon/multiqc_report_data/fastqc_raw_per_base_n_content_plot.txt", + "multiqc/star_salmon/multiqc_report_data/fastqc_raw_per_base_sequence_quality_plot.txt", + "multiqc/star_salmon/multiqc_report_data/fastqc_raw_per_sequence_gc_content_plot_Counts.txt", + "multiqc/star_salmon/multiqc_report_data/fastqc_raw_per_sequence_gc_content_plot_Percentages.txt", + "multiqc/star_salmon/multiqc_report_data/fastqc_raw_per_sequence_quality_scores_plot.txt", + "multiqc/star_salmon/multiqc_report_data/fastqc_raw_sequence_counts_plot.txt", + "multiqc/star_salmon/multiqc_report_data/fastqc_raw_sequence_duplication_levels_plot.txt", + "multiqc/star_salmon/multiqc_report_data/fastqc_raw_top_overrepresented_sequences_table.txt", + "multiqc/star_salmon/multiqc_report_data/fastqc_sequence_length_distribution_plot.txt", + "multiqc/star_salmon/multiqc_report_data/fastqc_trimmed-status-check-heatmap.txt", + "multiqc/star_salmon/multiqc_report_data/fastqc_trimmed_overrepresented_sequences_plot.txt", + "multiqc/star_salmon/multiqc_report_data/fastqc_trimmed_per_base_n_content_plot.txt", + "multiqc/star_salmon/multiqc_report_data/fastqc_trimmed_per_base_sequence_quality_plot.txt", + "multiqc/star_salmon/multiqc_report_data/fastqc_trimmed_per_sequence_gc_content_plot_Counts.txt", + "multiqc/star_salmon/multiqc_report_data/fastqc_trimmed_per_sequence_gc_content_plot_Percentages.txt", + "multiqc/star_salmon/multiqc_report_data/fastqc_trimmed_per_sequence_quality_scores_plot.txt", + "multiqc/star_salmon/multiqc_report_data/fastqc_trimmed_sequence_counts_plot.txt", + "multiqc/star_salmon/multiqc_report_data/fastqc_trimmed_sequence_duplication_levels_plot.txt", + "multiqc/star_salmon/multiqc_report_data/fastqc_trimmed_top_overrepresented_sequences_table.txt", + "multiqc/star_salmon/multiqc_report_data/junction_saturation_known.txt", + "multiqc/star_salmon/multiqc_report_data/junction_saturation_novel.txt", + "multiqc/star_salmon/multiqc_report_data/multiqc.log", + "multiqc/star_salmon/multiqc_report_data/multiqc_citations.txt", + "multiqc/star_salmon/multiqc_report_data/multiqc_cutadapt.txt", + "multiqc/star_salmon/multiqc_report_data/multiqc_data.json", + "multiqc/star_salmon/multiqc_report_data/multiqc_dupradar.txt", + "multiqc/star_salmon/multiqc_report_data/multiqc_fail_strand_check_table.txt", + "multiqc/star_salmon/multiqc_report_data/multiqc_fastqc_fastqc_raw.txt", + "multiqc/star_salmon/multiqc_report_data/multiqc_fastqc_fastqc_trimmed.txt", + "multiqc/star_salmon/multiqc_report_data/multiqc_featurecounts_biotype_plot.txt", + "multiqc/star_salmon/multiqc_report_data/multiqc_general_stats.txt", + "multiqc/star_salmon/multiqc_report_data/multiqc_rseqc_bam_stat.txt", + "multiqc/star_salmon/multiqc_report_data/multiqc_rseqc_infer_experiment.txt", + "multiqc/star_salmon/multiqc_report_data/multiqc_rseqc_junction_annotation.txt", + "multiqc/star_salmon/multiqc_report_data/multiqc_rseqc_read_distribution.txt", + "multiqc/star_salmon/multiqc_report_data/multiqc_salmon.txt", + "multiqc/star_salmon/multiqc_report_data/multiqc_sample-relationships.txt", + "multiqc/star_salmon/multiqc_report_data/multiqc_sample-relationships_1.txt", + "multiqc/star_salmon/multiqc_report_data/multiqc_sample-relationships_2.txt", + "multiqc/star_salmon/multiqc_report_data/multiqc_sample-relationships_3.txt", + "multiqc/star_salmon/multiqc_report_data/multiqc_samtools_flagstat.txt", + "multiqc/star_salmon/multiqc_report_data/multiqc_samtools_idxstats.txt", + "multiqc/star_salmon/multiqc_report_data/multiqc_samtools_stats.txt", + "multiqc/star_salmon/multiqc_report_data/multiqc_software_versions.txt", + "multiqc/star_salmon/multiqc_report_data/multiqc_sources.txt", + "multiqc/star_salmon/multiqc_report_data/multiqc_star.txt", + "multiqc/star_salmon/multiqc_report_data/multiqc_umitools_dedup.txt", + "multiqc/star_salmon/multiqc_report_data/multiqc_umitools_extract.txt", + "multiqc/star_salmon/multiqc_report_data/qualimap_gene_coverage_profile_Counts.txt", + "multiqc/star_salmon/multiqc_report_data/qualimap_gene_coverage_profile_Normalised.txt", + "multiqc/star_salmon/multiqc_report_data/qualimap_genomic_origin.txt", + "multiqc/star_salmon/multiqc_report_data/qualimap_rnaseq_cov_hist.txt", + "multiqc/star_salmon/multiqc_report_data/qualimap_rnaseq_genome_results.txt", + "multiqc/star_salmon/multiqc_report_data/rseqc_bam_stat.txt", + "multiqc/star_salmon/multiqc_report_data/rseqc_infer_experiment_plot.txt", + "multiqc/star_salmon/multiqc_report_data/rseqc_inner_distance.txt", + "multiqc/star_salmon/multiqc_report_data/rseqc_inner_distance_plot_Counts.txt", + "multiqc/star_salmon/multiqc_report_data/rseqc_inner_distance_plot_Percentages.txt", + "multiqc/star_salmon/multiqc_report_data/rseqc_junction_annotation_junctions_plot_Events.txt", + "multiqc/star_salmon/multiqc_report_data/rseqc_junction_annotation_junctions_plot_Junctions.txt", + "multiqc/star_salmon/multiqc_report_data/rseqc_junction_saturation_all.txt", + "multiqc/star_salmon/multiqc_report_data/rseqc_junction_saturation_plot_All_Junctions.txt", + "multiqc/star_salmon/multiqc_report_data/rseqc_junction_saturation_plot_Known_Junctions.txt", + "multiqc/star_salmon/multiqc_report_data/rseqc_junction_saturation_plot_Novel_Junctions.txt", + "multiqc/star_salmon/multiqc_report_data/rseqc_read_distribution_plot.txt", + "multiqc/star_salmon/multiqc_report_data/rseqc_read_dups.txt", + 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"i_data.ctab:md5,3d489da8244fc4d1fba0d0442220bf2d", + "tx2gene.tsv:md5,0e2418a69d2eba45097ebffc2f700bfe", + "RAP1_IAA_30M_REP1.umi_dedup.sorted_edit_distance.tsv:md5,620d5a5928e7739492de3bf66d149db2", + "RAP1_IAA_30M_REP1.umi_dedup.sorted_per_umi.tsv:md5,9c68e481bc942cfbf5cf51039238c269", + "RAP1_IAA_30M_REP1.umi_dedup.sorted_per_umi_per_position.tsv:md5,f37b7f5a783425506b434c9e5ff90f25", + "RAP1_IAA_30M_REP1.umi_dedup.transcriptome.sorted_edit_distance.tsv:md5,3ec96cdbe4e9eb45b18df4391b89df34", + "RAP1_IAA_30M_REP1.umi_dedup.transcriptome.sorted_per_umi.tsv:md5,a06aa2fd396dfa28eb3408a0f4e3bf48", + "RAP1_IAA_30M_REP1.umi_dedup.transcriptome.sorted_per_umi_per_position.tsv:md5,ce0c05c7994d600c75a34d5c9908f970", + "RAP1_UNINDUCED_REP1.umi_dedup.sorted_edit_distance.tsv:md5,048fb23f5ee8a0097feef5dd2f35381b", + "RAP1_UNINDUCED_REP1.umi_dedup.sorted_per_umi.tsv:md5,70a8bed55b47d069d0857f4697464bf7", + "RAP1_UNINDUCED_REP1.umi_dedup.sorted_per_umi_per_position.tsv:md5,8d13def9d9b55d8a43969a8aab6fe2bf", + "RAP1_UNINDUCED_REP1.umi_dedup.transcriptome.sorted_edit_distance.tsv:md5,4178e6966eee4c40584e0354571b590e", + "RAP1_UNINDUCED_REP1.umi_dedup.transcriptome.sorted_per_umi.tsv:md5,5c8b59f57c3d3d29024687b3429b7b34", + "RAP1_UNINDUCED_REP1.umi_dedup.transcriptome.sorted_per_umi_per_position.tsv:md5,1ebbaa9cdfacb82e9b71e8a44075cd5e", + "RAP1_UNINDUCED_REP2.umi_dedup.sorted_edit_distance.tsv:md5,97ba63438babb87df459a61fcdb6f5e9", + "RAP1_UNINDUCED_REP2.umi_dedup.sorted_per_umi.tsv:md5,d371fbcc14f22734f37edb0a6b7e6930", + "RAP1_UNINDUCED_REP2.umi_dedup.sorted_per_umi_per_position.tsv:md5,1f570419cbf55e52d96551af7a614fc3", + "RAP1_UNINDUCED_REP2.umi_dedup.transcriptome.sorted_edit_distance.tsv:md5,ec5c512b2ef465206e1e9de5895bd160", + "RAP1_UNINDUCED_REP2.umi_dedup.transcriptome.sorted_per_umi.tsv:md5,887091e6b5d87921e7a6a862dc1fc858", + "RAP1_UNINDUCED_REP2.umi_dedup.transcriptome.sorted_per_umi_per_position.tsv:md5,70eba520316c1b7f5c43f298979cbfbd", + "WT_REP1.umi_dedup.sorted_edit_distance.tsv:md5,30da317cb7ae948d5e70e6d5e20ff94b", + "WT_REP1.umi_dedup.sorted_per_umi.tsv:md5,d81c983368a0b2218eb8954579c376e7", + "WT_REP1.umi_dedup.sorted_per_umi_per_position.tsv:md5,e2412d716ca85a90f8dbb100a9614991", + "WT_REP1.umi_dedup.transcriptome.sorted_edit_distance.tsv:md5,2f1be285cf27bdcb6cb7aa3f6f2758bc", + "WT_REP1.umi_dedup.transcriptome.sorted_per_umi.tsv:md5,ee2df66b8427ea94fff18430cc1937a4", + "WT_REP1.umi_dedup.transcriptome.sorted_per_umi_per_position.tsv:md5,485a03ff0fec979ee84d918158502bf1", + "WT_REP2.umi_dedup.sorted_edit_distance.tsv:md5,a935a046c0a8f269cdeaa7c1c935f0ec", + "WT_REP2.umi_dedup.sorted_per_umi.tsv:md5,0cb0f12e99861120a0437051a7aaa152", + "WT_REP2.umi_dedup.sorted_per_umi_per_position.tsv:md5,6f5656947a7f0076df446e6f40430027", + "WT_REP2.umi_dedup.transcriptome.sorted_edit_distance.tsv:md5,3e3c6a7e8996e566350742e9911366d3", + "WT_REP2.umi_dedup.transcriptome.sorted_per_umi.tsv:md5,0c986c4cb7a77f650a19e2c454b9b179", + "WT_REP2.umi_dedup.transcriptome.sorted_per_umi_per_position.tsv:md5,af9028dbdab81de3854a32cd1d19ac8b", + "RAP1_IAA_30M_REP1.umi_dedup.sorted.log:md5,1d596684bb6487e751558b62b5eaaf1f", + "RAP1_UNINDUCED_REP1.umi_dedup.sorted.log:md5,73520f6fea900b4748414e8c9b50f172", + "RAP1_UNINDUCED_REP2.umi_dedup.sorted.log:md5,110cdd8e529f05d9c6205fd62a293167", + "WT_REP1.umi_dedup.sorted.log:md5,59431dec815addc430a05e5e415ce328", + "WT_REP2.umi_dedup.sorted.log:md5,713e4e11d5e3dad46169c36be0ba3e89", + "RAP1_IAA_30M_REP1.umi_dedup.transcriptome.filtered.prepare_for_rsem.log:md5,d0d4f7a5297dab70631c5cc640f2fc5e", + "WT_REP1.umi_dedup.transcriptome.filtered.prepare_for_rsem.log:md5,65728dc6e007352514ec07b331425ec5", + "WT_REP2.umi_dedup.transcriptome.filtered.prepare_for_rsem.log:md5,ac6061c5e9feaeaca9e4084f5c9e9390", + "RAP1_IAA_30M_REP1.umi_dedup.transcriptome.sorted.log:md5,801f3154d2d895f5d0c13618d9db1e69", + "RAP1_UNINDUCED_REP1.umi_dedup.transcriptome.sorted.log:md5,2b747aafaac49dcd3408b4cd6fac42b7", + "RAP1_UNINDUCED_REP2.umi_dedup.transcriptome.sorted.log:md5,6303ee2bc3001fdc96480b243b3f9a40", + "WT_REP1.umi_dedup.transcriptome.sorted.log:md5,b7635957de3aa6f8e22ba0944c5044ec", + "WT_REP2.umi_dedup.transcriptome.sorted.log:md5,7f991b0c33352d31facf563602a74180", + "RAP1_IAA_30M_REP1.umi_extract.log:md5,24881e489f7b72d687c14c215bf229eb", + "RAP1_UNINDUCED_REP1.umi_extract.log:md5,040e0f255ad8b81ca1177b73a5fa9c02", + "RAP1_UNINDUCED_REP2.umi_extract.log:md5,f9846c25cddf606a6892a0d163b54116", + "WT_REP1.umi_extract.log:md5,2d86323e1401085d467fa31fd7b3dfea", + "WT_REP2.umi_extract.log:md5,955ac04b8134297b85292eca6639efcd" + ] + ], + "meta": { + "nf-test": "0.9.0", + "nextflow": "24.10.2" + }, + "timestamp": "2024-12-10T18:38:07.264678218" + } +} \ No newline at end of file From e6843d8f83a1680c0fb9a28f9893c331748e6e2e Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Tue, 10 Dec 2024 22:23:43 +0000 Subject: [PATCH 060/107] Update umi tests --- tests/umi.nf.test | 43 +- tests/umi.nf.test.snap | 1897 ++++++++++++++++++++++++++++++++-------- 2 files changed, 1549 insertions(+), 391 deletions(-) diff --git a/tests/umi.nf.test b/tests/umi.nf.test index 3764ac8bd..3ef7c52d2 100644 --- a/tests/umi.nf.test +++ b/tests/umi.nf.test @@ -3,11 +3,48 @@ nextflow_pipeline { name "Test pipeline with dummy UMI settings" script "../main.nf" - test("Params: Dummy UMI setting") { + test("Params: --aligner hisat2 --umi_dedup_tool 'umicollapse'") { when { params { - with_umi = true + //with_umi = true + extra_umitools_extract_args = '--random-seed 1234' + umitools_extract_method = "regex" + umitools_bc_pattern = "^(?PCGA.{8}){s<=2}.*" + umitools_dedup_stats = true + umi_dedup_tool = 'umicollapse' + aligner = 'hisat2' + outdir = "$outputDir" + } + } + + then { + // stable_name: All files + folders in ${params.outdir}/ with a stable name + def stable_name = getAllFilesFromDir(params.outdir, relative: true, includeDir: true, ignore: ['pipeline_info/*.{html,json,txt}']) + // stable_path: All files in ${params.outdir}/ with stable content + def stable_path = getAllFilesFromDir(params.outdir, ignoreFile: 'tests/.nftignore') + assertAll( + { assert workflow.success}, + { assert snapshot( + // Number of successful tasks + workflow.trace.succeeded().size(), + // pipeline versions.yml file for multiqc from which Nextflow version is removed because we tests pipelines on multiple Nextflow versions + removeNextflowVersion("$outputDir/pipeline_info/nf_core_rnaseq_software_mqc_versions.yml"), + // All stable path name, with a relative path + stable_name, + // All files with stable contents + stable_path + ).match() } + ) + } + } + + test("--umi_dedup_tool 'umitools'") { + + when { + params { + //with_umi = true + extra_umitools_extract_args = '--random-seed 1234' umitools_extract_method = "regex" umitools_bc_pattern = "^(?PCGA.{8}){s<=2}.*" umitools_dedup_stats = true @@ -36,7 +73,7 @@ nextflow_pipeline { } } - test("Params: Dummy UMI settings - stub") { + test("--umi_dedup_tool 'umitools - stub") { options "-stub" diff --git a/tests/umi.nf.test.snap b/tests/umi.nf.test.snap index 407127426..5f2b59931 100644 --- a/tests/umi.nf.test.snap +++ b/tests/umi.nf.test.snap @@ -1,124 +1,7 @@ { - "Params: Dummy UMI settings - stub": { + "--umi_dedup_tool 'umitools'": { "content": [ - 32, - { - "BBMAP_BBSPLIT": { - "bbmap": 39.1 - }, - "CAT_FASTQ": { - "cat": 9.5 - }, - "CUSTOM_CATADDITIONALFASTA": { - "python": null - }, - "CUSTOM_GETCHROMSIZES": { - "getchromsizes": 1.21 - }, - "FASTQC": { - "fastqc": "0.12.1" - }, - "FQ_LINT": { - "fq": "0.12.0 (2024-07-08)" - }, - "GTF2BED": { - "perl": "5.26.2" - }, - "GTF_FILTER": { - "python": "3.9.5" - }, - "GUNZIP_ADDITIONAL_FASTA": { - "gunzip": 1.1 - }, - "GUNZIP_GTF": { - "gunzip": 1.1 - }, - "STAR_GENOMEGENERATE": { - "star": "2.7.11b", - "samtools": 1.2, - "gawk": "5.1.0" - }, - "TRIMGALORE": { - "trimgalore": "0.6.10", - "cutadapt": 4.9 - }, - "UMITOOLS_EXTRACT": { - "umitools": "1.1.5" - }, - "UNTAR_SALMON_INDEX": { - "untar": 1.34 - }, - "Workflow": { - "nf-core/rnaseq": "v3.18.0dev" - } - }, - [ - "custom", - "custom/out", - "custom/out/genome_transcriptome.fasta", - "custom/out/genome_transcriptome.gtf", - "fastqc", - "fastqc/raw", - "fastqc/raw/RAP1_IAA_30M_REP1_raw.html", - "fastqc/raw/RAP1_IAA_30M_REP1_raw.zip", - "fastqc/raw/RAP1_UNINDUCED_REP1_raw.html", - "fastqc/raw/RAP1_UNINDUCED_REP1_raw.zip", - "fastqc/raw/RAP1_UNINDUCED_REP2_raw.html", - "fastqc/raw/RAP1_UNINDUCED_REP2_raw.zip", - "fastqc/raw/WT_REP1_raw.html", - "fastqc/raw/WT_REP1_raw.zip", - "fastqc/raw/WT_REP2_raw.html", - "fastqc/raw/WT_REP2_raw.zip", - "fastqc/trim", - "fq_lint", - "fq_lint/raw", - "fq_lint/raw/RAP1_IAA_30M_REP1.fq_lint.txt", - "fq_lint/raw/RAP1_UNINDUCED_REP1.fq_lint.txt", - "fq_lint/raw/RAP1_UNINDUCED_REP2.fq_lint.txt", - "fq_lint/raw/WT_REP1.fq_lint.txt", - "fq_lint/raw/WT_REP2.fq_lint.txt", - "multiqc", - "multiqc/star_salmon", - "multiqc/star_salmon/multiqc_data", - "multiqc/star_salmon/multiqc_plots", - "multiqc/star_salmon/multiqc_report.html", - "pipeline_info", - "pipeline_info/nf_core_rnaseq_software_mqc_versions.yml", - "trimgalore", - "trimgalore/RAP1_IAA_30M_REP1_trimmed_1.fastq.gz_trimming_report.txt", - "trimgalore/RAP1_IAA_30M_REP1_trimmed_2.fastq.gz_trimming_report.txt", - "trimgalore/RAP1_UNINDUCED_REP1_trimmed.fastq.gz_trimming_report.txt", - "trimgalore/RAP1_UNINDUCED_REP2_trimmed.fastq.gz_trimming_report.txt", - "trimgalore/WT_REP1_trimmed_1.fastq.gz_trimming_report.txt", - "trimgalore/WT_REP1_trimmed_2.fastq.gz_trimming_report.txt", - "trimgalore/WT_REP2_trimmed_1.fastq.gz_trimming_report.txt", - "trimgalore/WT_REP2_trimmed_2.fastq.gz_trimming_report.txt", - "umitools", - "umitools/RAP1_IAA_30M_REP1.umi_extract.log", - "umitools/RAP1_UNINDUCED_REP1.umi_extract.log", - "umitools/RAP1_UNINDUCED_REP2.umi_extract.log", - "umitools/WT_REP1.umi_extract.log", - "umitools/WT_REP2.umi_extract.log" - ], - [ - "genome_transcriptome.fasta:md5,d41d8cd98f00b204e9800998ecf8427e", - "genome_transcriptome.gtf:md5,d41d8cd98f00b204e9800998ecf8427e", - "RAP1_IAA_30M_REP1.umi_extract.log:md5,d41d8cd98f00b204e9800998ecf8427e", - "RAP1_UNINDUCED_REP1.umi_extract.log:md5,d41d8cd98f00b204e9800998ecf8427e", - "RAP1_UNINDUCED_REP2.umi_extract.log:md5,d41d8cd98f00b204e9800998ecf8427e", - "WT_REP1.umi_extract.log:md5,d41d8cd98f00b204e9800998ecf8427e", - "WT_REP2.umi_extract.log:md5,d41d8cd98f00b204e9800998ecf8427e" - ] - ], - "meta": { - "nf-test": "0.9.0", - "nextflow": "24.10.2" - }, - "timestamp": "2024-12-10T18:39:20.510781783" - }, - "Params: Dummy UMI setting": { - "content": [ - 276, + 213, { "BBMAP_BBSPLIT": { "bbmap": 39.1 @@ -173,6 +56,9 @@ "MULTIQC_CUSTOM_BIOTYPE": { "python": "3.9.5" }, + "PICARD_MARKDUPLICATES": { + "picard": "3.1.1" + }, "QUALIMAP_RNASEQ": { "qualimap": 2.3 }, @@ -247,15 +133,6 @@ "UCSC_BEDGRAPHTOBIGWIG": { "ucsc": 469 }, - "UMITOOLS_DEDUP": { - "umitools": "1.1.5" - }, - "UMITOOLS_EXTRACT": { - "umitools": "1.1.5" - }, - "UMITOOLS_PREPAREFORSALMON": { - "umitools": "1.1.5" - }, "UNTAR_SALMON_INDEX": { "untar": 1.34 }, @@ -369,6 +246,7 @@ "multiqc/star_salmon/multiqc_report_data/multiqc_fastqc_fastqc_trimmed.txt", "multiqc/star_salmon/multiqc_report_data/multiqc_featurecounts_biotype_plot.txt", "multiqc/star_salmon/multiqc_report_data/multiqc_general_stats.txt", + "multiqc/star_salmon/multiqc_report_data/multiqc_picard_dups.txt", "multiqc/star_salmon/multiqc_report_data/multiqc_rseqc_bam_stat.txt", "multiqc/star_salmon/multiqc_report_data/multiqc_rseqc_infer_experiment.txt", "multiqc/star_salmon/multiqc_report_data/multiqc_rseqc_junction_annotation.txt", @@ -384,8 +262,10 @@ "multiqc/star_salmon/multiqc_report_data/multiqc_software_versions.txt", "multiqc/star_salmon/multiqc_report_data/multiqc_sources.txt", "multiqc/star_salmon/multiqc_report_data/multiqc_star.txt", - "multiqc/star_salmon/multiqc_report_data/multiqc_umitools_dedup.txt", - "multiqc/star_salmon/multiqc_report_data/multiqc_umitools_extract.txt", + "multiqc/star_salmon/multiqc_report_data/picard_deduplication.txt", + "multiqc/star_salmon/multiqc_report_data/picard_histogram.txt", + "multiqc/star_salmon/multiqc_report_data/picard_histogram_1.txt", + "multiqc/star_salmon/multiqc_report_data/picard_histogram_2.txt", "multiqc/star_salmon/multiqc_report_data/qualimap_gene_coverage_profile_Counts.txt", "multiqc/star_salmon/multiqc_report_data/qualimap_gene_coverage_profile_Normalised.txt", "multiqc/star_salmon/multiqc_report_data/qualimap_genomic_origin.txt", @@ -415,8 +295,6 @@ "multiqc/star_salmon/multiqc_report_data/samtools_alignment_plot.txt", "multiqc/star_salmon/multiqc_report_data/star_alignment_plot.txt", "multiqc/star_salmon/multiqc_report_data/star_summary_table.txt", - "multiqc/star_salmon/multiqc_report_data/umitools_deduplication_barplot.txt", - "multiqc/star_salmon/multiqc_report_data/umitools_stats_violin.txt", "multiqc/star_salmon/multiqc_report_plots", "multiqc/star_salmon/multiqc_report_plots/pdf", "multiqc/star_salmon/multiqc_report_plots/pdf/cutadapt_filtered_reads_plot-cnt.pdf", @@ -452,6 +330,8 @@ "multiqc/star_salmon/multiqc_report_plots/pdf/featurecounts_biotype_plot-cnt.pdf", "multiqc/star_salmon/multiqc_report_plots/pdf/featurecounts_biotype_plot-pct.pdf", "multiqc/star_salmon/multiqc_report_plots/pdf/general_stats_table.pdf", + "multiqc/star_salmon/multiqc_report_plots/pdf/picard_deduplication-cnt.pdf", + "multiqc/star_salmon/multiqc_report_plots/pdf/picard_deduplication-pct.pdf", "multiqc/star_salmon/multiqc_report_plots/pdf/qualimap_gene_coverage_profile_Counts.pdf", "multiqc/star_salmon/multiqc_report_plots/pdf/qualimap_gene_coverage_profile_Normalised.pdf", "multiqc/star_salmon/multiqc_report_plots/pdf/qualimap_genomic_origin-cnt.pdf", @@ -486,9 +366,6 @@ "multiqc/star_salmon/multiqc_report_plots/pdf/star_alignment_plot-cnt.pdf", "multiqc/star_salmon/multiqc_report_plots/pdf/star_alignment_plot-pct.pdf", "multiqc/star_salmon/multiqc_report_plots/pdf/star_summary_table.pdf", - "multiqc/star_salmon/multiqc_report_plots/pdf/umitools_deduplication_barplot-cnt.pdf", - "multiqc/star_salmon/multiqc_report_plots/pdf/umitools_deduplication_barplot-pct.pdf", - "multiqc/star_salmon/multiqc_report_plots/pdf/umitools_stats_violin.pdf", "multiqc/star_salmon/multiqc_report_plots/png", "multiqc/star_salmon/multiqc_report_plots/png/cutadapt_filtered_reads_plot-cnt.png", "multiqc/star_salmon/multiqc_report_plots/png/cutadapt_filtered_reads_plot-pct.png", @@ -523,6 +400,8 @@ "multiqc/star_salmon/multiqc_report_plots/png/featurecounts_biotype_plot-cnt.png", "multiqc/star_salmon/multiqc_report_plots/png/featurecounts_biotype_plot-pct.png", "multiqc/star_salmon/multiqc_report_plots/png/general_stats_table.png", + "multiqc/star_salmon/multiqc_report_plots/png/picard_deduplication-cnt.png", + "multiqc/star_salmon/multiqc_report_plots/png/picard_deduplication-pct.png", "multiqc/star_salmon/multiqc_report_plots/png/qualimap_gene_coverage_profile_Counts.png", "multiqc/star_salmon/multiqc_report_plots/png/qualimap_gene_coverage_profile_Normalised.png", "multiqc/star_salmon/multiqc_report_plots/png/qualimap_genomic_origin-cnt.png", @@ -557,9 +436,6 @@ "multiqc/star_salmon/multiqc_report_plots/png/star_alignment_plot-cnt.png", "multiqc/star_salmon/multiqc_report_plots/png/star_alignment_plot-pct.png", "multiqc/star_salmon/multiqc_report_plots/png/star_summary_table.png", - "multiqc/star_salmon/multiqc_report_plots/png/umitools_deduplication_barplot-cnt.png", - "multiqc/star_salmon/multiqc_report_plots/png/umitools_deduplication_barplot-pct.png", - "multiqc/star_salmon/multiqc_report_plots/png/umitools_stats_violin.png", "multiqc/star_salmon/multiqc_report_plots/svg", "multiqc/star_salmon/multiqc_report_plots/svg/cutadapt_filtered_reads_plot-cnt.svg", "multiqc/star_salmon/multiqc_report_plots/svg/cutadapt_filtered_reads_plot-pct.svg", @@ -594,6 +470,8 @@ "multiqc/star_salmon/multiqc_report_plots/svg/featurecounts_biotype_plot-cnt.svg", "multiqc/star_salmon/multiqc_report_plots/svg/featurecounts_biotype_plot-pct.svg", "multiqc/star_salmon/multiqc_report_plots/svg/general_stats_table.svg", + "multiqc/star_salmon/multiqc_report_plots/svg/picard_deduplication-cnt.svg", + "multiqc/star_salmon/multiqc_report_plots/svg/picard_deduplication-pct.svg", "multiqc/star_salmon/multiqc_report_plots/svg/qualimap_gene_coverage_profile_Counts.svg", "multiqc/star_salmon/multiqc_report_plots/svg/qualimap_gene_coverage_profile_Normalised.svg", "multiqc/star_salmon/multiqc_report_plots/svg/qualimap_genomic_origin-cnt.svg", @@ -628,9 +506,6 @@ "multiqc/star_salmon/multiqc_report_plots/svg/star_alignment_plot-cnt.svg", "multiqc/star_salmon/multiqc_report_plots/svg/star_alignment_plot-pct.svg", "multiqc/star_salmon/multiqc_report_plots/svg/star_summary_table.svg", - "multiqc/star_salmon/multiqc_report_plots/svg/umitools_deduplication_barplot-cnt.svg", - "multiqc/star_salmon/multiqc_report_plots/svg/umitools_deduplication_barplot-pct.svg", - "multiqc/star_salmon/multiqc_report_plots/svg/umitools_stats_violin.svg", "pipeline_info", "pipeline_info/nf_core_rnaseq_software_mqc_versions.yml", "salmon", @@ -742,8 +617,8 @@ "salmon/tx2gene.tsv", "star_salmon", "star_salmon/RAP1_IAA_30M_REP1", - "star_salmon/RAP1_IAA_30M_REP1.umi_dedup.sorted.bam", - "star_salmon/RAP1_IAA_30M_REP1.umi_dedup.sorted.bam.bai", + "star_salmon/RAP1_IAA_30M_REP1.markdup.sorted.bam", + "star_salmon/RAP1_IAA_30M_REP1.markdup.sorted.bam.bai", "star_salmon/RAP1_IAA_30M_REP1/aux_info", "star_salmon/RAP1_IAA_30M_REP1/aux_info/ambig_info.tsv", "star_salmon/RAP1_IAA_30M_REP1/aux_info/expected_bias.gz", @@ -759,8 +634,8 @@ "star_salmon/RAP1_IAA_30M_REP1/quant.genes.sf", "star_salmon/RAP1_IAA_30M_REP1/quant.sf", "star_salmon/RAP1_UNINDUCED_REP1", - "star_salmon/RAP1_UNINDUCED_REP1.umi_dedup.sorted.bam", - "star_salmon/RAP1_UNINDUCED_REP1.umi_dedup.sorted.bam.bai", + "star_salmon/RAP1_UNINDUCED_REP1.markdup.sorted.bam", + "star_salmon/RAP1_UNINDUCED_REP1.markdup.sorted.bam.bai", "star_salmon/RAP1_UNINDUCED_REP1/aux_info", "star_salmon/RAP1_UNINDUCED_REP1/aux_info/ambig_info.tsv", "star_salmon/RAP1_UNINDUCED_REP1/aux_info/expected_bias.gz", @@ -776,8 +651,8 @@ "star_salmon/RAP1_UNINDUCED_REP1/quant.genes.sf", "star_salmon/RAP1_UNINDUCED_REP1/quant.sf", "star_salmon/RAP1_UNINDUCED_REP2", - "star_salmon/RAP1_UNINDUCED_REP2.umi_dedup.sorted.bam", - "star_salmon/RAP1_UNINDUCED_REP2.umi_dedup.sorted.bam.bai", + "star_salmon/RAP1_UNINDUCED_REP2.markdup.sorted.bam", + "star_salmon/RAP1_UNINDUCED_REP2.markdup.sorted.bam.bai", "star_salmon/RAP1_UNINDUCED_REP2/aux_info", "star_salmon/RAP1_UNINDUCED_REP2/aux_info/ambig_info.tsv", "star_salmon/RAP1_UNINDUCED_REP2/aux_info/expected_bias.gz", @@ -793,8 +668,8 @@ "star_salmon/RAP1_UNINDUCED_REP2/quant.genes.sf", "star_salmon/RAP1_UNINDUCED_REP2/quant.sf", "star_salmon/WT_REP1", - "star_salmon/WT_REP1.umi_dedup.sorted.bam", - "star_salmon/WT_REP1.umi_dedup.sorted.bam.bai", + "star_salmon/WT_REP1.markdup.sorted.bam", + "star_salmon/WT_REP1.markdup.sorted.bam.bai", "star_salmon/WT_REP1/aux_info", "star_salmon/WT_REP1/aux_info/ambig_info.tsv", "star_salmon/WT_REP1/aux_info/expected_bias.gz", @@ -810,8 +685,8 @@ "star_salmon/WT_REP1/quant.genes.sf", "star_salmon/WT_REP1/quant.sf", "star_salmon/WT_REP2", - "star_salmon/WT_REP2.umi_dedup.sorted.bam", - "star_salmon/WT_REP2.umi_dedup.sorted.bam.bai", + "star_salmon/WT_REP2.markdup.sorted.bam", + "star_salmon/WT_REP2.markdup.sorted.bam.bai", "star_salmon/WT_REP2/aux_info", "star_salmon/WT_REP2/aux_info/ambig_info.tsv", "star_salmon/WT_REP2/aux_info/expected_bias.gz", @@ -923,6 +798,12 @@ "star_salmon/log/WT_REP2.Log.out", "star_salmon/log/WT_REP2.Log.progress.out", "star_salmon/log/WT_REP2.SJ.out.tab", + "star_salmon/picard_metrics", + "star_salmon/picard_metrics/RAP1_IAA_30M_REP1.markdup.sorted.MarkDuplicates.metrics.txt", + "star_salmon/picard_metrics/RAP1_UNINDUCED_REP1.markdup.sorted.MarkDuplicates.metrics.txt", + "star_salmon/picard_metrics/RAP1_UNINDUCED_REP2.markdup.sorted.MarkDuplicates.metrics.txt", + "star_salmon/picard_metrics/WT_REP1.markdup.sorted.MarkDuplicates.metrics.txt", + "star_salmon/picard_metrics/WT_REP2.markdup.sorted.MarkDuplicates.metrics.txt", "star_salmon/qualimap", "star_salmon/qualimap/RAP1_IAA_30M_REP1", "star_salmon/qualimap/RAP1_IAA_30M_REP1/css", @@ -1243,51 +1124,36 @@ "star_salmon/salmon.merged.transcript_lengths.tsv", "star_salmon/salmon.merged.transcript_tpm.tsv", "star_salmon/samtools_stats", + "star_salmon/samtools_stats/RAP1_IAA_30M_REP1.markdup.sorted.bam.flagstat", + "star_salmon/samtools_stats/RAP1_IAA_30M_REP1.markdup.sorted.bam.idxstats", + "star_salmon/samtools_stats/RAP1_IAA_30M_REP1.markdup.sorted.bam.stats", "star_salmon/samtools_stats/RAP1_IAA_30M_REP1.sorted.bam.flagstat", "star_salmon/samtools_stats/RAP1_IAA_30M_REP1.sorted.bam.idxstats", "star_salmon/samtools_stats/RAP1_IAA_30M_REP1.sorted.bam.stats", - "star_salmon/samtools_stats/RAP1_IAA_30M_REP1.umi_dedup.sorted.bam.flagstat", - "star_salmon/samtools_stats/RAP1_IAA_30M_REP1.umi_dedup.sorted.bam.idxstats", - "star_salmon/samtools_stats/RAP1_IAA_30M_REP1.umi_dedup.sorted.bam.stats", - "star_salmon/samtools_stats/RAP1_IAA_30M_REP1.umi_dedup.transcriptome.sorted.bam.flagstat", - "star_salmon/samtools_stats/RAP1_IAA_30M_REP1.umi_dedup.transcriptome.sorted.bam.idxstats", - "star_salmon/samtools_stats/RAP1_IAA_30M_REP1.umi_dedup.transcriptome.sorted.bam.stats", + "star_salmon/samtools_stats/RAP1_UNINDUCED_REP1.markdup.sorted.bam.flagstat", + "star_salmon/samtools_stats/RAP1_UNINDUCED_REP1.markdup.sorted.bam.idxstats", + "star_salmon/samtools_stats/RAP1_UNINDUCED_REP1.markdup.sorted.bam.stats", "star_salmon/samtools_stats/RAP1_UNINDUCED_REP1.sorted.bam.flagstat", "star_salmon/samtools_stats/RAP1_UNINDUCED_REP1.sorted.bam.idxstats", "star_salmon/samtools_stats/RAP1_UNINDUCED_REP1.sorted.bam.stats", - "star_salmon/samtools_stats/RAP1_UNINDUCED_REP1.umi_dedup.sorted.bam.flagstat", - "star_salmon/samtools_stats/RAP1_UNINDUCED_REP1.umi_dedup.sorted.bam.idxstats", - "star_salmon/samtools_stats/RAP1_UNINDUCED_REP1.umi_dedup.sorted.bam.stats", - "star_salmon/samtools_stats/RAP1_UNINDUCED_REP1.umi_dedup.transcriptome.sorted.bam.flagstat", - "star_salmon/samtools_stats/RAP1_UNINDUCED_REP1.umi_dedup.transcriptome.sorted.bam.idxstats", - "star_salmon/samtools_stats/RAP1_UNINDUCED_REP1.umi_dedup.transcriptome.sorted.bam.stats", + "star_salmon/samtools_stats/RAP1_UNINDUCED_REP2.markdup.sorted.bam.flagstat", + "star_salmon/samtools_stats/RAP1_UNINDUCED_REP2.markdup.sorted.bam.idxstats", + "star_salmon/samtools_stats/RAP1_UNINDUCED_REP2.markdup.sorted.bam.stats", "star_salmon/samtools_stats/RAP1_UNINDUCED_REP2.sorted.bam.flagstat", "star_salmon/samtools_stats/RAP1_UNINDUCED_REP2.sorted.bam.idxstats", "star_salmon/samtools_stats/RAP1_UNINDUCED_REP2.sorted.bam.stats", - "star_salmon/samtools_stats/RAP1_UNINDUCED_REP2.umi_dedup.sorted.bam.flagstat", - "star_salmon/samtools_stats/RAP1_UNINDUCED_REP2.umi_dedup.sorted.bam.idxstats", - "star_salmon/samtools_stats/RAP1_UNINDUCED_REP2.umi_dedup.sorted.bam.stats", - "star_salmon/samtools_stats/RAP1_UNINDUCED_REP2.umi_dedup.transcriptome.sorted.bam.flagstat", - "star_salmon/samtools_stats/RAP1_UNINDUCED_REP2.umi_dedup.transcriptome.sorted.bam.idxstats", - "star_salmon/samtools_stats/RAP1_UNINDUCED_REP2.umi_dedup.transcriptome.sorted.bam.stats", + "star_salmon/samtools_stats/WT_REP1.markdup.sorted.bam.flagstat", + "star_salmon/samtools_stats/WT_REP1.markdup.sorted.bam.idxstats", + "star_salmon/samtools_stats/WT_REP1.markdup.sorted.bam.stats", "star_salmon/samtools_stats/WT_REP1.sorted.bam.flagstat", "star_salmon/samtools_stats/WT_REP1.sorted.bam.idxstats", "star_salmon/samtools_stats/WT_REP1.sorted.bam.stats", - "star_salmon/samtools_stats/WT_REP1.umi_dedup.sorted.bam.flagstat", - "star_salmon/samtools_stats/WT_REP1.umi_dedup.sorted.bam.idxstats", - "star_salmon/samtools_stats/WT_REP1.umi_dedup.sorted.bam.stats", - "star_salmon/samtools_stats/WT_REP1.umi_dedup.transcriptome.sorted.bam.flagstat", - "star_salmon/samtools_stats/WT_REP1.umi_dedup.transcriptome.sorted.bam.idxstats", - "star_salmon/samtools_stats/WT_REP1.umi_dedup.transcriptome.sorted.bam.stats", + "star_salmon/samtools_stats/WT_REP2.markdup.sorted.bam.flagstat", + "star_salmon/samtools_stats/WT_REP2.markdup.sorted.bam.idxstats", + "star_salmon/samtools_stats/WT_REP2.markdup.sorted.bam.stats", "star_salmon/samtools_stats/WT_REP2.sorted.bam.flagstat", "star_salmon/samtools_stats/WT_REP2.sorted.bam.idxstats", "star_salmon/samtools_stats/WT_REP2.sorted.bam.stats", - "star_salmon/samtools_stats/WT_REP2.umi_dedup.sorted.bam.flagstat", - "star_salmon/samtools_stats/WT_REP2.umi_dedup.sorted.bam.idxstats", - "star_salmon/samtools_stats/WT_REP2.umi_dedup.sorted.bam.stats", - "star_salmon/samtools_stats/WT_REP2.umi_dedup.transcriptome.sorted.bam.flagstat", - "star_salmon/samtools_stats/WT_REP2.umi_dedup.transcriptome.sorted.bam.idxstats", - "star_salmon/samtools_stats/WT_REP2.umi_dedup.transcriptome.sorted.bam.stats", "star_salmon/stringtie", "star_salmon/stringtie/RAP1_IAA_30M_REP1.ballgown", "star_salmon/stringtie/RAP1_IAA_30M_REP1.ballgown/e2t.ctab", @@ -1335,53 +1201,6 @@ "star_salmon/stringtie/WT_REP2.gene.abundance.txt", "star_salmon/stringtie/WT_REP2.transcripts.gtf", "star_salmon/tx2gene.tsv", - "star_salmon/umitools", - "star_salmon/umitools/RAP1_IAA_30M_REP1.umi_dedup.sorted_edit_distance.tsv", - "star_salmon/umitools/RAP1_IAA_30M_REP1.umi_dedup.sorted_per_umi.tsv", - "star_salmon/umitools/RAP1_IAA_30M_REP1.umi_dedup.sorted_per_umi_per_position.tsv", - "star_salmon/umitools/RAP1_IAA_30M_REP1.umi_dedup.transcriptome.sorted_edit_distance.tsv", - "star_salmon/umitools/RAP1_IAA_30M_REP1.umi_dedup.transcriptome.sorted_per_umi.tsv", - "star_salmon/umitools/RAP1_IAA_30M_REP1.umi_dedup.transcriptome.sorted_per_umi_per_position.tsv", - "star_salmon/umitools/RAP1_UNINDUCED_REP1.umi_dedup.sorted_edit_distance.tsv", - "star_salmon/umitools/RAP1_UNINDUCED_REP1.umi_dedup.sorted_per_umi.tsv", - "star_salmon/umitools/RAP1_UNINDUCED_REP1.umi_dedup.sorted_per_umi_per_position.tsv", - "star_salmon/umitools/RAP1_UNINDUCED_REP1.umi_dedup.transcriptome.sorted_edit_distance.tsv", - "star_salmon/umitools/RAP1_UNINDUCED_REP1.umi_dedup.transcriptome.sorted_per_umi.tsv", - "star_salmon/umitools/RAP1_UNINDUCED_REP1.umi_dedup.transcriptome.sorted_per_umi_per_position.tsv", - "star_salmon/umitools/RAP1_UNINDUCED_REP2.umi_dedup.sorted_edit_distance.tsv", - "star_salmon/umitools/RAP1_UNINDUCED_REP2.umi_dedup.sorted_per_umi.tsv", - "star_salmon/umitools/RAP1_UNINDUCED_REP2.umi_dedup.sorted_per_umi_per_position.tsv", - "star_salmon/umitools/RAP1_UNINDUCED_REP2.umi_dedup.transcriptome.sorted_edit_distance.tsv", - "star_salmon/umitools/RAP1_UNINDUCED_REP2.umi_dedup.transcriptome.sorted_per_umi.tsv", - "star_salmon/umitools/RAP1_UNINDUCED_REP2.umi_dedup.transcriptome.sorted_per_umi_per_position.tsv", - "star_salmon/umitools/WT_REP1.umi_dedup.sorted_edit_distance.tsv", - "star_salmon/umitools/WT_REP1.umi_dedup.sorted_per_umi.tsv", - "star_salmon/umitools/WT_REP1.umi_dedup.sorted_per_umi_per_position.tsv", - "star_salmon/umitools/WT_REP1.umi_dedup.transcriptome.sorted_edit_distance.tsv", - "star_salmon/umitools/WT_REP1.umi_dedup.transcriptome.sorted_per_umi.tsv", - "star_salmon/umitools/WT_REP1.umi_dedup.transcriptome.sorted_per_umi_per_position.tsv", - "star_salmon/umitools/WT_REP2.umi_dedup.sorted_edit_distance.tsv", - "star_salmon/umitools/WT_REP2.umi_dedup.sorted_per_umi.tsv", - "star_salmon/umitools/WT_REP2.umi_dedup.sorted_per_umi_per_position.tsv", - "star_salmon/umitools/WT_REP2.umi_dedup.transcriptome.sorted_edit_distance.tsv", - "star_salmon/umitools/WT_REP2.umi_dedup.transcriptome.sorted_per_umi.tsv", - "star_salmon/umitools/WT_REP2.umi_dedup.transcriptome.sorted_per_umi_per_position.tsv", - "star_salmon/umitools/genomic_dedup_log", - "star_salmon/umitools/genomic_dedup_log/RAP1_IAA_30M_REP1.umi_dedup.sorted.log", - "star_salmon/umitools/genomic_dedup_log/RAP1_UNINDUCED_REP1.umi_dedup.sorted.log", - "star_salmon/umitools/genomic_dedup_log/RAP1_UNINDUCED_REP2.umi_dedup.sorted.log", - "star_salmon/umitools/genomic_dedup_log/WT_REP1.umi_dedup.sorted.log", - "star_salmon/umitools/genomic_dedup_log/WT_REP2.umi_dedup.sorted.log", - "star_salmon/umitools/prepare_for_salmon_log", - "star_salmon/umitools/prepare_for_salmon_log/RAP1_IAA_30M_REP1.umi_dedup.transcriptome.filtered.prepare_for_rsem.log", - "star_salmon/umitools/prepare_for_salmon_log/WT_REP1.umi_dedup.transcriptome.filtered.prepare_for_rsem.log", - "star_salmon/umitools/prepare_for_salmon_log/WT_REP2.umi_dedup.transcriptome.filtered.prepare_for_rsem.log", - "star_salmon/umitools/transcriptomic_dedup_log", - "star_salmon/umitools/transcriptomic_dedup_log/RAP1_IAA_30M_REP1.umi_dedup.transcriptome.sorted.log", - "star_salmon/umitools/transcriptomic_dedup_log/RAP1_UNINDUCED_REP1.umi_dedup.transcriptome.sorted.log", - "star_salmon/umitools/transcriptomic_dedup_log/RAP1_UNINDUCED_REP2.umi_dedup.transcriptome.sorted.log", - "star_salmon/umitools/transcriptomic_dedup_log/WT_REP1.umi_dedup.transcriptome.sorted.log", - "star_salmon/umitools/transcriptomic_dedup_log/WT_REP2.umi_dedup.transcriptome.sorted.log", "trimgalore", "trimgalore/RAP1_IAA_30M_REP1_trimmed_1.fastq.gz_trimming_report.txt", "trimgalore/RAP1_IAA_30M_REP1_trimmed_2.fastq.gz_trimming_report.txt", @@ -1390,20 +1209,14 @@ "trimgalore/WT_REP1_trimmed_1.fastq.gz_trimming_report.txt", "trimgalore/WT_REP1_trimmed_2.fastq.gz_trimming_report.txt", "trimgalore/WT_REP2_trimmed_1.fastq.gz_trimming_report.txt", - "trimgalore/WT_REP2_trimmed_2.fastq.gz_trimming_report.txt", - "umitools", - "umitools/RAP1_IAA_30M_REP1.umi_extract.log", - "umitools/RAP1_UNINDUCED_REP1.umi_extract.log", - "umitools/RAP1_UNINDUCED_REP2.umi_extract.log", - "umitools/WT_REP1.umi_extract.log", - "umitools/WT_REP2.umi_extract.log" + "trimgalore/WT_REP2_trimmed_2.fastq.gz_trimming_report.txt" ], [ "genome_gfp.fasta:md5,e23e302af63736a199985a169fdac055", "genome_gfp.gtf:md5,c98b12c302f15731bfc36bcf297cfe28", - "cutadapt_filtered_reads_plot.txt:md5,3f122969fa288888e5abef061b7963f2", - "cutadapt_trimmed_sequences_plot_3_Counts.txt:md5,5e8a821c9a4deb46c11bc65969b8864f", - "cutadapt_trimmed_sequences_plot_3_Obs_Exp.txt:md5,bf8abefa7c5f2f1e1140749983279d9d", + "cutadapt_filtered_reads_plot.txt:md5,6fa381627f7c1f664f3d4b2cb79cce90", + "cutadapt_trimmed_sequences_plot_3_Counts.txt:md5,13dfa866fd91dbb072689efe9aa83b1f", + "cutadapt_trimmed_sequences_plot_3_Obs_Exp.txt:md5,07145dd8dd3db654859b18eb0389046c", "fastqc_raw-status-check-heatmap.txt:md5,5a89b0d8d162f6b1dbdaf39457bbc03b", "fastqc_raw_adapter_content_plot.txt:md5,da0389be84cfdd189b1d045212eb2974", "fastqc_raw_overrepresented_sequences_plot.txt:md5,25d88ea8a72f55e8a374ae802bc7f0b1", @@ -1414,216 +1227,1524 @@ "fastqc_raw_per_sequence_quality_scores_plot.txt:md5,b5f9a02933e3065952237afd2ec9ce82", "fastqc_raw_sequence_counts_plot.txt:md5,cbae4979d5db66d3b894abcf8d1c453c", 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+ "RAP1_IAA_30M_REP1.umi_extract.log:md5,d41d8cd98f00b204e9800998ecf8427e", + "RAP1_UNINDUCED_REP1.umi_extract.log:md5,d41d8cd98f00b204e9800998ecf8427e", + "RAP1_UNINDUCED_REP2.umi_extract.log:md5,d41d8cd98f00b204e9800998ecf8427e", + "WT_REP1.umi_extract.log:md5,d41d8cd98f00b204e9800998ecf8427e", + "WT_REP2.umi_extract.log:md5,d41d8cd98f00b204e9800998ecf8427e" ] ], "meta": { "nf-test": "0.9.0", "nextflow": "24.10.2" }, - "timestamp": "2024-12-10T18:38:07.264678218" + "timestamp": "2024-12-10T22:14:10.443068191" } } \ No newline at end of file From a4aea81337cc5e8bbf130899c6c00713b817a57c Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Wed, 11 Dec 2024 16:59:12 +0000 Subject: [PATCH 061/107] Don't mix fastq stats files passed to MultiQC --- workflows/rnaseq/main.nf | 21 +++++++++++++++------ 1 file changed, 15 insertions(+), 6 deletions(-) diff --git a/workflows/rnaseq/main.nf b/workflows/rnaseq/main.nf index 2e0d7d161..c108eb62e 100755 --- a/workflows/rnaseq/main.nf +++ b/workflows/rnaseq/main.nf @@ -204,9 +204,6 @@ workflow RNASEQ { ch_transcriptome_bam = ALIGN_STAR.out.bam_transcript ch_star_log = ALIGN_STAR.out.log_final ch_unaligned_sequences = ALIGN_STAR.out.fastq - ch_multiqc_files = ch_multiqc_files.mix(ALIGN_STAR.out.stats.collect{it[1]}) - ch_multiqc_files = ch_multiqc_files.mix(ALIGN_STAR.out.flagstat.collect{it[1]}) - ch_multiqc_files = ch_multiqc_files.mix(ALIGN_STAR.out.idxstats.collect{it[1]}) ch_multiqc_files = ch_multiqc_files.mix(ch_star_log.collect{it[1]}) if (params.bam_csi_index) { @@ -300,6 +297,14 @@ workflow RNASEQ { .single_end .mix(UMITOOLS_PREPAREFORSALMON.out.bam) .set { ch_transcriptome_bam } + } else { + // The deduplicated stats should take priority for MultiQC, but use + // them straight out of the aligner otherwise + + ch_multiqc_files = ch_multiqc_files + .mix(ALIGN_STAR.out.stats.collect{it[1]}) + .mix(ALIGN_STAR.out.flagstat.collect{it[1]}) + .mix(ALIGN_STAR.out.idxstats.collect{it[1]}) } // @@ -381,9 +386,6 @@ workflow RNASEQ { ch_genome_bam = FASTQ_ALIGN_HISAT2.out.bam ch_genome_bam_index = FASTQ_ALIGN_HISAT2.out.bai ch_unaligned_sequences = FASTQ_ALIGN_HISAT2.out.fastq - ch_multiqc_files = ch_multiqc_files.mix(FASTQ_ALIGN_HISAT2.out.stats.collect{it[1]}) - ch_multiqc_files = ch_multiqc_files.mix(FASTQ_ALIGN_HISAT2.out.flagstat.collect{it[1]}) - ch_multiqc_files = ch_multiqc_files.mix(FASTQ_ALIGN_HISAT2.out.idxstats.collect{it[1]}) ch_multiqc_files = ch_multiqc_files.mix(FASTQ_ALIGN_HISAT2.out.summary.collect{it[1]}) if (params.bam_csi_index) { @@ -420,6 +422,13 @@ workflow RNASEQ { ch_genome_bam_index = UMI_DEDUP_GENOME.out.csi } ch_versions = ch_versions.mix(UMI_DEDUP_GENOME.out.versions) + } else { + // The deduplicated stats should take priority for MultiQC, but use + // them straight out of the aligner otherwise + ch_multiqc_files = ch_multiqc_files + .mix(FASTQ_ALIGN_HISAT2.out.stats.collect{it[1]}) + .mix(FASTQ_ALIGN_HISAT2.out.flagstat.collect{it[1]}) + .mix(FASTQ_ALIGN_HISAT2.out.idxstats.collect{it[1]}) } } From caccadc9fbc877d9c32c6706eda00796e711c08b Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Wed, 11 Dec 2024 17:27:43 +0000 Subject: [PATCH 062/107] Exclude date-containing umi handling logs from snapshotting --- tests/.nftignore | 2 + tests/umi.nf.test | 8 +- tests/umi.nf.test.snap | 847 ++++++++++++++++++++--------------------- 3 files changed, 411 insertions(+), 446 deletions(-) diff --git a/tests/.nftignore b/tests/.nftignore index a01770b1e..e96f0ec17 100644 --- a/tests/.nftignore +++ b/tests/.nftignore @@ -13,6 +13,7 @@ star_rsem/*.{genes,isoforms}.results star_rsem/log/*.log star_salmon/log/*.Log.{final.out,out,progress.out} trimgalore/*fastq.gz_trimming_report.txt +umitools/*.umi_extract.log {hisat2,star_rsem,star_salmon}/*.{bam,bam.bai} {hisat2,star_rsem,star_salmon}/bigwig/*.{forward,reverse}.bigWig {hisat2,star_rsem,star_salmon}/dupradar/box_plot/*_duprateExpBoxplot.pdf @@ -31,6 +32,7 @@ trimgalore/*fastq.gz_trimming_report.txt {hisat2,star_rsem,star_salmon}/stringtie/*.ballgown/t_data.ctab {hisat2,star_rsem,star_salmon}/stringtie/*.gene.abundance.txt {hisat2,star_rsem,star_salmon}/stringtie/*.{coverage,transcripts}.gtf +{hisat2,star_rsem,star_salmon}/umitools/genomic_dedup_log/*_UMICollapse.log {multiqc,multiqc/**}/multiqc_report.html {multiqc,multiqc/**}/multiqc_report_data/fastqc_{raw,trimmed}_top_overrepresented_sequences_table.txt {multiqc,multiqc/**}/multiqc_report_data/hisat2_pe_plot.txt diff --git a/tests/umi.nf.test b/tests/umi.nf.test index 3ef7c52d2..dba4c07fe 100644 --- a/tests/umi.nf.test +++ b/tests/umi.nf.test @@ -7,11 +7,11 @@ nextflow_pipeline { when { params { - //with_umi = true - extra_umitools_extract_args = '--random-seed 1234' + with_umi = true umitools_extract_method = "regex" umitools_bc_pattern = "^(?PCGA.{8}){s<=2}.*" umitools_dedup_stats = true + skip_bbsplit = true umi_dedup_tool = 'umicollapse' aligner = 'hisat2' outdir = "$outputDir" @@ -43,11 +43,11 @@ nextflow_pipeline { when { params { - //with_umi = true - extra_umitools_extract_args = '--random-seed 1234' + with_umi = true umitools_extract_method = "regex" umitools_bc_pattern = "^(?PCGA.{8}){s<=2}.*" umitools_dedup_stats = true + skip_bbsplit = true outdir = "$outputDir" } } diff --git a/tests/umi.nf.test.snap b/tests/umi.nf.test.snap index 5f2b59931..243481316 100644 --- a/tests/umi.nf.test.snap +++ b/tests/umi.nf.test.snap @@ -1,11 +1,8 @@ { "--umi_dedup_tool 'umitools'": { "content": [ - 213, + 265, { - "BBMAP_BBSPLIT": { - "bbmap": 39.1 - }, "BEDTOOLS_GENOMECOV_FW": { "bedtools": "2.31.1" }, @@ -56,9 +53,6 @@ "MULTIQC_CUSTOM_BIOTYPE": { "python": "3.9.5" }, - "PICARD_MARKDUPLICATES": { - "picard": "3.1.1" - }, "QUALIMAP_RNASEQ": { "qualimap": 2.3 }, @@ -133,6 +127,15 @@ "UCSC_BEDGRAPHTOBIGWIG": { "ucsc": 469 }, + "UMITOOLS_DEDUP": { + "umitools": "1.1.5" + }, + "UMITOOLS_EXTRACT": { + "umitools": "1.1.5" + }, + "UMITOOLS_PREPAREFORSALMON": { + "umitools": "1.1.5" + }, "UNTAR_SALMON_INDEX": { "untar": 1.34 }, @@ -141,12 +144,6 @@ } }, [ - "bbsplit", - "bbsplit/RAP1_IAA_30M_REP1.stats.txt", - "bbsplit/RAP1_UNINDUCED_REP1.stats.txt", - "bbsplit/RAP1_UNINDUCED_REP2.stats.txt", - "bbsplit/WT_REP1.stats.txt", - "bbsplit/WT_REP2.stats.txt", "custom", "custom/out", "custom/out/genome_gfp.fasta", @@ -193,12 +190,6 @@ "fq_lint/raw/RAP1_UNINDUCED_REP2.fq_lint.txt", "fq_lint/raw/WT_REP1.fq_lint.txt", "fq_lint/raw/WT_REP2.fq_lint.txt", - "fq_lint/sortmerna", - "fq_lint/sortmerna/RAP1_IAA_30M_REP1.fq_lint.txt", - "fq_lint/sortmerna/RAP1_UNINDUCED_REP1.fq_lint.txt", - "fq_lint/sortmerna/RAP1_UNINDUCED_REP2.fq_lint.txt", - "fq_lint/sortmerna/WT_REP1.fq_lint.txt", - "fq_lint/sortmerna/WT_REP2.fq_lint.txt", "fq_lint/trimmed", "fq_lint/trimmed/RAP1_IAA_30M_REP1.fq_lint.txt", "fq_lint/trimmed/RAP1_UNINDUCED_REP1.fq_lint.txt", @@ -246,7 +237,6 @@ "multiqc/star_salmon/multiqc_report_data/multiqc_fastqc_fastqc_trimmed.txt", "multiqc/star_salmon/multiqc_report_data/multiqc_featurecounts_biotype_plot.txt", "multiqc/star_salmon/multiqc_report_data/multiqc_general_stats.txt", - "multiqc/star_salmon/multiqc_report_data/multiqc_picard_dups.txt", "multiqc/star_salmon/multiqc_report_data/multiqc_rseqc_bam_stat.txt", "multiqc/star_salmon/multiqc_report_data/multiqc_rseqc_infer_experiment.txt", "multiqc/star_salmon/multiqc_report_data/multiqc_rseqc_junction_annotation.txt", @@ -262,10 +252,8 @@ "multiqc/star_salmon/multiqc_report_data/multiqc_software_versions.txt", "multiqc/star_salmon/multiqc_report_data/multiqc_sources.txt", "multiqc/star_salmon/multiqc_report_data/multiqc_star.txt", - "multiqc/star_salmon/multiqc_report_data/picard_deduplication.txt", - "multiqc/star_salmon/multiqc_report_data/picard_histogram.txt", - "multiqc/star_salmon/multiqc_report_data/picard_histogram_1.txt", - "multiqc/star_salmon/multiqc_report_data/picard_histogram_2.txt", + "multiqc/star_salmon/multiqc_report_data/multiqc_umitools_dedup.txt", + "multiqc/star_salmon/multiqc_report_data/multiqc_umitools_extract.txt", "multiqc/star_salmon/multiqc_report_data/qualimap_gene_coverage_profile_Counts.txt", "multiqc/star_salmon/multiqc_report_data/qualimap_gene_coverage_profile_Normalised.txt", "multiqc/star_salmon/multiqc_report_data/qualimap_genomic_origin.txt", @@ -295,6 +283,8 @@ "multiqc/star_salmon/multiqc_report_data/samtools_alignment_plot.txt", "multiqc/star_salmon/multiqc_report_data/star_alignment_plot.txt", "multiqc/star_salmon/multiqc_report_data/star_summary_table.txt", + "multiqc/star_salmon/multiqc_report_data/umitools_deduplication_barplot.txt", + "multiqc/star_salmon/multiqc_report_data/umitools_stats_violin.txt", "multiqc/star_salmon/multiqc_report_plots", "multiqc/star_salmon/multiqc_report_plots/pdf", "multiqc/star_salmon/multiqc_report_plots/pdf/cutadapt_filtered_reads_plot-cnt.pdf", @@ -330,8 +320,6 @@ "multiqc/star_salmon/multiqc_report_plots/pdf/featurecounts_biotype_plot-cnt.pdf", "multiqc/star_salmon/multiqc_report_plots/pdf/featurecounts_biotype_plot-pct.pdf", "multiqc/star_salmon/multiqc_report_plots/pdf/general_stats_table.pdf", - "multiqc/star_salmon/multiqc_report_plots/pdf/picard_deduplication-cnt.pdf", - "multiqc/star_salmon/multiqc_report_plots/pdf/picard_deduplication-pct.pdf", "multiqc/star_salmon/multiqc_report_plots/pdf/qualimap_gene_coverage_profile_Counts.pdf", "multiqc/star_salmon/multiqc_report_plots/pdf/qualimap_gene_coverage_profile_Normalised.pdf", "multiqc/star_salmon/multiqc_report_plots/pdf/qualimap_genomic_origin-cnt.pdf", @@ -366,6 +354,9 @@ "multiqc/star_salmon/multiqc_report_plots/pdf/star_alignment_plot-cnt.pdf", "multiqc/star_salmon/multiqc_report_plots/pdf/star_alignment_plot-pct.pdf", "multiqc/star_salmon/multiqc_report_plots/pdf/star_summary_table.pdf", + "multiqc/star_salmon/multiqc_report_plots/pdf/umitools_deduplication_barplot-cnt.pdf", + "multiqc/star_salmon/multiqc_report_plots/pdf/umitools_deduplication_barplot-pct.pdf", + "multiqc/star_salmon/multiqc_report_plots/pdf/umitools_stats_violin.pdf", "multiqc/star_salmon/multiqc_report_plots/png", "multiqc/star_salmon/multiqc_report_plots/png/cutadapt_filtered_reads_plot-cnt.png", "multiqc/star_salmon/multiqc_report_plots/png/cutadapt_filtered_reads_plot-pct.png", @@ -400,8 +391,6 @@ "multiqc/star_salmon/multiqc_report_plots/png/featurecounts_biotype_plot-cnt.png", "multiqc/star_salmon/multiqc_report_plots/png/featurecounts_biotype_plot-pct.png", "multiqc/star_salmon/multiqc_report_plots/png/general_stats_table.png", - "multiqc/star_salmon/multiqc_report_plots/png/picard_deduplication-cnt.png", - "multiqc/star_salmon/multiqc_report_plots/png/picard_deduplication-pct.png", "multiqc/star_salmon/multiqc_report_plots/png/qualimap_gene_coverage_profile_Counts.png", "multiqc/star_salmon/multiqc_report_plots/png/qualimap_gene_coverage_profile_Normalised.png", "multiqc/star_salmon/multiqc_report_plots/png/qualimap_genomic_origin-cnt.png", @@ -436,6 +425,9 @@ "multiqc/star_salmon/multiqc_report_plots/png/star_alignment_plot-cnt.png", "multiqc/star_salmon/multiqc_report_plots/png/star_alignment_plot-pct.png", "multiqc/star_salmon/multiqc_report_plots/png/star_summary_table.png", + "multiqc/star_salmon/multiqc_report_plots/png/umitools_deduplication_barplot-cnt.png", + "multiqc/star_salmon/multiqc_report_plots/png/umitools_deduplication_barplot-pct.png", + "multiqc/star_salmon/multiqc_report_plots/png/umitools_stats_violin.png", "multiqc/star_salmon/multiqc_report_plots/svg", "multiqc/star_salmon/multiqc_report_plots/svg/cutadapt_filtered_reads_plot-cnt.svg", "multiqc/star_salmon/multiqc_report_plots/svg/cutadapt_filtered_reads_plot-pct.svg", @@ -470,8 +462,6 @@ "multiqc/star_salmon/multiqc_report_plots/svg/featurecounts_biotype_plot-cnt.svg", "multiqc/star_salmon/multiqc_report_plots/svg/featurecounts_biotype_plot-pct.svg", "multiqc/star_salmon/multiqc_report_plots/svg/general_stats_table.svg", - "multiqc/star_salmon/multiqc_report_plots/svg/picard_deduplication-cnt.svg", - "multiqc/star_salmon/multiqc_report_plots/svg/picard_deduplication-pct.svg", "multiqc/star_salmon/multiqc_report_plots/svg/qualimap_gene_coverage_profile_Counts.svg", "multiqc/star_salmon/multiqc_report_plots/svg/qualimap_gene_coverage_profile_Normalised.svg", "multiqc/star_salmon/multiqc_report_plots/svg/qualimap_genomic_origin-cnt.svg", @@ -506,6 +496,9 @@ "multiqc/star_salmon/multiqc_report_plots/svg/star_alignment_plot-cnt.svg", "multiqc/star_salmon/multiqc_report_plots/svg/star_alignment_plot-pct.svg", "multiqc/star_salmon/multiqc_report_plots/svg/star_summary_table.svg", + "multiqc/star_salmon/multiqc_report_plots/svg/umitools_deduplication_barplot-cnt.svg", + "multiqc/star_salmon/multiqc_report_plots/svg/umitools_deduplication_barplot-pct.svg", + "multiqc/star_salmon/multiqc_report_plots/svg/umitools_stats_violin.svg", "pipeline_info", "pipeline_info/nf_core_rnaseq_software_mqc_versions.yml", "salmon", @@ -617,8 +610,8 @@ "salmon/tx2gene.tsv", "star_salmon", "star_salmon/RAP1_IAA_30M_REP1", - "star_salmon/RAP1_IAA_30M_REP1.markdup.sorted.bam", - "star_salmon/RAP1_IAA_30M_REP1.markdup.sorted.bam.bai", + "star_salmon/RAP1_IAA_30M_REP1.umi_dedup.sorted.bam", + "star_salmon/RAP1_IAA_30M_REP1.umi_dedup.sorted.bam.bai", "star_salmon/RAP1_IAA_30M_REP1/aux_info", "star_salmon/RAP1_IAA_30M_REP1/aux_info/ambig_info.tsv", "star_salmon/RAP1_IAA_30M_REP1/aux_info/expected_bias.gz", @@ -634,8 +627,8 @@ "star_salmon/RAP1_IAA_30M_REP1/quant.genes.sf", "star_salmon/RAP1_IAA_30M_REP1/quant.sf", "star_salmon/RAP1_UNINDUCED_REP1", - "star_salmon/RAP1_UNINDUCED_REP1.markdup.sorted.bam", - "star_salmon/RAP1_UNINDUCED_REP1.markdup.sorted.bam.bai", + "star_salmon/RAP1_UNINDUCED_REP1.umi_dedup.sorted.bam", + "star_salmon/RAP1_UNINDUCED_REP1.umi_dedup.sorted.bam.bai", "star_salmon/RAP1_UNINDUCED_REP1/aux_info", "star_salmon/RAP1_UNINDUCED_REP1/aux_info/ambig_info.tsv", "star_salmon/RAP1_UNINDUCED_REP1/aux_info/expected_bias.gz", @@ -651,8 +644,8 @@ "star_salmon/RAP1_UNINDUCED_REP1/quant.genes.sf", "star_salmon/RAP1_UNINDUCED_REP1/quant.sf", "star_salmon/RAP1_UNINDUCED_REP2", - "star_salmon/RAP1_UNINDUCED_REP2.markdup.sorted.bam", - "star_salmon/RAP1_UNINDUCED_REP2.markdup.sorted.bam.bai", + "star_salmon/RAP1_UNINDUCED_REP2.umi_dedup.sorted.bam", + "star_salmon/RAP1_UNINDUCED_REP2.umi_dedup.sorted.bam.bai", "star_salmon/RAP1_UNINDUCED_REP2/aux_info", "star_salmon/RAP1_UNINDUCED_REP2/aux_info/ambig_info.tsv", "star_salmon/RAP1_UNINDUCED_REP2/aux_info/expected_bias.gz", @@ -668,8 +661,8 @@ "star_salmon/RAP1_UNINDUCED_REP2/quant.genes.sf", "star_salmon/RAP1_UNINDUCED_REP2/quant.sf", "star_salmon/WT_REP1", - "star_salmon/WT_REP1.markdup.sorted.bam", - "star_salmon/WT_REP1.markdup.sorted.bam.bai", + "star_salmon/WT_REP1.umi_dedup.sorted.bam", + "star_salmon/WT_REP1.umi_dedup.sorted.bam.bai", "star_salmon/WT_REP1/aux_info", "star_salmon/WT_REP1/aux_info/ambig_info.tsv", "star_salmon/WT_REP1/aux_info/expected_bias.gz", @@ -685,8 +678,8 @@ "star_salmon/WT_REP1/quant.genes.sf", "star_salmon/WT_REP1/quant.sf", "star_salmon/WT_REP2", - "star_salmon/WT_REP2.markdup.sorted.bam", - "star_salmon/WT_REP2.markdup.sorted.bam.bai", + "star_salmon/WT_REP2.umi_dedup.sorted.bam", + "star_salmon/WT_REP2.umi_dedup.sorted.bam.bai", "star_salmon/WT_REP2/aux_info", "star_salmon/WT_REP2/aux_info/ambig_info.tsv", "star_salmon/WT_REP2/aux_info/expected_bias.gz", @@ -798,12 +791,6 @@ "star_salmon/log/WT_REP2.Log.out", "star_salmon/log/WT_REP2.Log.progress.out", "star_salmon/log/WT_REP2.SJ.out.tab", - "star_salmon/picard_metrics", - "star_salmon/picard_metrics/RAP1_IAA_30M_REP1.markdup.sorted.MarkDuplicates.metrics.txt", - "star_salmon/picard_metrics/RAP1_UNINDUCED_REP1.markdup.sorted.MarkDuplicates.metrics.txt", - "star_salmon/picard_metrics/RAP1_UNINDUCED_REP2.markdup.sorted.MarkDuplicates.metrics.txt", - "star_salmon/picard_metrics/WT_REP1.markdup.sorted.MarkDuplicates.metrics.txt", - "star_salmon/picard_metrics/WT_REP2.markdup.sorted.MarkDuplicates.metrics.txt", "star_salmon/qualimap", "star_salmon/qualimap/RAP1_IAA_30M_REP1", "star_salmon/qualimap/RAP1_IAA_30M_REP1/css", @@ -1124,36 +1111,51 @@ "star_salmon/salmon.merged.transcript_lengths.tsv", "star_salmon/salmon.merged.transcript_tpm.tsv", "star_salmon/samtools_stats", - "star_salmon/samtools_stats/RAP1_IAA_30M_REP1.markdup.sorted.bam.flagstat", - "star_salmon/samtools_stats/RAP1_IAA_30M_REP1.markdup.sorted.bam.idxstats", - "star_salmon/samtools_stats/RAP1_IAA_30M_REP1.markdup.sorted.bam.stats", "star_salmon/samtools_stats/RAP1_IAA_30M_REP1.sorted.bam.flagstat", "star_salmon/samtools_stats/RAP1_IAA_30M_REP1.sorted.bam.idxstats", "star_salmon/samtools_stats/RAP1_IAA_30M_REP1.sorted.bam.stats", - "star_salmon/samtools_stats/RAP1_UNINDUCED_REP1.markdup.sorted.bam.flagstat", - "star_salmon/samtools_stats/RAP1_UNINDUCED_REP1.markdup.sorted.bam.idxstats", - "star_salmon/samtools_stats/RAP1_UNINDUCED_REP1.markdup.sorted.bam.stats", + "star_salmon/samtools_stats/RAP1_IAA_30M_REP1.umi_dedup.sorted.bam.flagstat", + "star_salmon/samtools_stats/RAP1_IAA_30M_REP1.umi_dedup.sorted.bam.idxstats", + "star_salmon/samtools_stats/RAP1_IAA_30M_REP1.umi_dedup.sorted.bam.stats", + "star_salmon/samtools_stats/RAP1_IAA_30M_REP1.umi_dedup.transcriptome.sorted.bam.flagstat", + "star_salmon/samtools_stats/RAP1_IAA_30M_REP1.umi_dedup.transcriptome.sorted.bam.idxstats", + "star_salmon/samtools_stats/RAP1_IAA_30M_REP1.umi_dedup.transcriptome.sorted.bam.stats", "star_salmon/samtools_stats/RAP1_UNINDUCED_REP1.sorted.bam.flagstat", "star_salmon/samtools_stats/RAP1_UNINDUCED_REP1.sorted.bam.idxstats", "star_salmon/samtools_stats/RAP1_UNINDUCED_REP1.sorted.bam.stats", - "star_salmon/samtools_stats/RAP1_UNINDUCED_REP2.markdup.sorted.bam.flagstat", - "star_salmon/samtools_stats/RAP1_UNINDUCED_REP2.markdup.sorted.bam.idxstats", - "star_salmon/samtools_stats/RAP1_UNINDUCED_REP2.markdup.sorted.bam.stats", + "star_salmon/samtools_stats/RAP1_UNINDUCED_REP1.umi_dedup.sorted.bam.flagstat", + "star_salmon/samtools_stats/RAP1_UNINDUCED_REP1.umi_dedup.sorted.bam.idxstats", + "star_salmon/samtools_stats/RAP1_UNINDUCED_REP1.umi_dedup.sorted.bam.stats", + "star_salmon/samtools_stats/RAP1_UNINDUCED_REP1.umi_dedup.transcriptome.sorted.bam.flagstat", + "star_salmon/samtools_stats/RAP1_UNINDUCED_REP1.umi_dedup.transcriptome.sorted.bam.idxstats", + "star_salmon/samtools_stats/RAP1_UNINDUCED_REP1.umi_dedup.transcriptome.sorted.bam.stats", "star_salmon/samtools_stats/RAP1_UNINDUCED_REP2.sorted.bam.flagstat", "star_salmon/samtools_stats/RAP1_UNINDUCED_REP2.sorted.bam.idxstats", "star_salmon/samtools_stats/RAP1_UNINDUCED_REP2.sorted.bam.stats", - "star_salmon/samtools_stats/WT_REP1.markdup.sorted.bam.flagstat", - "star_salmon/samtools_stats/WT_REP1.markdup.sorted.bam.idxstats", - "star_salmon/samtools_stats/WT_REP1.markdup.sorted.bam.stats", + "star_salmon/samtools_stats/RAP1_UNINDUCED_REP2.umi_dedup.sorted.bam.flagstat", + "star_salmon/samtools_stats/RAP1_UNINDUCED_REP2.umi_dedup.sorted.bam.idxstats", + "star_salmon/samtools_stats/RAP1_UNINDUCED_REP2.umi_dedup.sorted.bam.stats", + "star_salmon/samtools_stats/RAP1_UNINDUCED_REP2.umi_dedup.transcriptome.sorted.bam.flagstat", + "star_salmon/samtools_stats/RAP1_UNINDUCED_REP2.umi_dedup.transcriptome.sorted.bam.idxstats", + "star_salmon/samtools_stats/RAP1_UNINDUCED_REP2.umi_dedup.transcriptome.sorted.bam.stats", "star_salmon/samtools_stats/WT_REP1.sorted.bam.flagstat", "star_salmon/samtools_stats/WT_REP1.sorted.bam.idxstats", "star_salmon/samtools_stats/WT_REP1.sorted.bam.stats", - "star_salmon/samtools_stats/WT_REP2.markdup.sorted.bam.flagstat", - "star_salmon/samtools_stats/WT_REP2.markdup.sorted.bam.idxstats", - "star_salmon/samtools_stats/WT_REP2.markdup.sorted.bam.stats", + "star_salmon/samtools_stats/WT_REP1.umi_dedup.sorted.bam.flagstat", + "star_salmon/samtools_stats/WT_REP1.umi_dedup.sorted.bam.idxstats", + "star_salmon/samtools_stats/WT_REP1.umi_dedup.sorted.bam.stats", + "star_salmon/samtools_stats/WT_REP1.umi_dedup.transcriptome.sorted.bam.flagstat", + "star_salmon/samtools_stats/WT_REP1.umi_dedup.transcriptome.sorted.bam.idxstats", + "star_salmon/samtools_stats/WT_REP1.umi_dedup.transcriptome.sorted.bam.stats", "star_salmon/samtools_stats/WT_REP2.sorted.bam.flagstat", "star_salmon/samtools_stats/WT_REP2.sorted.bam.idxstats", "star_salmon/samtools_stats/WT_REP2.sorted.bam.stats", + "star_salmon/samtools_stats/WT_REP2.umi_dedup.sorted.bam.flagstat", + "star_salmon/samtools_stats/WT_REP2.umi_dedup.sorted.bam.idxstats", + "star_salmon/samtools_stats/WT_REP2.umi_dedup.sorted.bam.stats", + "star_salmon/samtools_stats/WT_REP2.umi_dedup.transcriptome.sorted.bam.flagstat", + "star_salmon/samtools_stats/WT_REP2.umi_dedup.transcriptome.sorted.bam.idxstats", + "star_salmon/samtools_stats/WT_REP2.umi_dedup.transcriptome.sorted.bam.stats", "star_salmon/stringtie", "star_salmon/stringtie/RAP1_IAA_30M_REP1.ballgown", "star_salmon/stringtie/RAP1_IAA_30M_REP1.ballgown/e2t.ctab", @@ -1201,6 +1203,53 @@ "star_salmon/stringtie/WT_REP2.gene.abundance.txt", "star_salmon/stringtie/WT_REP2.transcripts.gtf", "star_salmon/tx2gene.tsv", + "star_salmon/umitools", + "star_salmon/umitools/RAP1_IAA_30M_REP1.umi_dedup.sorted_edit_distance.tsv", + "star_salmon/umitools/RAP1_IAA_30M_REP1.umi_dedup.sorted_per_umi.tsv", + "star_salmon/umitools/RAP1_IAA_30M_REP1.umi_dedup.sorted_per_umi_per_position.tsv", + "star_salmon/umitools/RAP1_IAA_30M_REP1.umi_dedup.transcriptome.sorted_edit_distance.tsv", + "star_salmon/umitools/RAP1_IAA_30M_REP1.umi_dedup.transcriptome.sorted_per_umi.tsv", + "star_salmon/umitools/RAP1_IAA_30M_REP1.umi_dedup.transcriptome.sorted_per_umi_per_position.tsv", + "star_salmon/umitools/RAP1_UNINDUCED_REP1.umi_dedup.sorted_edit_distance.tsv", + "star_salmon/umitools/RAP1_UNINDUCED_REP1.umi_dedup.sorted_per_umi.tsv", + "star_salmon/umitools/RAP1_UNINDUCED_REP1.umi_dedup.sorted_per_umi_per_position.tsv", + "star_salmon/umitools/RAP1_UNINDUCED_REP1.umi_dedup.transcriptome.sorted_edit_distance.tsv", + "star_salmon/umitools/RAP1_UNINDUCED_REP1.umi_dedup.transcriptome.sorted_per_umi.tsv", + "star_salmon/umitools/RAP1_UNINDUCED_REP1.umi_dedup.transcriptome.sorted_per_umi_per_position.tsv", + "star_salmon/umitools/RAP1_UNINDUCED_REP2.umi_dedup.sorted_edit_distance.tsv", + "star_salmon/umitools/RAP1_UNINDUCED_REP2.umi_dedup.sorted_per_umi.tsv", + "star_salmon/umitools/RAP1_UNINDUCED_REP2.umi_dedup.sorted_per_umi_per_position.tsv", + "star_salmon/umitools/RAP1_UNINDUCED_REP2.umi_dedup.transcriptome.sorted_edit_distance.tsv", + "star_salmon/umitools/RAP1_UNINDUCED_REP2.umi_dedup.transcriptome.sorted_per_umi.tsv", + "star_salmon/umitools/RAP1_UNINDUCED_REP2.umi_dedup.transcriptome.sorted_per_umi_per_position.tsv", + "star_salmon/umitools/WT_REP1.umi_dedup.sorted_edit_distance.tsv", + "star_salmon/umitools/WT_REP1.umi_dedup.sorted_per_umi.tsv", + "star_salmon/umitools/WT_REP1.umi_dedup.sorted_per_umi_per_position.tsv", + "star_salmon/umitools/WT_REP1.umi_dedup.transcriptome.sorted_edit_distance.tsv", + "star_salmon/umitools/WT_REP1.umi_dedup.transcriptome.sorted_per_umi.tsv", + "star_salmon/umitools/WT_REP1.umi_dedup.transcriptome.sorted_per_umi_per_position.tsv", + "star_salmon/umitools/WT_REP2.umi_dedup.sorted_edit_distance.tsv", + "star_salmon/umitools/WT_REP2.umi_dedup.sorted_per_umi.tsv", + "star_salmon/umitools/WT_REP2.umi_dedup.sorted_per_umi_per_position.tsv", + "star_salmon/umitools/WT_REP2.umi_dedup.transcriptome.sorted_edit_distance.tsv", + "star_salmon/umitools/WT_REP2.umi_dedup.transcriptome.sorted_per_umi.tsv", + "star_salmon/umitools/WT_REP2.umi_dedup.transcriptome.sorted_per_umi_per_position.tsv", + "star_salmon/umitools/genomic_dedup_log", + "star_salmon/umitools/genomic_dedup_log/RAP1_IAA_30M_REP1.umi_dedup.sorted.log", + "star_salmon/umitools/genomic_dedup_log/RAP1_UNINDUCED_REP1.umi_dedup.sorted.log", + "star_salmon/umitools/genomic_dedup_log/RAP1_UNINDUCED_REP2.umi_dedup.sorted.log", + "star_salmon/umitools/genomic_dedup_log/WT_REP1.umi_dedup.sorted.log", + "star_salmon/umitools/genomic_dedup_log/WT_REP2.umi_dedup.sorted.log", + "star_salmon/umitools/prepare_for_salmon_log", + "star_salmon/umitools/prepare_for_salmon_log/RAP1_IAA_30M_REP1.umi_dedup.transcriptome.filtered.prepare_for_rsem.log", + "star_salmon/umitools/prepare_for_salmon_log/WT_REP1.umi_dedup.transcriptome.filtered.prepare_for_rsem.log", + "star_salmon/umitools/prepare_for_salmon_log/WT_REP2.umi_dedup.transcriptome.filtered.prepare_for_rsem.log", + "star_salmon/umitools/transcriptomic_dedup_log", + "star_salmon/umitools/transcriptomic_dedup_log/RAP1_IAA_30M_REP1.umi_dedup.transcriptome.sorted.log", + "star_salmon/umitools/transcriptomic_dedup_log/RAP1_UNINDUCED_REP1.umi_dedup.transcriptome.sorted.log", + "star_salmon/umitools/transcriptomic_dedup_log/RAP1_UNINDUCED_REP2.umi_dedup.transcriptome.sorted.log", + "star_salmon/umitools/transcriptomic_dedup_log/WT_REP1.umi_dedup.transcriptome.sorted.log", + "star_salmon/umitools/transcriptomic_dedup_log/WT_REP2.umi_dedup.transcriptome.sorted.log", "trimgalore", "trimgalore/RAP1_IAA_30M_REP1_trimmed_1.fastq.gz_trimming_report.txt", "trimgalore/RAP1_IAA_30M_REP1_trimmed_2.fastq.gz_trimming_report.txt", @@ -1209,14 +1258,20 @@ "trimgalore/WT_REP1_trimmed_1.fastq.gz_trimming_report.txt", "trimgalore/WT_REP1_trimmed_2.fastq.gz_trimming_report.txt", "trimgalore/WT_REP2_trimmed_1.fastq.gz_trimming_report.txt", - "trimgalore/WT_REP2_trimmed_2.fastq.gz_trimming_report.txt" + "trimgalore/WT_REP2_trimmed_2.fastq.gz_trimming_report.txt", + "umitools", + "umitools/RAP1_IAA_30M_REP1.umi_extract.log", + "umitools/RAP1_UNINDUCED_REP1.umi_extract.log", + "umitools/RAP1_UNINDUCED_REP2.umi_extract.log", + "umitools/WT_REP1.umi_extract.log", + "umitools/WT_REP2.umi_extract.log" ], [ "genome_gfp.fasta:md5,e23e302af63736a199985a169fdac055", "genome_gfp.gtf:md5,c98b12c302f15731bfc36bcf297cfe28", - "cutadapt_filtered_reads_plot.txt:md5,6fa381627f7c1f664f3d4b2cb79cce90", - "cutadapt_trimmed_sequences_plot_3_Counts.txt:md5,13dfa866fd91dbb072689efe9aa83b1f", - "cutadapt_trimmed_sequences_plot_3_Obs_Exp.txt:md5,07145dd8dd3db654859b18eb0389046c", + "cutadapt_filtered_reads_plot.txt:md5,3f122969fa288888e5abef061b7963f2", + "cutadapt_trimmed_sequences_plot_3_Counts.txt:md5,5e8a821c9a4deb46c11bc65969b8864f", + "cutadapt_trimmed_sequences_plot_3_Obs_Exp.txt:md5,bf8abefa7c5f2f1e1140749983279d9d", "fastqc_raw-status-check-heatmap.txt:md5,5a89b0d8d162f6b1dbdaf39457bbc03b", "fastqc_raw_adapter_content_plot.txt:md5,da0389be84cfdd189b1d045212eb2974", "fastqc_raw_overrepresented_sequences_plot.txt:md5,25d88ea8a72f55e8a374ae802bc7f0b1", @@ -1227,176 +1282,217 @@ "fastqc_raw_per_sequence_quality_scores_plot.txt:md5,b5f9a02933e3065952237afd2ec9ce82", "fastqc_raw_sequence_counts_plot.txt:md5,cbae4979d5db66d3b894abcf8d1c453c", "fastqc_raw_sequence_duplication_levels_plot.txt:md5,8812cee16f6ca65e2c33635754de1772", - "fastqc_sequence_length_distribution_plot.txt:md5,6fe2c985606abad947bcca99b015ae33", - "fastqc_trimmed-status-check-heatmap.txt:md5,22a03548736b88b23be6bc0c9ef1b4a6", - "fastqc_trimmed_overrepresented_sequences_plot.txt:md5,c755e9d044ea1a82b2c8edde867b4878", - "fastqc_trimmed_per_base_n_content_plot.txt:md5,418610c1ce119cb786ad434db75d366e", - "fastqc_trimmed_per_base_sequence_quality_plot.txt:md5,bd22e06e41c096ad4f745d40fe96a1e5", - "fastqc_trimmed_per_sequence_gc_content_plot_Counts.txt:md5,004c60768ceb6197765154e3eaa37b7a", - "fastqc_trimmed_per_sequence_gc_content_plot_Percentages.txt:md5,95d29060b687f745288ad1ec47750037", - "fastqc_trimmed_per_sequence_quality_scores_plot.txt:md5,0f9834cc19f76dd5c87cf8cba7435a7c", - "fastqc_trimmed_sequence_counts_plot.txt:md5,9fd642bdd1da354f296bb8092205608f", - "fastqc_trimmed_sequence_duplication_levels_plot.txt:md5,0758257b497283b1ef28171e694db6db", - "multiqc_citations.txt:md5,2d2ab6df367e36e98e081c33dec187a0", - "multiqc_cutadapt.txt:md5,583b7b9ba76b26162bb9610ed746454b", + "fastqc_sequence_length_distribution_plot.txt:md5,7a2263743d5c452dc940eabffcb0ea82", + "fastqc_trimmed-status-check-heatmap.txt:md5,1913b4c077a341437475c758fb119559", + "fastqc_trimmed_overrepresented_sequences_plot.txt:md5,6a7c50868a36ca6052e9f4adba6b8567", + "fastqc_trimmed_per_base_n_content_plot.txt:md5,0cdd698fb9a2a4987572b9b4e8c8990e", + "fastqc_trimmed_per_base_sequence_quality_plot.txt:md5,82a9073151a413efbb0f11be8d0db1f2", + "fastqc_trimmed_per_sequence_gc_content_plot_Counts.txt:md5,08b43a02f34a3afa7eaddef602cdcafd", + "fastqc_trimmed_per_sequence_gc_content_plot_Percentages.txt:md5,0768af6bf06bbad52a3ac6ec0a444138", + "fastqc_trimmed_per_sequence_quality_scores_plot.txt:md5,ac167d250e9f88899da0ea81ef2b002e", + "fastqc_trimmed_sequence_counts_plot.txt:md5,370ddc5e58838dc5b55e53b6f8b8902a", + "fastqc_trimmed_sequence_duplication_levels_plot.txt:md5,b106d8ee7b83efe237afb862ebbe2f47", + "multiqc_citations.txt:md5,34e8b8c69015de25f9fce8c31d2ff864", + "multiqc_cutadapt.txt:md5,0937983d3ddfeccd4ccc5b0d74477558", "multiqc_fastqc_fastqc_raw.txt:md5,81c3c1a2575a1891a7f2a9637a0f2cc0", - "multiqc_fastqc_fastqc_trimmed.txt:md5,54743154d0e8858980acffeb5b6f6a97", - "multiqc_featurecounts_biotype_plot.txt:md5,5d83930a8c0aebf5fff0a5d12857b42d", - "multiqc_samtools_idxstats.txt:md5,8b8f6cb092004918c18319a8cd64eb4c", - "picard_histogram.txt:md5,d41d8cd98f00b204e9800998ecf8427e", - "picard_histogram_1.txt:md5,d41d8cd98f00b204e9800998ecf8427e", - "picard_histogram_2.txt:md5,d41d8cd98f00b204e9800998ecf8427e", - "qualimap_gene_coverage_profile_Counts.txt:md5,d86b5743ba4486f5796327cbd2854882", - "qualimap_gene_coverage_profile_Normalised.txt:md5,05618239e3183e8f312552676c4256b5", - "qualimap_rnaseq_cov_hist.txt:md5,ea4b7cf07e5c7590ded5af29161db405", - "rseqc_infer_experiment_plot.txt:md5,de5a0bad9cca763928e7c33375eb5218", + "multiqc_fastqc_fastqc_trimmed.txt:md5,a8a003b61b03295c416f3eb66c1cca65", + "multiqc_featurecounts_biotype_plot.txt:md5,5d1c33f3ff898b9bb95ed161e4235011", + "multiqc_samtools_idxstats.txt:md5,bdf185faa6d15d22efedb39d38c84943", + "multiqc_umitools_dedup.txt:md5,bad73f567baddb2272488b0e78e208cb", + "multiqc_umitools_extract.txt:md5,d41d8cd98f00b204e9800998ecf8427e", + "qualimap_gene_coverage_profile_Counts.txt:md5,48d6c0b4df0f6f535a4526fc74b0fd47", + "qualimap_gene_coverage_profile_Normalised.txt:md5,8974b37cbdbbc2a23ca103a3a4c10dd4", + "qualimap_rnaseq_cov_hist.txt:md5,55d1a7026062c28624be5901ac6781a4", + "rseqc_infer_experiment_plot.txt:md5,595bfcda9eddeed4f9ae4e162fe7120d", "samtools-idxstats-mapped-reads-plot_Normalised_Counts.txt:md5,75acd04232d1804b5f960ee4c5db4722", - "samtools-idxstats-mapped-reads-plot_Observed_over_Expected_Counts.txt:md5,ae45731d8d4595f77e6b271004f3a070", - "samtools-idxstats-mapped-reads-plot_Raw_Counts.txt:md5,3e57c1686c863035b6e6569210dfd4a8", - "ambig_info.tsv:md5,de973a4b22a4457217ae3dc04caf9401", + "samtools-idxstats-mapped-reads-plot_Observed_over_Expected_Counts.txt:md5,53addd7b78d8ae224920ec9b97489d72", + "samtools-idxstats-mapped-reads-plot_Raw_Counts.txt:md5,2cae5122b2e6371f068722ed666678a1", + "umitools_deduplication_barplot.txt:md5,a2d6295db66f53d1044b261a05352782", + "umitools_stats_violin.txt:md5,136374122cf99fb0771667fcb9b48aee", + "ambig_info.tsv:md5,0b462f9b2d8eff1b4d76b7433434899c", "expected_bias.gz:md5,3407f87245d0003e0ffbfdf6d8c04f20", 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"RAP1_UNINDUCED_REP1.umi_dedup.sorted_per_umi_per_position.tsv:md5,1a4f7954086d4a76d93694443e074312", + "RAP1_UNINDUCED_REP1.umi_dedup.transcriptome.sorted_edit_distance.tsv:md5,176fbeac8e4f1cfa1def599cc5ad4847", + "RAP1_UNINDUCED_REP1.umi_dedup.transcriptome.sorted_per_umi.tsv:md5,fcded67bc5e50902c296d40d53e9079c", + "RAP1_UNINDUCED_REP1.umi_dedup.transcriptome.sorted_per_umi_per_position.tsv:md5,5a3d1e0ec5f3af2c279d8321bae2de34", + "RAP1_UNINDUCED_REP2.umi_dedup.sorted_edit_distance.tsv:md5,15120d542adb7ad45acf924d905e7fcf", + "RAP1_UNINDUCED_REP2.umi_dedup.sorted_per_umi.tsv:md5,d68aae0c4698c73c10c83df7f7cba06a", + "RAP1_UNINDUCED_REP2.umi_dedup.sorted_per_umi_per_position.tsv:md5,18f5c21477fc51e56c65ee0660649ffc", + "RAP1_UNINDUCED_REP2.umi_dedup.transcriptome.sorted_edit_distance.tsv:md5,13f3f84f11f6bb8e98f001b97902d559", + "RAP1_UNINDUCED_REP2.umi_dedup.transcriptome.sorted_per_umi.tsv:md5,eb7aedb12f64c703f76ade9b60d3ded0", + "RAP1_UNINDUCED_REP2.umi_dedup.transcriptome.sorted_per_umi_per_position.tsv:md5,dc636e57732d1548bd4e5454aef63788", + "WT_REP1.umi_dedup.sorted_edit_distance.tsv:md5,5d18333d2131dfb7083ece42f5538eb4", + "WT_REP1.umi_dedup.sorted_per_umi.tsv:md5,d81c983368a0b2218eb8954579c376e7", + "WT_REP1.umi_dedup.sorted_per_umi_per_position.tsv:md5,e2412d716ca85a90f8dbb100a9614991", + "WT_REP1.umi_dedup.transcriptome.sorted_edit_distance.tsv:md5,d698cb0a0d23dbb2d78deff0928b2a1f", + "WT_REP1.umi_dedup.transcriptome.sorted_per_umi.tsv:md5,ee2df66b8427ea94fff18430cc1937a4", + "WT_REP1.umi_dedup.transcriptome.sorted_per_umi_per_position.tsv:md5,485a03ff0fec979ee84d918158502bf1", + "WT_REP2.umi_dedup.sorted_edit_distance.tsv:md5,a935a046c0a8f269cdeaa7c1c935f0ec", + "WT_REP2.umi_dedup.sorted_per_umi.tsv:md5,0cb0f12e99861120a0437051a7aaa152", + "WT_REP2.umi_dedup.sorted_per_umi_per_position.tsv:md5,6f5656947a7f0076df446e6f40430027", + "WT_REP2.umi_dedup.transcriptome.sorted_edit_distance.tsv:md5,3e3c6a7e8996e566350742e9911366d3", + "WT_REP2.umi_dedup.transcriptome.sorted_per_umi.tsv:md5,0c986c4cb7a77f650a19e2c454b9b179", + "WT_REP2.umi_dedup.transcriptome.sorted_per_umi_per_position.tsv:md5,af9028dbdab81de3854a32cd1d19ac8b", + "RAP1_IAA_30M_REP1.umi_dedup.sorted.log:md5,0001a4ebfeaedfa164311e075b4c6c8a", + "RAP1_UNINDUCED_REP1.umi_dedup.sorted.log:md5,150b19399146d766746e991cf2f20834", + "RAP1_UNINDUCED_REP2.umi_dedup.sorted.log:md5,218313d63828b1bd97bf0f379bad5e8f", + "WT_REP1.umi_dedup.sorted.log:md5,982c5089b199ca8647511f4b10e3a713", + "WT_REP2.umi_dedup.sorted.log:md5,3f2cc0f12cdc53db11a7f7409dde7d62", + "RAP1_IAA_30M_REP1.umi_dedup.transcriptome.filtered.prepare_for_rsem.log:md5,a7c8743ab54408ec184c669d42b53907", + "WT_REP1.umi_dedup.transcriptome.filtered.prepare_for_rsem.log:md5,5c334da8161e17825c08fc6a2522d187", + "WT_REP2.umi_dedup.transcriptome.filtered.prepare_for_rsem.log:md5,ddc19f73d0a622abc3bae9000a3daa4e", + "RAP1_IAA_30M_REP1.umi_dedup.transcriptome.sorted.log:md5,058d0796e29f1037460d936947cf2ba8", + "RAP1_UNINDUCED_REP1.umi_dedup.transcriptome.sorted.log:md5,5c93e408bbfd13061ad2f2b432747abe", + "RAP1_UNINDUCED_REP2.umi_dedup.transcriptome.sorted.log:md5,15bcb30ad07c9a71852ff38c40f9e79f", + "WT_REP1.umi_dedup.transcriptome.sorted.log:md5,8938cfbb06f7ec98635687b64151d098", + "WT_REP2.umi_dedup.transcriptome.sorted.log:md5,08cd52b814d3e2f4f4adc688643641c6" ] ], "meta": { "nf-test": "0.9.0", "nextflow": "24.10.2" }, - "timestamp": "2024-12-10T22:13:21.269998381" + "timestamp": "2024-12-11T17:23:58.835430691" }, "Params: --aligner hisat2 --umi_dedup_tool 'umicollapse'": { "content": [ - 202, + 196, { - "BBMAP_BBSPLIT": { - "bbmap": 39.1 - }, "BEDTOOLS_GENOMECOV_FW": { "bedtools": "2.31.1" }, @@ -1450,9 +1546,6 @@ "MULTIQC_CUSTOM_BIOTYPE": { "python": "3.9.5" }, - "PICARD_MARKDUPLICATES": { - "picard": "3.1.1" - }, "QUALIMAP_RNASEQ": { "qualimap": 2.3 }, @@ -1517,6 +1610,12 @@ "UCSC_BEDGRAPHTOBIGWIG": { "ucsc": 469 }, + "UMICOLLAPSE": { + "umicollapse": "1.1.0-0" + }, + "UMITOOLS_EXTRACT": { + "umitools": "1.1.5" + }, "UNTAR_HISAT2_INDEX": { "untar": 1.34 }, @@ -1528,12 +1627,6 @@ } }, [ - "bbsplit", - "bbsplit/RAP1_IAA_30M_REP1.stats.txt", - "bbsplit/RAP1_UNINDUCED_REP1.stats.txt", - "bbsplit/RAP1_UNINDUCED_REP2.stats.txt", - "bbsplit/WT_REP1.stats.txt", - "bbsplit/WT_REP2.stats.txt", "custom", "custom/out", "custom/out/genome_gfp.fasta", @@ -1580,12 +1673,6 @@ "fq_lint/raw/RAP1_UNINDUCED_REP2.fq_lint.txt", "fq_lint/raw/WT_REP1.fq_lint.txt", "fq_lint/raw/WT_REP2.fq_lint.txt", - "fq_lint/sortmerna", - "fq_lint/sortmerna/RAP1_IAA_30M_REP1.fq_lint.txt", - "fq_lint/sortmerna/RAP1_UNINDUCED_REP1.fq_lint.txt", - "fq_lint/sortmerna/RAP1_UNINDUCED_REP2.fq_lint.txt", - "fq_lint/sortmerna/WT_REP1.fq_lint.txt", - "fq_lint/sortmerna/WT_REP2.fq_lint.txt", "fq_lint/trimmed", "fq_lint/trimmed/RAP1_IAA_30M_REP1.fq_lint.txt", "fq_lint/trimmed/RAP1_UNINDUCED_REP1.fq_lint.txt", @@ -1593,16 +1680,16 @@ "fq_lint/trimmed/WT_REP1.fq_lint.txt", "fq_lint/trimmed/WT_REP2.fq_lint.txt", "hisat2", - "hisat2/RAP1_IAA_30M_REP1.markdup.sorted.bam", - "hisat2/RAP1_IAA_30M_REP1.markdup.sorted.bam.bai", - "hisat2/RAP1_UNINDUCED_REP1.markdup.sorted.bam", - "hisat2/RAP1_UNINDUCED_REP1.markdup.sorted.bam.bai", - "hisat2/RAP1_UNINDUCED_REP2.markdup.sorted.bam", - "hisat2/RAP1_UNINDUCED_REP2.markdup.sorted.bam.bai", - "hisat2/WT_REP1.markdup.sorted.bam", - "hisat2/WT_REP1.markdup.sorted.bam.bai", - "hisat2/WT_REP2.markdup.sorted.bam", - "hisat2/WT_REP2.markdup.sorted.bam.bai", + "hisat2/RAP1_IAA_30M_REP1.umi_dedup.sorted.bam", + "hisat2/RAP1_IAA_30M_REP1.umi_dedup.sorted.bam.bai", + "hisat2/RAP1_UNINDUCED_REP1.umi_dedup.sorted.bam", + "hisat2/RAP1_UNINDUCED_REP1.umi_dedup.sorted.bam.bai", + "hisat2/RAP1_UNINDUCED_REP2.umi_dedup.sorted.bam", + "hisat2/RAP1_UNINDUCED_REP2.umi_dedup.sorted.bam.bai", + "hisat2/WT_REP1.umi_dedup.sorted.bam", + "hisat2/WT_REP1.umi_dedup.sorted.bam.bai", + "hisat2/WT_REP2.umi_dedup.sorted.bam", + "hisat2/WT_REP2.umi_dedup.sorted.bam.bai", "hisat2/bigwig", "hisat2/bigwig/RAP1_IAA_30M_REP1.forward.bigWig", "hisat2/bigwig/RAP1_IAA_30M_REP1.reverse.bigWig", @@ -1672,12 +1759,6 @@ "hisat2/log/RAP1_UNINDUCED_REP2.hisat2.summary.log", "hisat2/log/WT_REP1.hisat2.summary.log", "hisat2/log/WT_REP2.hisat2.summary.log", - "hisat2/picard_metrics", - "hisat2/picard_metrics/RAP1_IAA_30M_REP1.markdup.sorted.MarkDuplicates.metrics.txt", - "hisat2/picard_metrics/RAP1_UNINDUCED_REP1.markdup.sorted.MarkDuplicates.metrics.txt", - "hisat2/picard_metrics/RAP1_UNINDUCED_REP2.markdup.sorted.MarkDuplicates.metrics.txt", - "hisat2/picard_metrics/WT_REP1.markdup.sorted.MarkDuplicates.metrics.txt", - "hisat2/picard_metrics/WT_REP2.markdup.sorted.MarkDuplicates.metrics.txt", "hisat2/qualimap", "hisat2/qualimap/RAP1_IAA_30M_REP1", "hisat2/qualimap/RAP1_IAA_30M_REP1/css", @@ -1986,36 +2067,36 @@ "hisat2/rseqc/read_duplication/xls/WT_REP2.pos.DupRate.xls", "hisat2/rseqc/read_duplication/xls/WT_REP2.seq.DupRate.xls", "hisat2/samtools_stats", - "hisat2/samtools_stats/RAP1_IAA_30M_REP1.markdup.sorted.bam.flagstat", - "hisat2/samtools_stats/RAP1_IAA_30M_REP1.markdup.sorted.bam.idxstats", - "hisat2/samtools_stats/RAP1_IAA_30M_REP1.markdup.sorted.bam.stats", "hisat2/samtools_stats/RAP1_IAA_30M_REP1.sorted.bam.flagstat", "hisat2/samtools_stats/RAP1_IAA_30M_REP1.sorted.bam.idxstats", "hisat2/samtools_stats/RAP1_IAA_30M_REP1.sorted.bam.stats", - "hisat2/samtools_stats/RAP1_UNINDUCED_REP1.markdup.sorted.bam.flagstat", - "hisat2/samtools_stats/RAP1_UNINDUCED_REP1.markdup.sorted.bam.idxstats", - "hisat2/samtools_stats/RAP1_UNINDUCED_REP1.markdup.sorted.bam.stats", + "hisat2/samtools_stats/RAP1_IAA_30M_REP1.umi_dedup.sorted.bam.flagstat", + "hisat2/samtools_stats/RAP1_IAA_30M_REP1.umi_dedup.sorted.bam.idxstats", + "hisat2/samtools_stats/RAP1_IAA_30M_REP1.umi_dedup.sorted.bam.stats", "hisat2/samtools_stats/RAP1_UNINDUCED_REP1.sorted.bam.flagstat", "hisat2/samtools_stats/RAP1_UNINDUCED_REP1.sorted.bam.idxstats", "hisat2/samtools_stats/RAP1_UNINDUCED_REP1.sorted.bam.stats", - "hisat2/samtools_stats/RAP1_UNINDUCED_REP2.markdup.sorted.bam.flagstat", - "hisat2/samtools_stats/RAP1_UNINDUCED_REP2.markdup.sorted.bam.idxstats", - "hisat2/samtools_stats/RAP1_UNINDUCED_REP2.markdup.sorted.bam.stats", + "hisat2/samtools_stats/RAP1_UNINDUCED_REP1.umi_dedup.sorted.bam.flagstat", + "hisat2/samtools_stats/RAP1_UNINDUCED_REP1.umi_dedup.sorted.bam.idxstats", + "hisat2/samtools_stats/RAP1_UNINDUCED_REP1.umi_dedup.sorted.bam.stats", "hisat2/samtools_stats/RAP1_UNINDUCED_REP2.sorted.bam.flagstat", "hisat2/samtools_stats/RAP1_UNINDUCED_REP2.sorted.bam.idxstats", "hisat2/samtools_stats/RAP1_UNINDUCED_REP2.sorted.bam.stats", - "hisat2/samtools_stats/WT_REP1.markdup.sorted.bam.flagstat", - "hisat2/samtools_stats/WT_REP1.markdup.sorted.bam.idxstats", - "hisat2/samtools_stats/WT_REP1.markdup.sorted.bam.stats", + "hisat2/samtools_stats/RAP1_UNINDUCED_REP2.umi_dedup.sorted.bam.flagstat", + "hisat2/samtools_stats/RAP1_UNINDUCED_REP2.umi_dedup.sorted.bam.idxstats", + "hisat2/samtools_stats/RAP1_UNINDUCED_REP2.umi_dedup.sorted.bam.stats", "hisat2/samtools_stats/WT_REP1.sorted.bam.flagstat", "hisat2/samtools_stats/WT_REP1.sorted.bam.idxstats", "hisat2/samtools_stats/WT_REP1.sorted.bam.stats", - "hisat2/samtools_stats/WT_REP2.markdup.sorted.bam.flagstat", - "hisat2/samtools_stats/WT_REP2.markdup.sorted.bam.idxstats", - "hisat2/samtools_stats/WT_REP2.markdup.sorted.bam.stats", + "hisat2/samtools_stats/WT_REP1.umi_dedup.sorted.bam.flagstat", + "hisat2/samtools_stats/WT_REP1.umi_dedup.sorted.bam.idxstats", + "hisat2/samtools_stats/WT_REP1.umi_dedup.sorted.bam.stats", "hisat2/samtools_stats/WT_REP2.sorted.bam.flagstat", "hisat2/samtools_stats/WT_REP2.sorted.bam.idxstats", "hisat2/samtools_stats/WT_REP2.sorted.bam.stats", + "hisat2/samtools_stats/WT_REP2.umi_dedup.sorted.bam.flagstat", + "hisat2/samtools_stats/WT_REP2.umi_dedup.sorted.bam.idxstats", + "hisat2/samtools_stats/WT_REP2.umi_dedup.sorted.bam.stats", "hisat2/stringtie", "hisat2/stringtie/RAP1_IAA_30M_REP1.ballgown", "hisat2/stringtie/RAP1_IAA_30M_REP1.ballgown/e2t.ctab", @@ -2062,6 +2143,13 @@ "hisat2/stringtie/WT_REP2.coverage.gtf", "hisat2/stringtie/WT_REP2.gene.abundance.txt", "hisat2/stringtie/WT_REP2.transcripts.gtf", + "hisat2/umitools", + "hisat2/umitools/genomic_dedup_log", + "hisat2/umitools/genomic_dedup_log/RAP1_IAA_30M_REP1.umi_dedup.sorted_UMICollapse.log", + "hisat2/umitools/genomic_dedup_log/RAP1_UNINDUCED_REP1.umi_dedup.sorted_UMICollapse.log", + "hisat2/umitools/genomic_dedup_log/RAP1_UNINDUCED_REP2.umi_dedup.sorted_UMICollapse.log", + "hisat2/umitools/genomic_dedup_log/WT_REP1.umi_dedup.sorted_UMICollapse.log", + "hisat2/umitools/genomic_dedup_log/WT_REP2.umi_dedup.sorted_UMICollapse.log", "multiqc", "multiqc/hisat2", "multiqc/hisat2/multiqc_report.html", @@ -2106,7 +2194,6 @@ "multiqc/hisat2/multiqc_report_data/multiqc_featurecounts_biotype_plot.txt", "multiqc/hisat2/multiqc_report_data/multiqc_general_stats.txt", "multiqc/hisat2/multiqc_report_data/multiqc_hisat2.txt", - "multiqc/hisat2/multiqc_report_data/multiqc_picard_dups.txt", "multiqc/hisat2/multiqc_report_data/multiqc_rseqc_bam_stat.txt", "multiqc/hisat2/multiqc_report_data/multiqc_rseqc_infer_experiment.txt", "multiqc/hisat2/multiqc_report_data/multiqc_rseqc_junction_annotation.txt", @@ -2119,10 +2206,6 @@ "multiqc/hisat2/multiqc_report_data/multiqc_samtools_stats.txt", "multiqc/hisat2/multiqc_report_data/multiqc_software_versions.txt", "multiqc/hisat2/multiqc_report_data/multiqc_sources.txt", - "multiqc/hisat2/multiqc_report_data/picard_deduplication.txt", - "multiqc/hisat2/multiqc_report_data/picard_histogram.txt", - "multiqc/hisat2/multiqc_report_data/picard_histogram_1.txt", - "multiqc/hisat2/multiqc_report_data/picard_histogram_2.txt", "multiqc/hisat2/multiqc_report_data/qualimap_gene_coverage_profile_Counts.txt", "multiqc/hisat2/multiqc_report_data/qualimap_gene_coverage_profile_Normalised.txt", "multiqc/hisat2/multiqc_report_data/qualimap_genomic_origin.txt", @@ -2189,8 +2272,6 @@ "multiqc/hisat2/multiqc_report_plots/pdf/hisat2_pe_plot-pct.pdf", "multiqc/hisat2/multiqc_report_plots/pdf/hisat2_se_plot-cnt.pdf", "multiqc/hisat2/multiqc_report_plots/pdf/hisat2_se_plot-pct.pdf", - "multiqc/hisat2/multiqc_report_plots/pdf/picard_deduplication-cnt.pdf", - "multiqc/hisat2/multiqc_report_plots/pdf/picard_deduplication-pct.pdf", "multiqc/hisat2/multiqc_report_plots/pdf/qualimap_gene_coverage_profile_Counts.pdf", "multiqc/hisat2/multiqc_report_plots/pdf/qualimap_gene_coverage_profile_Normalised.pdf", "multiqc/hisat2/multiqc_report_plots/pdf/qualimap_genomic_origin-cnt.pdf", @@ -2260,8 +2341,6 @@ "multiqc/hisat2/multiqc_report_plots/png/hisat2_pe_plot-pct.png", "multiqc/hisat2/multiqc_report_plots/png/hisat2_se_plot-cnt.png", "multiqc/hisat2/multiqc_report_plots/png/hisat2_se_plot-pct.png", - "multiqc/hisat2/multiqc_report_plots/png/picard_deduplication-cnt.png", - "multiqc/hisat2/multiqc_report_plots/png/picard_deduplication-pct.png", "multiqc/hisat2/multiqc_report_plots/png/qualimap_gene_coverage_profile_Counts.png", "multiqc/hisat2/multiqc_report_plots/png/qualimap_gene_coverage_profile_Normalised.png", "multiqc/hisat2/multiqc_report_plots/png/qualimap_genomic_origin-cnt.png", @@ -2331,8 +2410,6 @@ "multiqc/hisat2/multiqc_report_plots/svg/hisat2_pe_plot-pct.svg", "multiqc/hisat2/multiqc_report_plots/svg/hisat2_se_plot-cnt.svg", "multiqc/hisat2/multiqc_report_plots/svg/hisat2_se_plot-pct.svg", - "multiqc/hisat2/multiqc_report_plots/svg/picard_deduplication-cnt.svg", - "multiqc/hisat2/multiqc_report_plots/svg/picard_deduplication-pct.svg", "multiqc/hisat2/multiqc_report_plots/svg/qualimap_gene_coverage_profile_Counts.svg", "multiqc/hisat2/multiqc_report_plots/svg/qualimap_gene_coverage_profile_Normalised.svg", "multiqc/hisat2/multiqc_report_plots/svg/qualimap_genomic_origin-cnt.svg", @@ -2481,79 +2558,85 @@ "trimgalore/WT_REP1_trimmed_1.fastq.gz_trimming_report.txt", "trimgalore/WT_REP1_trimmed_2.fastq.gz_trimming_report.txt", "trimgalore/WT_REP2_trimmed_1.fastq.gz_trimming_report.txt", - "trimgalore/WT_REP2_trimmed_2.fastq.gz_trimming_report.txt" + "trimgalore/WT_REP2_trimmed_2.fastq.gz_trimming_report.txt", + "umitools", + "umitools/RAP1_IAA_30M_REP1.umi_extract.log", + "umitools/RAP1_UNINDUCED_REP1.umi_extract.log", + "umitools/RAP1_UNINDUCED_REP2.umi_extract.log", + "umitools/WT_REP1.umi_extract.log", + "umitools/WT_REP2.umi_extract.log" ], [ "genome_gfp.fasta:md5,e23e302af63736a199985a169fdac055", "genome_gfp.gtf:md5,c98b12c302f15731bfc36bcf297cfe28", - "RAP1_IAA_30M_REP1_dupMatrix.txt:md5,8a6bf324a928534cb7ed7a5522aff7d0", - "RAP1_UNINDUCED_REP1_dupMatrix.txt:md5,ae3e70edc98ec8117608f8f608effab9", - "RAP1_UNINDUCED_REP2_dupMatrix.txt:md5,9a5c2672c5817e930c7b884ada8fd92c", - "WT_REP1_dupMatrix.txt:md5,11371da7a087879340c2e7e6842a5d89", - "WT_REP2_dupMatrix.txt:md5,5176c7447c4295f94e2683dd9995cea0", - "RAP1_IAA_30M_REP1_intercept_slope.txt:md5,1285c70833b46849b726412858736ed7", - "RAP1_UNINDUCED_REP1_intercept_slope.txt:md5,a2efe7c3cad6f910d5dc208c2825a245", - "RAP1_UNINDUCED_REP2_intercept_slope.txt:md5,117712525fcbd396f77710f4f4b605d9", - "WT_REP1_intercept_slope.txt:md5,df33cbc6c3cb1c85c0c06cdba7df3873", - "WT_REP2_intercept_slope.txt:md5,38fbb93a419e666bc81020602852b1e2", - "RAP1_IAA_30M_REP1.biotype_counts_mqc.tsv:md5,cd7494b3bb12295a287f36506638f3c6", + "RAP1_IAA_30M_REP1_dupMatrix.txt:md5,a32f996ee7119c10cdfc3c706fed8560", + "RAP1_UNINDUCED_REP1_dupMatrix.txt:md5,0f0858724747050c17a9009fe7f7827d", + "RAP1_UNINDUCED_REP2_dupMatrix.txt:md5,6726b1c5e63db1f6efd2882de701adc1", + "WT_REP1_dupMatrix.txt:md5,1d57d6942d0720bddd25ff260dce08fa", + "WT_REP2_dupMatrix.txt:md5,b944b0f71ef8a98b5a4d53f4542c8e6f", + "RAP1_IAA_30M_REP1_intercept_slope.txt:md5,a07fbfcbd487003cdd2123cb89209d14", + "RAP1_UNINDUCED_REP1_intercept_slope.txt:md5,38cea811141edcfd28d1e7770628f6c9", + "RAP1_UNINDUCED_REP2_intercept_slope.txt:md5,b897caec80a37ace72c156ed091acbcd", + "WT_REP1_intercept_slope.txt:md5,9895ef03e6f9282b11c4a32fe16a25b0", + "WT_REP2_intercept_slope.txt:md5,6c22669508aecfdfe5583c38496db314", + "RAP1_IAA_30M_REP1.biotype_counts_mqc.tsv:md5,8433a395e65315feb0f8bfca4a1d1aba", "RAP1_IAA_30M_REP1.biotype_counts_rrna_mqc.tsv:md5,dde2de0cb90e10d0195c726f768e9941", - "RAP1_IAA_30M_REP1.featureCounts.txt:md5,a60088d7013eebeb240e02457431eedb", - "RAP1_UNINDUCED_REP1.biotype_counts_mqc.tsv:md5,4d1820a35481f454f17a14326356253d", + "RAP1_IAA_30M_REP1.featureCounts.txt:md5,1ecf699b693201b29cadd187bb0f2971", + "RAP1_UNINDUCED_REP1.biotype_counts_mqc.tsv:md5,036ab790c1e5fb2625c543ddccbf492d", "RAP1_UNINDUCED_REP1.biotype_counts_rrna_mqc.tsv:md5,845ff9059c72bc6722a8de69776e22bb", - "RAP1_UNINDUCED_REP1.featureCounts.txt:md5,3e6a34d7435d5249229c849532a45930", - "RAP1_UNINDUCED_REP2.biotype_counts_mqc.tsv:md5,3632da2126cb33391428ba0d6d10787f", + "RAP1_UNINDUCED_REP1.featureCounts.txt:md5,5776b0c29cfbf0551a1e9020c1d799c0", + "RAP1_UNINDUCED_REP2.biotype_counts_mqc.tsv:md5,5784c14524d6d06a927a745f1398d90e", "RAP1_UNINDUCED_REP2.biotype_counts_rrna_mqc.tsv:md5,6d3fa4c88c7fe61f638e4624ad5e22f0", - "RAP1_UNINDUCED_REP2.featureCounts.txt:md5,72513319be04c591dcbbaf14f2081b32", - "WT_REP1.biotype_counts_mqc.tsv:md5,4b2f1b065666fbe235aa72ba06142025", + "RAP1_UNINDUCED_REP2.featureCounts.txt:md5,9a53ceb0d1357e8015dedbd77cf620cc", + "WT_REP1.biotype_counts_mqc.tsv:md5,e9b102b8576a141a6cc0ce4876b1b5ea", "WT_REP1.biotype_counts_rrna_mqc.tsv:md5,8ef76d717492ca23764938aee8ea33a9", - "WT_REP1.featureCounts.txt:md5,8faf3b2101c5e07bbf02415663855690", - "WT_REP2.biotype_counts_mqc.tsv:md5,994f8e87aeaa769479fcc4747ffbe6d9", + "WT_REP1.featureCounts.txt:md5,fcda1cfb8b3664e95962c65cbbd45b9e", + "WT_REP2.biotype_counts_mqc.tsv:md5,6760be59877c02cb7ca5128e5be05f8e", "WT_REP2.biotype_counts_rrna_mqc.tsv:md5,12294618fe44df1e7f39348372dcb481", - "WT_REP2.featureCounts.txt:md5,efa5c0e711a3e0bdd42035be239e04a2", - "coverage_profile_along_genes_(high).txt:md5,b56043c3546cac003461c57abad93536", - "coverage_profile_along_genes_(low).txt:md5,1b55d86defcc541643137497c4c6bb06", - "coverage_profile_along_genes_(total).txt:md5,1b55d86defcc541643137497c4c6bb06", - "coverage_profile_along_genes_(high).txt:md5,9ffadadb953cc9ea1b1c01ac92fcdf61", - "coverage_profile_along_genes_(low).txt:md5,fb281c857a2c4e093b0d8e1f756abf81", - "coverage_profile_along_genes_(total).txt:md5,fb281c857a2c4e093b0d8e1f756abf81", - "coverage_profile_along_genes_(high).txt:md5,a31cb4ba804080544072cd57fc1bdf1c", - "coverage_profile_along_genes_(low).txt:md5,f744e295c3d034b1a86e1dde8bb18eff", - "coverage_profile_along_genes_(total).txt:md5,f744e295c3d034b1a86e1dde8bb18eff", - "coverage_profile_along_genes_(high).txt:md5,be72a2c9023f6eb81bd5816af33abb65", - "coverage_profile_along_genes_(low).txt:md5,9fce120533ff57f8bc2a2f44f1b6decb", - "coverage_profile_along_genes_(total).txt:md5,9fce120533ff57f8bc2a2f44f1b6decb", - "coverage_profile_along_genes_(high).txt:md5,83d436e0ddb7435bb343db5e4dfb744a", - "coverage_profile_along_genes_(low).txt:md5,58021e91f3595a0d6dc74abc41690638", - "coverage_profile_along_genes_(total).txt:md5,58021e91f3595a0d6dc74abc41690638", - "RAP1_IAA_30M_REP1.infer_experiment.txt:md5,1d4348685fa61819ff8076213d0873b3", - "RAP1_UNINDUCED_REP1.infer_experiment.txt:md5,3ee0f64e82f0b1ac16b5f3e2b30b024c", - "RAP1_UNINDUCED_REP2.infer_experiment.txt:md5,3d17628d178e5a3cc9dc03f32614bff7", - "WT_REP1.infer_experiment.txt:md5,13e3c748f5b968455a39eb9fefd87053", - "WT_REP2.infer_experiment.txt:md5,230910b354a5531d1c4bf711ccd6e493", + "WT_REP2.featureCounts.txt:md5,a2b116e5a1ddbf47659eba4c02ca9c84", + "coverage_profile_along_genes_(high).txt:md5,b10337c576a87e918a30c0e1f497752d", + "coverage_profile_along_genes_(low).txt:md5,0d7a6b63a10241843b5380f1ab16a160", + "coverage_profile_along_genes_(total).txt:md5,0d7a6b63a10241843b5380f1ab16a160", + "coverage_profile_along_genes_(high).txt:md5,1ca7ae6a9528442bcf32baabd72d369f", + "coverage_profile_along_genes_(low).txt:md5,cfabb16286c8ca57dedd66097d21e0b1", + "coverage_profile_along_genes_(total).txt:md5,cfabb16286c8ca57dedd66097d21e0b1", + "coverage_profile_along_genes_(high).txt:md5,609bfc84014c5b175a1b577a7491ba2b", + "coverage_profile_along_genes_(low).txt:md5,a0230508803a0f61ff3bd807a5596934", + "coverage_profile_along_genes_(total).txt:md5,a0230508803a0f61ff3bd807a5596934", + "coverage_profile_along_genes_(high).txt:md5,28950a8298e08c0609cae6873f631dab", + "coverage_profile_along_genes_(low).txt:md5,ea1a6cccb02d528d52025acbc4006d8a", + "coverage_profile_along_genes_(total).txt:md5,ea1a6cccb02d528d52025acbc4006d8a", + "coverage_profile_along_genes_(high).txt:md5,90b40674d78d1ea532d131f8080672c2", + "coverage_profile_along_genes_(low).txt:md5,74625695f7e325b51d076fcaa96e0368", + "coverage_profile_along_genes_(total).txt:md5,74625695f7e325b51d076fcaa96e0368", + "RAP1_IAA_30M_REP1.infer_experiment.txt:md5,b0cf79d3b9d1d3742ca4698cada70ea6", + "RAP1_UNINDUCED_REP1.infer_experiment.txt:md5,69f95beb80658b40ef4964b0cec41cb6", + "RAP1_UNINDUCED_REP2.infer_experiment.txt:md5,d48a674bd9e64adadd66b89b5c096056", + "WT_REP1.infer_experiment.txt:md5,8559d69b9f350897869ca61097643449", + "WT_REP2.infer_experiment.txt:md5,2ac93ef54e0aaa62482ceb45c91c64bb", "e2t.ctab:md5,54dd6de2daa90e973f47524a738a3d69", - "e_data.ctab:md5,c93ce5b90352398208e3d88c68df45c6", + "e_data.ctab:md5,b3f8769326278237c14c0820a3c627c3", "i2t.ctab:md5,dda3d3ccd7d4184d947c654ae73efb7b", - "i_data.ctab:md5,fe9d92d1d28e1bc1b223e2ce22b4ef05", + "i_data.ctab:md5,a8047633f3ba499a0c68af993429b1af", "e2t.ctab:md5,54dd6de2daa90e973f47524a738a3d69", - "e_data.ctab:md5,293d64f70bd8353b822e4fa8a6f082a3", + "e_data.ctab:md5,dd7844bd42536ccba73b5b41b11a4c89", "i2t.ctab:md5,dda3d3ccd7d4184d947c654ae73efb7b", - "i_data.ctab:md5,d279003d92f7feef9adb31203f84474a", + "i_data.ctab:md5,a715eca5dcbed38cbb21e6aeae27d4c6", "e2t.ctab:md5,54dd6de2daa90e973f47524a738a3d69", - "e_data.ctab:md5,ed48a29b77c315cb61a6bf09e68c877d", + "e_data.ctab:md5,97d59d95d8298cde4082ff4bfcf06fd6", "i2t.ctab:md5,dda3d3ccd7d4184d947c654ae73efb7b", - "i_data.ctab:md5,cf2506824949c02e0bfde17a9ed86e61", + "i_data.ctab:md5,349abadc17965a12f1fd2a24b111203e", "e2t.ctab:md5,54dd6de2daa90e973f47524a738a3d69", - "e_data.ctab:md5,a17c7749a005a89875876eb2d90721d4", + "e_data.ctab:md5,cd81e0cbece094019ca814d34f4f3033", "i2t.ctab:md5,dda3d3ccd7d4184d947c654ae73efb7b", - "i_data.ctab:md5,2e2d760739539fba09cbb666ab95afa1", + "i_data.ctab:md5,7f21bf2c570993f5a444c2e67c7d2983", "e2t.ctab:md5,54dd6de2daa90e973f47524a738a3d69", - "e_data.ctab:md5,22bdc84c1c4acce9798868e8c7b56004", + "e_data.ctab:md5,593daf0b84f008c0c64319990ef2fd42", "i2t.ctab:md5,dda3d3ccd7d4184d947c654ae73efb7b", - "i_data.ctab:md5,80fe14762a8f7230d516bba37b7275d1", - "cutadapt_filtered_reads_plot.txt:md5,6fa381627f7c1f664f3d4b2cb79cce90", - "cutadapt_trimmed_sequences_plot_3_Counts.txt:md5,13dfa866fd91dbb072689efe9aa83b1f", - "cutadapt_trimmed_sequences_plot_3_Obs_Exp.txt:md5,07145dd8dd3db654859b18eb0389046c", + "i_data.ctab:md5,01c675638f918a70ea689f618a2610bc", + "cutadapt_filtered_reads_plot.txt:md5,3f122969fa288888e5abef061b7963f2", + "cutadapt_trimmed_sequences_plot_3_Counts.txt:md5,5e8a821c9a4deb46c11bc65969b8864f", + "cutadapt_trimmed_sequences_plot_3_Obs_Exp.txt:md5,bf8abefa7c5f2f1e1140749983279d9d", "fastqc_raw-status-check-heatmap.txt:md5,5a89b0d8d162f6b1dbdaf39457bbc03b", "fastqc_raw_adapter_content_plot.txt:md5,da0389be84cfdd189b1d045212eb2974", "fastqc_raw_overrepresented_sequences_plot.txt:md5,25d88ea8a72f55e8a374ae802bc7f0b1", @@ -2564,62 +2647,59 @@ "fastqc_raw_per_sequence_quality_scores_plot.txt:md5,b5f9a02933e3065952237afd2ec9ce82", "fastqc_raw_sequence_counts_plot.txt:md5,cbae4979d5db66d3b894abcf8d1c453c", "fastqc_raw_sequence_duplication_levels_plot.txt:md5,8812cee16f6ca65e2c33635754de1772", - "fastqc_sequence_length_distribution_plot.txt:md5,6fe2c985606abad947bcca99b015ae33", - "fastqc_trimmed-status-check-heatmap.txt:md5,22a03548736b88b23be6bc0c9ef1b4a6", - "fastqc_trimmed_overrepresented_sequences_plot.txt:md5,c755e9d044ea1a82b2c8edde867b4878", - "fastqc_trimmed_per_base_n_content_plot.txt:md5,418610c1ce119cb786ad434db75d366e", - "fastqc_trimmed_per_base_sequence_quality_plot.txt:md5,bd22e06e41c096ad4f745d40fe96a1e5", - "fastqc_trimmed_per_sequence_gc_content_plot_Counts.txt:md5,004c60768ceb6197765154e3eaa37b7a", - "fastqc_trimmed_per_sequence_gc_content_plot_Percentages.txt:md5,95d29060b687f745288ad1ec47750037", - "fastqc_trimmed_per_sequence_quality_scores_plot.txt:md5,0f9834cc19f76dd5c87cf8cba7435a7c", - "fastqc_trimmed_sequence_counts_plot.txt:md5,9fd642bdd1da354f296bb8092205608f", - "fastqc_trimmed_sequence_duplication_levels_plot.txt:md5,0758257b497283b1ef28171e694db6db", + "fastqc_sequence_length_distribution_plot.txt:md5,7a2263743d5c452dc940eabffcb0ea82", + "fastqc_trimmed-status-check-heatmap.txt:md5,1913b4c077a341437475c758fb119559", + "fastqc_trimmed_overrepresented_sequences_plot.txt:md5,6a7c50868a36ca6052e9f4adba6b8567", + "fastqc_trimmed_per_base_n_content_plot.txt:md5,0cdd698fb9a2a4987572b9b4e8c8990e", + "fastqc_trimmed_per_base_sequence_quality_plot.txt:md5,82a9073151a413efbb0f11be8d0db1f2", + "fastqc_trimmed_per_sequence_gc_content_plot_Counts.txt:md5,08b43a02f34a3afa7eaddef602cdcafd", + "fastqc_trimmed_per_sequence_gc_content_plot_Percentages.txt:md5,0768af6bf06bbad52a3ac6ec0a444138", + "fastqc_trimmed_per_sequence_quality_scores_plot.txt:md5,ac167d250e9f88899da0ea81ef2b002e", + "fastqc_trimmed_sequence_counts_plot.txt:md5,370ddc5e58838dc5b55e53b6f8b8902a", + "fastqc_trimmed_sequence_duplication_levels_plot.txt:md5,b106d8ee7b83efe237afb862ebbe2f47", "multiqc_citations.txt:md5,da5043f8ab9b5bd51d91ccf3111c655e", - "multiqc_cutadapt.txt:md5,583b7b9ba76b26162bb9610ed746454b", + "multiqc_cutadapt.txt:md5,0937983d3ddfeccd4ccc5b0d74477558", "multiqc_fastqc_fastqc_raw.txt:md5,81c3c1a2575a1891a7f2a9637a0f2cc0", - "multiqc_fastqc_fastqc_trimmed.txt:md5,54743154d0e8858980acffeb5b6f6a97", - "multiqc_featurecounts_biotype_plot.txt:md5,d784fc4b2dce860c8f88cc3e936e589e", - "multiqc_samtools_idxstats.txt:md5,66a8e8aecb6233f5a3521151b1ce8d49", - "picard_histogram.txt:md5,d41d8cd98f00b204e9800998ecf8427e", - "picard_histogram_1.txt:md5,d41d8cd98f00b204e9800998ecf8427e", - "picard_histogram_2.txt:md5,d41d8cd98f00b204e9800998ecf8427e", - "qualimap_gene_coverage_profile_Counts.txt:md5,f3b9bb1902af4c7b5e04c0830ded2c02", - "qualimap_gene_coverage_profile_Normalised.txt:md5,ebcedec8e5c959414a4e89ccae3fc07e", - "qualimap_rnaseq_cov_hist.txt:md5,b78ff616f267f9b061b3297b767e88fb", - "rseqc_infer_experiment_plot.txt:md5,0556a013129be7d78fac4345fddbe8c5", + "multiqc_fastqc_fastqc_trimmed.txt:md5,a8a003b61b03295c416f3eb66c1cca65", + "multiqc_featurecounts_biotype_plot.txt:md5,078841c35fb163ad1e7c99393015ecac", + "multiqc_samtools_idxstats.txt:md5,35fe3b73df5ab19b53c117d3269e7e72", + "qualimap_gene_coverage_profile_Counts.txt:md5,54613117acef91c2271327e31203242c", + "qualimap_gene_coverage_profile_Normalised.txt:md5,f2884314cb4296877eb6ffd03a12a5b8", + "qualimap_rnaseq_cov_hist.txt:md5,c5abd5f3ddd835052c7bcac0f92ff0a4", + "rseqc_infer_experiment_plot.txt:md5,8e71c47cf89a0283de00ba1e4325a25f", "samtools-idxstats-mapped-reads-plot_Normalised_Counts.txt:md5,75acd04232d1804b5f960ee4c5db4722", - "samtools-idxstats-mapped-reads-plot_Observed_over_Expected_Counts.txt:md5,3625f666cc09d0f07990716aeccd869f", - "samtools-idxstats-mapped-reads-plot_Raw_Counts.txt:md5,6c323b383a6506d124506405b9463d93", - "ambig_info.tsv:md5,de973a4b22a4457217ae3dc04caf9401", + "samtools-idxstats-mapped-reads-plot_Observed_over_Expected_Counts.txt:md5,f9f9e9472fcd03f74e0186d29ee922dd", + "samtools-idxstats-mapped-reads-plot_Raw_Counts.txt:md5,e71efae3d46b66490b096b12b4ae27c5", + "ambig_info.tsv:md5,0b462f9b2d8eff1b4d76b7433434899c", "expected_bias.gz:md5,3407f87245d0003e0ffbfdf6d8c04f20", "observed_bias.gz:md5,92bcd0592d22a6a58d0360fc76103e56", "observed_bias_3p.gz:md5,92bcd0592d22a6a58d0360fc76103e56", - "cmd_info.json:md5,138b27764a97d468418b3dac5e7f1cef", - "lib_format_counts.json:md5,c24ffe28d70476b5ccdd8bc2d22c0ac1", - "ambig_info.tsv:md5,45f252b4f0e11e6730cf0c29f800fdbb", + "cmd_info.json:md5,910a60cb6ef954008c9e6d037342d773", + "lib_format_counts.json:md5,5846a0bb874171d30ef60059f6c10868", + "ambig_info.tsv:md5,2c45f73a5f73ae4d0053198ea98c87f3", "expected_bias.gz:md5,3407f87245d0003e0ffbfdf6d8c04f20", "observed_bias.gz:md5,92bcd0592d22a6a58d0360fc76103e56", "observed_bias_3p.gz:md5,92bcd0592d22a6a58d0360fc76103e56", - "cmd_info.json:md5,87bcf1e84fc31a794e3c8f8b227d11c3", - "lib_format_counts.json:md5,f6d44c0221f7fd559f11a9afe04c9935", - "ambig_info.tsv:md5,6dcc2891ea572e9b8d1ba52cd434ab84", + "cmd_info.json:md5,e364ca1b150c3c0ae57f0397ff8ae471", + "lib_format_counts.json:md5,d662a174758011a56e6669f4810e0aaf", + "ambig_info.tsv:md5,644c4352884872a05fe36cbb2b119879", "expected_bias.gz:md5,3407f87245d0003e0ffbfdf6d8c04f20", "observed_bias.gz:md5,92bcd0592d22a6a58d0360fc76103e56", "observed_bias_3p.gz:md5,92bcd0592d22a6a58d0360fc76103e56", - "cmd_info.json:md5,ec504469f9b0e33c94f85846f1486d7f", - "lib_format_counts.json:md5,7c562bf2f70e42f3a7292687dfd328c3", - "ambig_info.tsv:md5,194f574e0586416155e3f33d42e2b167", + "cmd_info.json:md5,b472447f8ac9530be1ca2d9733af2686", + "lib_format_counts.json:md5,f01d7b98afc4b6bab773536a88e08e7e", + "ambig_info.tsv:md5,d6ca0827f17be19b6e08730a45063f9d", "expected_bias.gz:md5,3407f87245d0003e0ffbfdf6d8c04f20", "observed_bias.gz:md5,92bcd0592d22a6a58d0360fc76103e56", "observed_bias_3p.gz:md5,92bcd0592d22a6a58d0360fc76103e56", - "cmd_info.json:md5,591436d401b6df6f3c6a8b16913158f4", - "lib_format_counts.json:md5,d46250bb3677d72feeefc435fe6395a6", - "ambig_info.tsv:md5,a26e3f936e65d7da66392603c2f91f6f", + "cmd_info.json:md5,bdc36620d04525d343faea5297f0f0d7", + "lib_format_counts.json:md5,cf8bf8db790bede5136fca52968e7c43", + "ambig_info.tsv:md5,4aa8036707dc557004a80852394beebe", "expected_bias.gz:md5,3407f87245d0003e0ffbfdf6d8c04f20", "observed_bias.gz:md5,92bcd0592d22a6a58d0360fc76103e56", "observed_bias_3p.gz:md5,92bcd0592d22a6a58d0360fc76103e56", - "cmd_info.json:md5,fe66c9d876645b9260d8c2488157d4d4", - "lib_format_counts.json:md5,088fd51db07022ffde47033bbd029400", + "cmd_info.json:md5,809380ddce725a8fab75dd7741b64bf6", + "lib_format_counts.json:md5,d231ba7624b67eb654989f69530e2925", "R_sessionInfo.log:md5,fb0da0d7ad6994ed66a8e68348b19676", "tx2gene.tsv:md5,0e2418a69d2eba45097ebffc2f700bfe" ] @@ -2628,123 +2708,6 @@ "nf-test": "0.9.0", "nextflow": "24.10.2" }, - "timestamp": "2024-12-10T22:05:37.781319314" - }, - "--umi_dedup_tool 'umitools - stub": { - "content": [ - 32, - { - "BBMAP_BBSPLIT": { - "bbmap": 39.1 - }, - "CAT_FASTQ": { - "cat": 9.5 - }, - "CUSTOM_CATADDITIONALFASTA": { - "python": null - }, - "CUSTOM_GETCHROMSIZES": { - "getchromsizes": 1.21 - }, - "FASTQC": { - "fastqc": "0.12.1" - }, - "FQ_LINT": { - "fq": "0.12.0 (2024-07-08)" - }, - "GTF2BED": { - "perl": "5.26.2" - }, - "GTF_FILTER": { - "python": "3.9.5" - }, - "GUNZIP_ADDITIONAL_FASTA": { - "gunzip": 1.1 - }, - "GUNZIP_GTF": { - "gunzip": 1.1 - }, - "STAR_GENOMEGENERATE": { - "star": "2.7.11b", - "samtools": 1.2, - "gawk": "5.1.0" - }, - "TRIMGALORE": { - "trimgalore": "0.6.10", - "cutadapt": 4.9 - }, - "UMITOOLS_EXTRACT": { - "umitools": "1.1.5" - }, - "UNTAR_SALMON_INDEX": { - "untar": 1.34 - }, - "Workflow": { - "nf-core/rnaseq": "v3.18.0dev" - } - }, - [ - "custom", - "custom/out", - "custom/out/genome_transcriptome.fasta", - "custom/out/genome_transcriptome.gtf", - "fastqc", - "fastqc/raw", - "fastqc/raw/RAP1_IAA_30M_REP1_raw.html", - "fastqc/raw/RAP1_IAA_30M_REP1_raw.zip", - "fastqc/raw/RAP1_UNINDUCED_REP1_raw.html", - "fastqc/raw/RAP1_UNINDUCED_REP1_raw.zip", - "fastqc/raw/RAP1_UNINDUCED_REP2_raw.html", - "fastqc/raw/RAP1_UNINDUCED_REP2_raw.zip", - "fastqc/raw/WT_REP1_raw.html", - "fastqc/raw/WT_REP1_raw.zip", - "fastqc/raw/WT_REP2_raw.html", - "fastqc/raw/WT_REP2_raw.zip", - "fastqc/trim", - "fq_lint", - "fq_lint/raw", - "fq_lint/raw/RAP1_IAA_30M_REP1.fq_lint.txt", - "fq_lint/raw/RAP1_UNINDUCED_REP1.fq_lint.txt", - "fq_lint/raw/RAP1_UNINDUCED_REP2.fq_lint.txt", - "fq_lint/raw/WT_REP1.fq_lint.txt", - "fq_lint/raw/WT_REP2.fq_lint.txt", - "multiqc", - "multiqc/star_salmon", - "multiqc/star_salmon/multiqc_data", - "multiqc/star_salmon/multiqc_plots", - "multiqc/star_salmon/multiqc_report.html", - "pipeline_info", - "pipeline_info/nf_core_rnaseq_software_mqc_versions.yml", - "trimgalore", - "trimgalore/RAP1_IAA_30M_REP1_trimmed_1.fastq.gz_trimming_report.txt", - "trimgalore/RAP1_IAA_30M_REP1_trimmed_2.fastq.gz_trimming_report.txt", - "trimgalore/RAP1_UNINDUCED_REP1_trimmed.fastq.gz_trimming_report.txt", - "trimgalore/RAP1_UNINDUCED_REP2_trimmed.fastq.gz_trimming_report.txt", - "trimgalore/WT_REP1_trimmed_1.fastq.gz_trimming_report.txt", - "trimgalore/WT_REP1_trimmed_2.fastq.gz_trimming_report.txt", - "trimgalore/WT_REP2_trimmed_1.fastq.gz_trimming_report.txt", - "trimgalore/WT_REP2_trimmed_2.fastq.gz_trimming_report.txt", - "umitools", - "umitools/RAP1_IAA_30M_REP1.umi_extract.log", - "umitools/RAP1_UNINDUCED_REP1.umi_extract.log", - "umitools/RAP1_UNINDUCED_REP2.umi_extract.log", - "umitools/WT_REP1.umi_extract.log", - "umitools/WT_REP2.umi_extract.log" - ], - [ - "genome_transcriptome.fasta:md5,d41d8cd98f00b204e9800998ecf8427e", - "genome_transcriptome.gtf:md5,d41d8cd98f00b204e9800998ecf8427e", - "RAP1_IAA_30M_REP1.umi_extract.log:md5,d41d8cd98f00b204e9800998ecf8427e", - "RAP1_UNINDUCED_REP1.umi_extract.log:md5,d41d8cd98f00b204e9800998ecf8427e", - "RAP1_UNINDUCED_REP2.umi_extract.log:md5,d41d8cd98f00b204e9800998ecf8427e", - "WT_REP1.umi_extract.log:md5,d41d8cd98f00b204e9800998ecf8427e", - "WT_REP2.umi_extract.log:md5,d41d8cd98f00b204e9800998ecf8427e" - ] - ], - "meta": { - "nf-test": "0.9.0", - "nextflow": "24.10.2" - }, - "timestamp": "2024-12-10T22:14:10.443068191" + "timestamp": "2024-12-11T17:17:19.399917398" } } \ No newline at end of file From 3478877c31402fea7b3d4fff084e5f703198defc Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Wed, 11 Dec 2024 17:49:33 +0000 Subject: [PATCH 063/107] update snapshot --- tests/umi.nf.test.snap | 112 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 112 insertions(+) diff --git a/tests/umi.nf.test.snap b/tests/umi.nf.test.snap index 243481316..a8a3a57d9 100644 --- a/tests/umi.nf.test.snap +++ b/tests/umi.nf.test.snap @@ -2709,5 +2709,117 @@ "nextflow": "24.10.2" }, "timestamp": "2024-12-11T17:17:19.399917398" + }, + "--umi_dedup_tool 'umitools - stub": { + "content": [ + 32, + { + "BBMAP_BBSPLIT": { + "bbmap": 39.1 + }, + "CAT_FASTQ": { + "cat": 9.5 + }, + "CUSTOM_CATADDITIONALFASTA": { + "python": null + }, + "CUSTOM_GETCHROMSIZES": { + "getchromsizes": 1.21 + }, + "FASTQC": { + "fastqc": "0.12.1" + }, + "FQ_LINT": { + "fq": "0.12.0 (2024-07-08)" + }, + "GTF2BED": { + "perl": "5.26.2" + }, + "GTF_FILTER": { + "python": "3.9.5" + }, + "GUNZIP_ADDITIONAL_FASTA": { + "gunzip": 1.1 + }, + "GUNZIP_GTF": { + "gunzip": 1.1 + }, + "STAR_GENOMEGENERATE": { + "star": "2.7.11b", + "samtools": 1.2, + "gawk": "5.1.0" + }, + "TRIMGALORE": { + "trimgalore": "0.6.10", + "cutadapt": 4.9 + }, + "UMITOOLS_EXTRACT": { + "umitools": "1.1.5" + }, + "UNTAR_SALMON_INDEX": { + "untar": 1.34 + }, + "Workflow": { + "nf-core/rnaseq": "v3.18.0dev" + } + }, + [ + "custom", + "custom/out", + "custom/out/genome_transcriptome.fasta", + "custom/out/genome_transcriptome.gtf", + "fastqc", + "fastqc/raw", + "fastqc/raw/RAP1_IAA_30M_REP1_raw.html", + "fastqc/raw/RAP1_IAA_30M_REP1_raw.zip", + "fastqc/raw/RAP1_UNINDUCED_REP1_raw.html", + "fastqc/raw/RAP1_UNINDUCED_REP1_raw.zip", + "fastqc/raw/RAP1_UNINDUCED_REP2_raw.html", + "fastqc/raw/RAP1_UNINDUCED_REP2_raw.zip", + "fastqc/raw/WT_REP1_raw.html", + "fastqc/raw/WT_REP1_raw.zip", + "fastqc/raw/WT_REP2_raw.html", + "fastqc/raw/WT_REP2_raw.zip", + "fastqc/trim", + "fq_lint", + "fq_lint/raw", + "fq_lint/raw/RAP1_IAA_30M_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP1.fq_lint.txt", + "fq_lint/raw/RAP1_UNINDUCED_REP2.fq_lint.txt", + "fq_lint/raw/WT_REP1.fq_lint.txt", + "fq_lint/raw/WT_REP2.fq_lint.txt", + "multiqc", + "multiqc/star_salmon", + "multiqc/star_salmon/multiqc_data", + "multiqc/star_salmon/multiqc_plots", + "multiqc/star_salmon/multiqc_report.html", + "pipeline_info", + "pipeline_info/nf_core_rnaseq_software_mqc_versions.yml", + "trimgalore", + "trimgalore/RAP1_IAA_30M_REP1_trimmed_1.fastq.gz_trimming_report.txt", + "trimgalore/RAP1_IAA_30M_REP1_trimmed_2.fastq.gz_trimming_report.txt", + "trimgalore/RAP1_UNINDUCED_REP1_trimmed.fastq.gz_trimming_report.txt", + "trimgalore/RAP1_UNINDUCED_REP2_trimmed.fastq.gz_trimming_report.txt", + "trimgalore/WT_REP1_trimmed_1.fastq.gz_trimming_report.txt", + "trimgalore/WT_REP1_trimmed_2.fastq.gz_trimming_report.txt", + "trimgalore/WT_REP2_trimmed_1.fastq.gz_trimming_report.txt", + "trimgalore/WT_REP2_trimmed_2.fastq.gz_trimming_report.txt", + "umitools", + "umitools/RAP1_IAA_30M_REP1.umi_extract.log", + "umitools/RAP1_UNINDUCED_REP1.umi_extract.log", + "umitools/RAP1_UNINDUCED_REP2.umi_extract.log", + "umitools/WT_REP1.umi_extract.log", + "umitools/WT_REP2.umi_extract.log" + ], + [ + "genome_transcriptome.fasta:md5,d41d8cd98f00b204e9800998ecf8427e", + "genome_transcriptome.gtf:md5,d41d8cd98f00b204e9800998ecf8427e" + ] + ], + "meta": { + "nf-test": "0.9.0", + "nextflow": "24.10.2" + }, + "timestamp": "2024-12-11T17:36:56.031072985" } } \ No newline at end of file From 43c55fe956af514c41b488f142fdf84c3a549715 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Wed, 11 Dec 2024 18:10:17 +0000 Subject: [PATCH 064/107] Exclude more variable logs --- tests/.nftignore | 2 ++ tests/umi.nf.test.snap | 21 ++++----------------- 2 files changed, 6 insertions(+), 17 deletions(-) diff --git a/tests/.nftignore b/tests/.nftignore index e96f0ec17..7f3fad699 100644 --- a/tests/.nftignore +++ b/tests/.nftignore @@ -83,6 +83,8 @@ umitools/*.umi_extract.log {salmon,star_rsem,star_salmon}/deseq2_qc/deseq2.sample.dists.txt {salmon,star_rsem,star_salmon}/deseq2_qc/size_factors/*.txt {salmon,star_rsem,star_salmon}/deseq2_qc/size_factors/deseq2.size_factors.RData +{salmon,star_rsem,star_salmon}/umitools/{genomic,transcriptomic}_dedup_log/* +{salmon,star_rsem,star_salmon}/umitools/prepare_for_salmon_log/* {salmon,star_salmon}/*/aux_info/fld.gz {salmon,star_salmon}/*/aux_info/meta_info.json {salmon,star_salmon}/*/libParams/flenDist.txt diff --git a/tests/umi.nf.test.snap b/tests/umi.nf.test.snap index a8a3a57d9..b12fa48d0 100644 --- a/tests/umi.nf.test.snap +++ b/tests/umi.nf.test.snap @@ -1467,27 +1467,14 @@ "WT_REP2.umi_dedup.sorted_per_umi_per_position.tsv:md5,6f5656947a7f0076df446e6f40430027", "WT_REP2.umi_dedup.transcriptome.sorted_edit_distance.tsv:md5,3e3c6a7e8996e566350742e9911366d3", "WT_REP2.umi_dedup.transcriptome.sorted_per_umi.tsv:md5,0c986c4cb7a77f650a19e2c454b9b179", - "WT_REP2.umi_dedup.transcriptome.sorted_per_umi_per_position.tsv:md5,af9028dbdab81de3854a32cd1d19ac8b", - "RAP1_IAA_30M_REP1.umi_dedup.sorted.log:md5,0001a4ebfeaedfa164311e075b4c6c8a", - "RAP1_UNINDUCED_REP1.umi_dedup.sorted.log:md5,150b19399146d766746e991cf2f20834", - "RAP1_UNINDUCED_REP2.umi_dedup.sorted.log:md5,218313d63828b1bd97bf0f379bad5e8f", - "WT_REP1.umi_dedup.sorted.log:md5,982c5089b199ca8647511f4b10e3a713", - "WT_REP2.umi_dedup.sorted.log:md5,3f2cc0f12cdc53db11a7f7409dde7d62", - "RAP1_IAA_30M_REP1.umi_dedup.transcriptome.filtered.prepare_for_rsem.log:md5,a7c8743ab54408ec184c669d42b53907", - "WT_REP1.umi_dedup.transcriptome.filtered.prepare_for_rsem.log:md5,5c334da8161e17825c08fc6a2522d187", - "WT_REP2.umi_dedup.transcriptome.filtered.prepare_for_rsem.log:md5,ddc19f73d0a622abc3bae9000a3daa4e", - "RAP1_IAA_30M_REP1.umi_dedup.transcriptome.sorted.log:md5,058d0796e29f1037460d936947cf2ba8", - "RAP1_UNINDUCED_REP1.umi_dedup.transcriptome.sorted.log:md5,5c93e408bbfd13061ad2f2b432747abe", - "RAP1_UNINDUCED_REP2.umi_dedup.transcriptome.sorted.log:md5,15bcb30ad07c9a71852ff38c40f9e79f", - "WT_REP1.umi_dedup.transcriptome.sorted.log:md5,8938cfbb06f7ec98635687b64151d098", - "WT_REP2.umi_dedup.transcriptome.sorted.log:md5,08cd52b814d3e2f4f4adc688643641c6" + "WT_REP2.umi_dedup.transcriptome.sorted_per_umi_per_position.tsv:md5,af9028dbdab81de3854a32cd1d19ac8b" ] ], "meta": { "nf-test": "0.9.0", "nextflow": "24.10.2" }, - "timestamp": "2024-12-11T17:23:58.835430691" + "timestamp": "2024-12-11T18:07:55.751564456" }, "Params: --aligner hisat2 --umi_dedup_tool 'umicollapse'": { "content": [ @@ -2708,7 +2695,7 @@ "nf-test": "0.9.0", "nextflow": "24.10.2" }, - "timestamp": "2024-12-11T17:17:19.399917398" + "timestamp": "2024-12-11T18:01:45.228731692" }, "--umi_dedup_tool 'umitools - stub": { "content": [ @@ -2820,6 +2807,6 @@ "nf-test": "0.9.0", "nextflow": "24.10.2" }, - "timestamp": "2024-12-11T17:36:56.031072985" + "timestamp": "2024-12-11T18:08:48.404716766" } } \ No newline at end of file From 52ba2d4be0667d99c544cd361cd9678716070c9c Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Wed, 11 Dec 2024 18:13:36 +0000 Subject: [PATCH 065/107] update CHANGELOG --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index c5c417dd3..f85fb7c6c 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -17,6 +17,7 @@ Special thanks to the following for their contributions to the release: - [PR #1369](https://github.com/nf-core/rnaseq/pull/1369) - Add umicollapse as an alternative to umi-tools - [PR #1461](https://github.com/nf-core/rnaseq/pull/1461) - Add FASTQ linting during preprocessing - [PR #1463](https://github.com/nf-core/rnaseq/pull/1463) - Move channel operations outside of the onComplete() block +- [PR #1467](https://github.com/nf-core/rnaseq/pull/1467) - Add test suite for UMI handling functionality ### Software dependencies From c01791630139c3e4948f1e18452ee3c4abec72fc Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Thu, 12 Dec 2024 09:46:22 +0000 Subject: [PATCH 066/107] Fix subworkflow alias --- workflows/rnaseq/main.nf | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/workflows/rnaseq/main.nf b/workflows/rnaseq/main.nf index 62764d343..131a86dd1 100755 --- a/workflows/rnaseq/main.nf +++ b/workflows/rnaseq/main.nf @@ -15,10 +15,10 @@ include { MULTIQC_CUSTOM_BIOTYPE } from '../../modules/local/multiqc // // SUBWORKFLOW: Consisting of a mix of local and nf-core/modules // -include { ALIGN_STAR } from '../../subworkflows/local/align_star' -include { QUANTIFY_RSEM } from '../../subworkflows/local/quantify_rsem' -include { BAM_DEDUP_UMI as BAM_DEDUP_UMI_STAR } from '../../subworkflows/local/bam_dedup_umi' -include { BAM_DEDUP_UMI as BAM_DEDUP_UMI_HISAT } from '../../subworkflows/local/bam_dedup_umi' +include { ALIGN_STAR } from '../../subworkflows/local/align_star' +include { QUANTIFY_RSEM } from '../../subworkflows/local/quantify_rsem' +include { BAM_DEDUP_UMI as BAM_DEDUP_UMI_STAR } from '../../subworkflows/local/bam_dedup_umi' +include { BAM_DEDUP_UMI as BAM_DEDUP_UMI_HISAT2 } from '../../subworkflows/local/bam_dedup_umi' include { checkSamplesAfterGrouping } from '../../subworkflows/local/utils_nfcore_rnaseq_pipeline' include { multiqcTsvFromList } from '../../subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness' From 42f3fa7147846a9f6bfc53b2cd6f32d12431090a Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Thu, 12 Dec 2024 09:49:57 +0000 Subject: [PATCH 067/107] Fix linting --- workflows/rnaseq/main.nf | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/workflows/rnaseq/main.nf b/workflows/rnaseq/main.nf index 131a86dd1..bece803c1 100755 --- a/workflows/rnaseq/main.nf +++ b/workflows/rnaseq/main.nf @@ -354,7 +354,7 @@ workflow RNASEQ { ch_multiqc_files = ch_multiqc_files .mix(BAM_DEDUP_UMI_HISAT2.out.multiqc_files) } else { - + // The deduplicated stats should take priority for MultiQC, but use // them straight out of the aligner otherwise ch_multiqc_files = ch_multiqc_files From d3cc50d77aa30379b257df16bd1ab25d60cf0d20 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Thu, 12 Dec 2024 10:26:40 +0000 Subject: [PATCH 068/107] Fix version mixing --- workflows/rnaseq/main.nf | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/workflows/rnaseq/main.nf b/workflows/rnaseq/main.nf index bece803c1..5d4085b81 100755 --- a/workflows/rnaseq/main.nf +++ b/workflows/rnaseq/main.nf @@ -181,6 +181,8 @@ workflow RNASEQ { ch_genome_bam_index = Channel.empty() ch_star_log = Channel.empty() ch_unaligned_sequences = Channel.empty() + ch_transcriptome_bam = Channel.empty() + if (!params.skip_alignment && params.aligner == 'star_salmon') { // Check if an AWS iGenome has been provided to use the appropriate version of STAR def is_aws_igenome = false @@ -230,7 +232,7 @@ workflow RNASEQ { ch_genome_bam = BAM_DEDUP_UMI_STAR.out.bam ch_transcriptome_bam = BAM_DEDUP_UMI_STAR.out.transcriptome_bam ch_genome_bam_index = BAM_DEDUP_UMI_STAR.out.bai - ch_versions = BAM_DEDUP_UMI_STAR.out.versions + ch_versions = ch_versions.mix(BAM_DEDUP_UMI_STAR.out.versions) ch_multiqc_files = ch_multiqc_files .mix(BAM_DEDUP_UMI_STAR.out.multiqc_files) @@ -343,13 +345,13 @@ workflow RNASEQ { params.umi_dedup_tool, params.umitools_dedup_stats, params.bam_csi_index, - [[],[]], - [[],[]] + ch_transcriptome_bam, + ch_transcript_fasta.map { [ [:], it ] } ) ch_genome_bam = BAM_DEDUP_UMI_HISAT2.out.bam ch_genome_bam_index = BAM_DEDUP_UMI_HISAT2.out.bai - ch_versions = BAM_DEDUP_UMI_HISAT2.out.versions + ch_versions = ch_versions.mix(BAM_DEDUP_UMI_HISAT2.out.versions) ch_multiqc_files = ch_multiqc_files .mix(BAM_DEDUP_UMI_HISAT2.out.multiqc_files) From 476902522a6d8cfa6590229a17c115110977949e Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Thu, 12 Dec 2024 10:26:56 +0000 Subject: [PATCH 069/107] fix selector --- workflows/rnaseq/nextflow.config | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/workflows/rnaseq/nextflow.config b/workflows/rnaseq/nextflow.config index 2eef40849..38fa58c50 100644 --- a/workflows/rnaseq/nextflow.config +++ b/workflows/rnaseq/nextflow.config @@ -130,7 +130,7 @@ if (!params.skip_alignment && params.aligner == 'star_salmon') { ] } - withName: 'NFCORE_RNASEQ:RNASEQ:BAM_DEDUP_UMI_(STAR|HISAT2):BAM_SORT_STATS_SAMTOOLS:SAMTOOLS_SORT' { + withName: 'NFCORE_RNASEQ:RNASEQ:BAM_DEDUP_UMI_STAR:BAM_SORT_STATS_SAMTOOLS:SAMTOOLS_SORT' { ext.prefix = { "${meta.id}.transcriptome.sorted" } publishDir = [ path: { params.save_align_intermeds || params.save_umi_intermeds ? "${params.outdir}/${params.aligner}" : params.outdir }, From 742be2fc8e3d8f556ee7298b2a12199671a7c031 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Thu, 12 Dec 2024 10:27:17 +0000 Subject: [PATCH 070/107] misc --- subworkflows/local/bam_dedup_umi/main.nf | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/subworkflows/local/bam_dedup_umi/main.nf b/subworkflows/local/bam_dedup_umi/main.nf index a7f76f9a3..9390a0c43 100644 --- a/subworkflows/local/bam_dedup_umi/main.nf +++ b/subworkflows/local/bam_dedup_umi/main.nf @@ -23,7 +23,7 @@ workflow BAM_DEDUP_UMI { main: ch_versions = Channel.empty() - if (umi_dedup_tool == "umicollapse" && umi_dedup_tool != "umitools"){ + if (umi_dedup_tool != "umicollapse" && umi_dedup_tool != "umitools"){ error("Unknown umi_dedup_tool '${umi_dedup_tool}'") } @@ -64,7 +64,7 @@ workflow BAM_DEDUP_UMI { ch_sorted_transcriptome_bam ) UMI_DEDUP_TRANSCRIPTOME = BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_TRANSCRIPTOME - ch_dedup_log = dedup_log.mix(UMI_DEDUP_GENOME.out.dedup_stats) + ch_dedup_log = ch_dedup_log.mix(UMI_DEDUP_GENOME.out.dedup_stats) } else if (umi_dedup_tool == "umitools") { BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_TRANSCRIPTOME ( From 9f06d707012e243e2fa86dc11ce6f7182cacd187 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Thu, 12 Dec 2024 10:52:28 +0000 Subject: [PATCH 071/107] Fix process name in snap --- tests/umi.nf.test.snap | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/umi.nf.test.snap b/tests/umi.nf.test.snap index b12fa48d0..fd8700918 100644 --- a/tests/umi.nf.test.snap +++ b/tests/umi.nf.test.snap @@ -133,7 +133,7 @@ "UMITOOLS_EXTRACT": { "umitools": "1.1.5" }, - "UMITOOLS_PREPAREFORSALMON": { + "UMITOOLS_PREPAREFORRSEM": { "umitools": "1.1.5" }, "UNTAR_SALMON_INDEX": { @@ -2809,4 +2809,4 @@ }, "timestamp": "2024-12-11T18:08:48.404716766" } -} \ No newline at end of file +} From dbd3343b5077c2b9dfd033921f5a475e8323e6c1 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Thu, 12 Dec 2024 10:52:47 +0000 Subject: [PATCH 072/107] Remove unneeded subworkflow include --- workflows/rnaseq/main.nf | 1 - 1 file changed, 1 deletion(-) diff --git a/workflows/rnaseq/main.nf b/workflows/rnaseq/main.nf index 5d4085b81..63c3678de 100755 --- a/workflows/rnaseq/main.nf +++ b/workflows/rnaseq/main.nf @@ -55,7 +55,6 @@ include { samplesheetToList } from 'plugin/nf-schema' include { paramsSummaryMultiqc } from '../../subworkflows/nf-core/utils_nfcore_pipeline' include { softwareVersionsToYAML } from '../../subworkflows/nf-core/utils_nfcore_pipeline' include { FASTQ_ALIGN_HISAT2 } from '../../subworkflows/nf-core/fastq_align_hisat2' -include { BAM_SORT_STATS_SAMTOOLS } from '../../subworkflows/nf-core/bam_sort_stats_samtools' include { BAM_MARKDUPLICATES_PICARD } from '../../subworkflows/nf-core/bam_markduplicates_picard' include { BAM_RSEQC } from '../../subworkflows/nf-core/bam_rseqc' include { BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE as BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_GENOME } from '../../subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse' From 1bc1b73bdf7ebd6a201402eb02c47a3976d8fec3 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Thu, 12 Dec 2024 10:53:15 +0000 Subject: [PATCH 073/107] Fix more config selectors --- workflows/rnaseq/nextflow.config | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/workflows/rnaseq/nextflow.config b/workflows/rnaseq/nextflow.config index 38fa58c50..f29916354 100644 --- a/workflows/rnaseq/nextflow.config +++ b/workflows/rnaseq/nextflow.config @@ -140,7 +140,7 @@ if (!params.skip_alignment && params.aligner == 'star_salmon') { ] } - withName: 'NFCORE_RNASEQ:RNASEQ:BAM_DEDUP_UMI_(STAR|HISAT2):BAM_SORT_STATS_SAMTOOLS:SAMTOOLS_INDEX' { + withName: 'NFCORE_RNASEQ:RNASEQ:BAM_DEDUP_UMI_STAR:BAM_SORT_STATS_SAMTOOLS:SAMTOOLS_INDEX' { publishDir = [ path: { params.save_align_intermeds || params.save_umi_intermeds ? "${params.outdir}/${params.aligner}" : params.outdir }, mode: params.publish_dir_mode, @@ -149,7 +149,7 @@ if (!params.skip_alignment && params.aligner == 'star_salmon') { ] } - withName: 'NFCORE_RNASEQ:RNASEQ:BAM_DEDUP_UMI_(STAR|HISAT2):BAM_SORT_STATS_SAMTOOLS:BAM_STATS_SAMTOOLS:.*' { + withName: 'NFCORE_RNASEQ:RNASEQ:BAM_DEDUP_UMI_STAR:BAM_SORT_STATS_SAMTOOLS:BAM_STATS_SAMTOOLS:.*' { ext.prefix = { "${meta.id}.transcriptome.sorted.bam" } publishDir = [ path: { params.save_align_intermeds || params.save_umi_intermeds ? "${params.outdir}/${params.aligner}/samtools_stats" : params.outdir }, From 9196038ee8d06cbebde598ce7af37232c4833615 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Thu, 12 Dec 2024 11:14:33 +0000 Subject: [PATCH 074/107] Don't mix transcriptome bam stats with genome ones for multiqc --- subworkflows/local/bam_dedup_umi/main.nf | 25 +++++++++--------------- 1 file changed, 9 insertions(+), 16 deletions(-) diff --git a/subworkflows/local/bam_dedup_umi/main.nf b/subworkflows/local/bam_dedup_umi/main.nf index 9390a0c43..297267e1e 100644 --- a/subworkflows/local/bam_dedup_umi/main.nf +++ b/subworkflows/local/bam_dedup_umi/main.nf @@ -101,21 +101,14 @@ workflow BAM_DEDUP_UMI { ch_dedup_transcriptome_bam = ended_transcriptome_dedup_bam.single_end .mix(UMITOOLS_PREPAREFORRSEM.out.bam) - // Collect files useful for MultiQC into one helpful emission - - ch_stats = UMI_DEDUP_GENOME.out.stats - .mix(UMI_DEDUP_TRANSCRIPTOME.out.stats) - - ch_flagstat = UMI_DEDUP_GENOME.out.flagstat - .mix(UMI_DEDUP_TRANSCRIPTOME.out.flagstat) - - ch_idxstats = UMI_DEDUP_GENOME.out.idxstats - .mix(UMI_DEDUP_TRANSCRIPTOME.out.idxstats) + // Collect files useful for MultiQC into one helpful emission. Don't + // automatically add transcriptome stats- difficult to separate in multiqc + // without a bit more work ch_multiqc_files = ch_dedup_log - .mix(ch_stats) - .mix(ch_flagstat) - .mix(ch_idxstats) + .mix(UMI_DEDUP_GENOME.out.stats) + .mix(UMI_DEDUP_GENOME.out.flagstat) + .mix(UMI_DEDUP_GENOME.out.idxstats) .transpose() .map{it[1]} @@ -129,9 +122,9 @@ workflow BAM_DEDUP_UMI { bam = UMI_DEDUP_GENOME.out.bam // channel: [ val(meta), path(bam) ] bai = bam_csi_index ? UMI_DEDUP_GENOME.out.csi : UMI_DEDUP_GENOME.out.bai // channel: [ val(meta), path(bai) ] dedup_log = ch_dedup_log // channel: [ val(meta), path(log) ] - stats = ch_stats - flagstat = ch_flagstat - idxstats = ch_idxstats + stats = UMI_DEDUP_GENOME.out.stats.mix(UMI_DEDUP_TRANSCRIPTOME.out.stats) + flagstat = UMI_DEDUP_GENOME.out.flagstat.mix(UMI_DEDUP_TRANSCRIPTOME.out.flagstat) + idxstats = UMI_DEDUP_GENOME.out.idxstats.mix(UMI_DEDUP_TRANSCRIPTOME.out.idxstats) multiqc_files = ch_multiqc_files transcriptome_bam = ch_dedup_transcriptome_bam // channel: [ val(meta), path(bam) ] versions = ch_versions // channel: [ path(versions.yml) ] From bc3cffbd2def663d1c37ccb7bb6ea0d1cf931c20 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Thu, 12 Dec 2024 17:10:43 +0000 Subject: [PATCH 075/107] Remove method from config, tidy up --- workflows/rnaseq/nextflow.config | 45 +++++++++++++------------------- 1 file changed, 18 insertions(+), 27 deletions(-) diff --git a/workflows/rnaseq/nextflow.config b/workflows/rnaseq/nextflow.config index f29916354..e7a6290fa 100644 --- a/workflows/rnaseq/nextflow.config +++ b/workflows/rnaseq/nextflow.config @@ -21,31 +21,6 @@ includeConfig "../../subworkflows/nf-core/fastq_fastqc_umitools_trimgalore/nextf includeConfig "../../subworkflows/nf-core/fastq_subsample_fq_salmon/nextflow.config" includeConfig "../../subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/nextflow.config" -def umi_dedup_args() { - if (params.umi_dedup_tool == "umicollapse") { - def algo = params.umitools_grouping_method - if (params.umitools_grouping_method == 'directional') { - algo = 'dir' - } else if (params.umitools_grouping_method == 'adjacency') { - algo = 'adj' - } else if (params.umitools_grouping_method == 'cluster') { - algo = 'cc' - } - return { [ - '--two-pass', - meta.single_end ? '' : '--paired --remove-unpaired --remove-chimeric', - params.umitools_grouping_method ? "--algo '${algo}'" : '', - params.umitools_umi_separator ? "--umi-sep '${params.umitools_umi_separator}'" : '' - ].join(' ').trim() } - } else { - return { [ - meta.single_end ? '' : '--unpaired-reads=discard --chimeric-pairs=discard', - params.umitools_grouping_method ? "--method='${params.umitools_grouping_method}'" : '', - params.umitools_umi_separator ? "--umi-separator='${params.umitools_umi_separator}'" : '' - ].join(' ').trim() } - } -} - // // STAR Salmon alignment options // @@ -160,7 +135,6 @@ if (!params.skip_alignment && params.aligner == 'star_salmon') { } withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMI(COLLAPSE|TOOLS)_TRANSCRIPTOME:UMI(COLLAPSE|TOOLS_DEDUP)' { - ext.args = umi_dedup_args() ext.prefix = { "${meta.id}.umi_dedup.transcriptome.sorted" } publishDir = [ [ @@ -249,8 +223,25 @@ if (!params.skip_alignment) { if (params.with_umi && ['star_salmon','hisat2'].contains(params.aligner)) { process { + + withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_(GEN|TRANSCRIPT)OME:UMITOOLS_DEDUP' { + ext.args = { [ + meta.single_end ? '' : '--unpaired-reads=discard --chimeric-pairs=discard', + params.umitools_grouping_method ? "--method='${params.umitools_grouping_method}'" : '', + params.umitools_umi_separator ? "--umi-separator='${params.umitools_umi_separator}'" : '' + ].join(' ').trim() } + } + + withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_(GEN|TRANSCRIPT)OME:UMICOLLAPSE' { + ext.args = { [ + '--two-pass', + meta.single_end ? '' : '--paired --remove-unpaired --remove-chimeric', + params.umitools_grouping_method ? "--algo '" + ['directional':'dir','adjacency':'adj','cluster':'cc'].get(params.umitools_grouping_method, '') + "'" : '', + params.umitools_umi_separator ? "--umi-sep '${params.umitools_umi_separator}'" : '', + ].join(' ').trim()} + } + withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMI(COLLAPSE|TOOLS)_GENOME:UMI(COLLAPSE|TOOLS_DEDUP)' { - ext.args = umi_dedup_args() ext.prefix = { "${meta.id}.umi_dedup.sorted" } publishDir = [ [ From 83777206e000b0ed2e89357f3899a4e3ea9d222e Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Thu, 12 Dec 2024 17:11:13 +0000 Subject: [PATCH 076/107] umi workflow from nf-core --- modules.json | 13 +- .../{local => nf-core}/bam_dedup_umi/main.nf | 33 ++-- subworkflows/nf-core/bam_dedup_umi/meta.yml | 181 ++++++++++++++++++ .../nf-core/bam_dedup_umi/tests/main.nf.test | 99 ++++++++++ .../bam_dedup_umi/tests/main.nf.test.snap | 109 +++++++++++ .../bam_dedup_umi/tests/nextflow.config | 38 ++++ workflows/rnaseq/main.nf | 8 +- 7 files changed, 455 insertions(+), 26 deletions(-) rename subworkflows/{local => nf-core}/bam_dedup_umi/main.nf (75%) create mode 100644 subworkflows/nf-core/bam_dedup_umi/meta.yml create mode 100644 subworkflows/nf-core/bam_dedup_umi/tests/main.nf.test create mode 100644 subworkflows/nf-core/bam_dedup_umi/tests/main.nf.test.snap create mode 100644 subworkflows/nf-core/bam_dedup_umi/tests/nextflow.config diff --git a/modules.json b/modules.json index 43fdc1f9a..8957c2ef4 100644 --- a/modules.json +++ b/modules.json @@ -212,7 +212,7 @@ "samtools/sort": { "branch": "master", "git_sha": "b7800db9b069ed505db3f9d91b8c72faea9be17b", - "installed_by": ["bam_sort_stats_samtools"] + "installed_by": ["bam_dedup_umi", "bam_sort_stats_samtools"] }, "samtools/stats": { "branch": "master", @@ -290,7 +290,7 @@ "umitools/prepareforrsem": { "branch": "master", "git_sha": "666652151335353eef2fcd58880bcef5bc2928e1", - "installed_by": ["modules"] + "installed_by": ["bam_dedup_umi", "modules"] }, "untar": { "branch": "master", @@ -304,11 +304,16 @@ "bam_dedup_stats_samtools_umicollapse": { "branch": "master", "git_sha": "0b27602842d3d79fd0e8db79f4afa764967fc3d1", - "installed_by": ["subworkflows"] + "installed_by": ["bam_dedup_umi", "subworkflows"] }, "bam_dedup_stats_samtools_umitools": { "branch": "master", "git_sha": "0b27602842d3d79fd0e8db79f4afa764967fc3d1", + "installed_by": ["bam_dedup_umi", "subworkflows"] + }, + "bam_dedup_umi": { + "branch": "master", + "git_sha": "b5828f47c17c41ce3a4c70b863c99207e3f6d37c", "installed_by": ["subworkflows"] }, "bam_markduplicates_picard": { @@ -324,7 +329,7 @@ "bam_sort_stats_samtools": { "branch": "master", "git_sha": "763d4b5c05ffda3ac1ac969dc67f7458cfb2eb1d", - "installed_by": ["fastq_align_hisat2"] + "installed_by": ["bam_dedup_umi", "fastq_align_hisat2"] }, "bam_stats_samtools": { "branch": "master", diff --git a/subworkflows/local/bam_dedup_umi/main.nf b/subworkflows/nf-core/bam_dedup_umi/main.nf similarity index 75% rename from subworkflows/local/bam_dedup_umi/main.nf rename to subworkflows/nf-core/bam_dedup_umi/main.nf index 297267e1e..d091a5824 100644 --- a/subworkflows/local/bam_dedup_umi/main.nf +++ b/subworkflows/nf-core/bam_dedup_umi/main.nf @@ -2,13 +2,14 @@ // BAM deduplication with UMI processing // -include { BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE as BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_TRANSCRIPTOME } from '../../../subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse' -include { BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS as BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_TRANSCRIPTOME } from '../../../subworkflows/nf-core/bam_dedup_stats_samtools_umitools' -include { BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE as BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_GENOME } from '../../../subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse' -include { BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS as BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME } from '../../../subworkflows/nf-core/bam_dedup_stats_samtools_umitools' -include { BAM_SORT_STATS_SAMTOOLS } from '../../../subworkflows/nf-core/bam_sort_stats_samtools' -include { UMITOOLS_PREPAREFORRSEM } from '../../../modules/nf-core/umitools/prepareforrsem' -include { SAMTOOLS_SORT } from '../../../modules/nf-core/samtools/sort/main' +include { BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE as BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_TRANSCRIPTOME } from '../bam_dedup_stats_samtools_umicollapse' +include { BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS as BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_TRANSCRIPTOME } from '../bam_dedup_stats_samtools_umitools' +include { BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE as BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_GENOME } from '../bam_dedup_stats_samtools_umicollapse' +include { BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS as BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME } from '../bam_dedup_stats_samtools_umitools' +include { BAM_SORT_STATS_SAMTOOLS } from '../bam_sort_stats_samtools' + +include { UMITOOLS_PREPAREFORRSEM } from '../../../modules/nf-core/umitools/prepareforrsem' +include { SAMTOOLS_SORT } from '../../../modules/nf-core/samtools/sort/main' workflow BAM_DEDUP_UMI { take: @@ -119,13 +120,13 @@ workflow BAM_DEDUP_UMI { .mix(UMITOOLS_PREPAREFORRSEM.out.versions) emit: - bam = UMI_DEDUP_GENOME.out.bam // channel: [ val(meta), path(bam) ] - bai = bam_csi_index ? UMI_DEDUP_GENOME.out.csi : UMI_DEDUP_GENOME.out.bai // channel: [ val(meta), path(bai) ] - dedup_log = ch_dedup_log // channel: [ val(meta), path(log) ] - stats = UMI_DEDUP_GENOME.out.stats.mix(UMI_DEDUP_TRANSCRIPTOME.out.stats) - flagstat = UMI_DEDUP_GENOME.out.flagstat.mix(UMI_DEDUP_TRANSCRIPTOME.out.flagstat) - idxstats = UMI_DEDUP_GENOME.out.idxstats.mix(UMI_DEDUP_TRANSCRIPTOME.out.idxstats) - multiqc_files = ch_multiqc_files - transcriptome_bam = ch_dedup_transcriptome_bam // channel: [ val(meta), path(bam) ] - versions = ch_versions // channel: [ path(versions.yml) ] + bam = UMI_DEDUP_GENOME.out.bam // channel: [ val(meta), path(bam) ] + bai = bam_csi_index ? UMI_DEDUP_GENOME.out.csi : UMI_DEDUP_GENOME.out.bai // channel: [ val(meta), path(bai) ] + dedup_log = ch_dedup_log // channel: [ val(meta), path(log) ] + stats = UMI_DEDUP_GENOME.out.stats.mix(UMI_DEDUP_TRANSCRIPTOME.out.stats) // channel: [ val(meta), path(stats)] + flagstat = UMI_DEDUP_GENOME.out.flagstat.mix(UMI_DEDUP_TRANSCRIPTOME.out.flagstat) // channel: [ val(meta), path(flagstat)] + idxstats = UMI_DEDUP_GENOME.out.idxstats.mix(UMI_DEDUP_TRANSCRIPTOME.out.idxstats) // channel: [ val(meta), path(idxstats)] + multiqc_files = ch_multiqc_files // channel: file + transcriptome_bam = ch_dedup_transcriptome_bam // channel: [ val(meta), path(bam) ] + versions = ch_versions // channel: [ path(versions.yml) ] } diff --git a/subworkflows/nf-core/bam_dedup_umi/meta.yml b/subworkflows/nf-core/bam_dedup_umi/meta.yml new file mode 100644 index 000000000..f88fa2f1c --- /dev/null +++ b/subworkflows/nf-core/bam_dedup_umi/meta.yml @@ -0,0 +1,181 @@ +name: "bam_dedup_umi" +description: BAM deduplication with UMI processing for both genome and transcriptome alignments +keywords: + - deduplication + - UMI + - BAM + - genome + - transcriptome + - umicollapse + - umitools + +components: + - umitools/prepareforrsem + - samtools/sort + - bam_dedup_stats_samtools_umicollapse + - bam_dedup_stats_samtools_umitools + - bam_sort_stats_samtools + +input: + - ch_genome_bam: + description: Channel with genome BAM files + structure: + - meta: + type: map + description: Metadata map + - bam: + type: file + description: BAM file + pattern: "*.bam" + - bai: + type: file + description: BAM index file + pattern: "*.bai" + - ch_fasta: + description: Channel with genome FASTA file + structure: + - meta: + type: map + description: Metadata map + - fasta: + type: file + description: Genome FASTA file + pattern: "*.{fa,fasta}" + - umi_dedup_tool: + description: UMI deduplication tool to use + structure: + - value: + type: string + description: Either 'umicollapse' or 'umitools' + - umitools_dedup_stats: + description: Whether to generate UMI-tools deduplication stats + structure: + - value: + type: boolean + description: True or False + - bam_csi_index: + description: Whether to generate CSI index + structure: + - value: + type: boolean + description: True or False + - ch_transcriptome_bam: + description: Channel with transcriptome BAM files + structure: + - meta: + type: map + description: Metadata map + - bam: + type: file + description: BAM file + pattern: "*.bam" + - ch_transcript_fasta: + description: Channel with transcript FASTA file + structure: + - meta: + type: map + description: Metadata map + - fasta: + type: file + description: Transcript FASTA file + pattern: "*.{fa,fasta}" + +output: + - bam: + description: Channel containing deduplicated genome BAM files + structure: + - meta: + type: map + description: Metadata map + - bam: + type: file + description: Deduplicated BAM file + pattern: "*.bam" + - bai: + description: Channel containing indexed BAM (BAI) files + structure: + - meta: + type: map + description: Metadata map + - bai: + type: file + description: BAM index file + pattern: "*.bai" + - csi: + description: Channel containing CSI files (if bam_csi_index is true) + structure: + - meta: + type: map + description: Metadata map + - csi: + type: file + description: CSI index file + pattern: "*.csi" + - dedup_log: + description: Channel containing deduplication log files + structure: + - meta: + type: map + description: Metadata map + - log: + type: file + description: Deduplication log file + pattern: "*.log" + - stats: + description: Channel containing BAM statistics files + structure: + - meta: + type: map + description: Metadata map + - stats: + type: file + description: BAM statistics file + pattern: "*.stats" + - flagstat: + description: Channel containing flagstat files + structure: + - meta: + type: map + description: Metadata map + - flagstat: + type: file + description: Flagstat file + pattern: "*.flagstat" + - idxstats: + description: Channel containing idxstats files + structure: + - meta: + type: map + description: Metadata map + - idxstats: + type: file + description: Idxstats file + pattern: "*.idxstats" + - multiqc_files: + description: Channel containing files for MultiQC + structure: + - file: + type: file + description: File for MultiQC + - transcriptome_bam: + description: Channel containing deduplicated transcriptome BAM files + structure: + - meta: + type: map + description: Metadata map + - bam: + type: file + description: Deduplicated transcriptome BAM file + pattern: "*.bam" + - versions: + description: Channel containing software versions file + structure: + - versions: + type: file + description: File containing versions of the software used + pattern: "versions.yml" + +authors: + - "@pinin4fjords" +maintainers: + - "@pinin4fjords" diff --git a/subworkflows/nf-core/bam_dedup_umi/tests/main.nf.test b/subworkflows/nf-core/bam_dedup_umi/tests/main.nf.test new file mode 100644 index 000000000..b8e9da9ab --- /dev/null +++ b/subworkflows/nf-core/bam_dedup_umi/tests/main.nf.test @@ -0,0 +1,99 @@ +nextflow_workflow { + + name "Test Workflow BAM_DEDUP_UMI" + script "../main.nf" + workflow "BAM_DEDUP_UMI" + config "./nextflow.config" + + tag "subworkflows" + tag "subworkflows_nfcore" + tag "subworkflows/bam_dedup_umi" + tag "bam_dedup_stats_samtools_umicollapse" + tag "bam_dedup_stats_samtools_umitools" + tag "bam_sort_stats_samtools" + tag "umitools/prepareforrsem" + tag "samtools/sort" + + test("sarscov2_bam_bai - umitools - with transcriptome bams") { + + when { + workflow { + """ + input[0] = [ + [ id:'test'], // meta map + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.umi.sorted.bam', checkIfExists: true), + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.umi.sorted.bam.bai', checkIfExists: true) + ] + input[1] = [ + [id:'genome'], + file(params.modules_testdata_base_path + '/genomics/sarscov2/genome/genome.fasta', checkIfExists: true) + ] + input[2] = 'umitools' + input[3] = false + input[4] = false + input[5] = [ + [ id:'test'], // meta map + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.umi.sorted.bam', checkIfExists: true) ] + input[6] = [ + [id:'genome'], + file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/transcriptome.fasta', checkIfExists: true) + ] + """ + } + } + + then { + assertAll( + { assert workflow.success }, + { assert snapshot( + bam(workflow.out.bam[0][1]).getReadsMD5(), + bam(workflow.out.transcriptome_bam[0][1]).getReadsMD5(), + workflow.out.stats, + workflow.out.flagstat, + workflow.out.idxstats, + workflow.out.versions + ).match() }, + { assert path(workflow.out.bai.get(0).get(1)).exists() } + ) + } + + } + + test("sarscov2_bam_bai - umicollapse - no transcriptome bams") { + + when { + workflow { + """ + input[0] = [ + [ id:'test'], // meta map + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.umi.sorted.bam', checkIfExists: true), + file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.umi.sorted.bam.bai', checkIfExists: true) + ] + input[1] = [ + [id:'genome'], + file(params.modules_testdata_base_path + '/genomics/sarscov2/genome/genome.fasta', checkIfExists: true) + ] + input[2] = 'umicollapse' + input[3] = false + input[4] = false + input[5] = Channel.empty() + input[6] = Channel.empty() + """ + } + } + + then { + assertAll( + { assert workflow.success }, + { assert snapshot( + bam(workflow.out.bam[0][1]).getReadsMD5(), + workflow.out.stats, + workflow.out.flagstat, + workflow.out.idxstats, + workflow.out.versions + ).match() }, + { assert path(workflow.out.bai.get(0).get(1)).exists() } + ) + } + } +} \ No newline at end of file diff --git a/subworkflows/nf-core/bam_dedup_umi/tests/main.nf.test.snap b/subworkflows/nf-core/bam_dedup_umi/tests/main.nf.test.snap new file mode 100644 index 000000000..94151f04b --- /dev/null +++ b/subworkflows/nf-core/bam_dedup_umi/tests/main.nf.test.snap @@ -0,0 +1,109 @@ +{ + "sarscov2_bam_bai - umicollapse - no transcriptome bams": { + "content": [ + "c1917631c47d16320d002b867e226a2e", + [ + [ + { + "id": "test" + }, + "test.umi_dedup.genome.sorted.bam.stats:md5,a03e635b85a846a2650c7b747926c7ed" + ] + ], + [ + [ + { + "id": "test" + }, + "test.umi_dedup.genome.sorted.bam.flagstat:md5,18d602435a02a4d721b78d1812622159" + ] + ], + [ + [ + { + "id": "test" + }, + "test.umi_dedup.genome.sorted.bam.idxstats:md5,85d20a901eef23ca50c323638a2eb602" + ] + ], + [ + "versions.yml:md5,3fe6727d1a90130a932400464233ab5d", + "versions.yml:md5,5d91176bd409dc54816e7e3a7773b5ef", + "versions.yml:md5,7eaf0c81f627dde8e2710a57e19b4d87", + "versions.yml:md5,d6bba04463b79564f053e7e5033025dd", + "versions.yml:md5,f90574f645fd8876ba52b89b2272afc1" + ] + ], + "meta": { + "nf-test": "0.9.2", + "nextflow": "24.10.2" + }, + "timestamp": "2024-12-12T13:04:25.48875" + }, + "sarscov2_bam_bai - umitools - with transcriptome bams": { + "content": [ + "c1917631c47d16320d002b867e226a2e", + "fe38ce657d4208a850702bdbfaa062ca", + [ + [ + { + "id": "test" + }, + "test.umi_dedup.genome.sorted.bam.stats:md5,a03e635b85a846a2650c7b747926c7ed" + ], + [ + { + "id": "test" + }, + "test.umi_dedup.transcriptome.sorted.bam.stats:md5,720f03daf649606a50c2cccbf135edc5" + ] + ], + [ + [ + { + "id": "test" + }, + "test.umi_dedup.genome.sorted.bam.flagstat:md5,18d602435a02a4d721b78d1812622159" + ], + [ + { + "id": "test" + }, + "test.umi_dedup.transcriptome.sorted.bam.flagstat:md5,18d602435a02a4d721b78d1812622159" + ] + ], + [ + [ + { + "id": "test" + }, + "test.umi_dedup.genome.sorted.bam.idxstats:md5,85d20a901eef23ca50c323638a2eb602" + ], + [ + { + "id": "test" + }, + "test.umi_dedup.transcriptome.sorted.bam.idxstats:md5,85d20a901eef23ca50c323638a2eb602" + ] + ], + [ + "versions.yml:md5,017dce06e6fadba312ce46808e2b19ff", + "versions.yml:md5,0ca2bd583dd1951c792b526f768db343", + "versions.yml:md5,1e761d924c3e0f35284799aa566e7c1a", + "versions.yml:md5,43e34c4518b8d546b632b372bc282ac4", + "versions.yml:md5,9d7724974d6dc288b485b5509c89297b", + "versions.yml:md5,a4562f4cc5d04041a79ba57761e231d9", + "versions.yml:md5,acfef82b716e50563fb22f47f33de00a", + "versions.yml:md5,afd68b9511aecba9cc5d6f7dd4c34bbd", + "versions.yml:md5,c2fda58ea2b120a510502b9db24d8a16", + "versions.yml:md5,d531cd7e744b2f95659bdb544f2cd8a5", + "versions.yml:md5,e22174a7d3003395e8e8c34ebfba1719" + ] + ], + "meta": { + "nf-test": "0.9.2", + "nextflow": "24.10.2" + }, + "timestamp": "2024-12-12T13:04:12.182087" + } +} \ No newline at end of file diff --git a/subworkflows/nf-core/bam_dedup_umi/tests/nextflow.config b/subworkflows/nf-core/bam_dedup_umi/tests/nextflow.config new file mode 100644 index 000000000..cd10b5c78 --- /dev/null +++ b/subworkflows/nf-core/bam_dedup_umi/tests/nextflow.config @@ -0,0 +1,38 @@ +process { + withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMI(COLLAPSE|TOOLS)_GENOME:UMI(COLLAPSE|TOOLS_DEDUP)' { + ext.prefix = { "${meta.id}_umi_dedup.genome" } + } + + withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMI(COLLAPSE|TOOLS)_TRANSCRIPTOME:UMI(COLLAPSE|TOOLS_DEDUP)' { + ext.prefix = { "${meta.id}.umi_dedup.transcriptome.sorted" } + } + + withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMI(COLLAPSE|TOOLS)_GENOME:UMI(COLLAPSE|TOOLS_DEDUP)' { + ext.prefix = { "${meta.id}.umi_dedup.genome.sorted" } + } + + withName: 'UMITOOLS_PREPAREFORRSEM' { + ext.prefix = { "${meta.id}_preparedforrsem" } + } + + withName: 'BAM_DEDUP_UMI:SAMTOOLS_SORT' { + ext.args = '-n' + ext.prefix = { "${meta.id}.umi_dedup.transcriptome" } + } + + withName: 'BAM_DEDUP_UMI:BAM_SORT_STATS_SAMTOOLS:SAMTOOLS_SORT' { + ext.prefix = { "${meta.id}.transcriptome.sorted" } + } + + withName: 'BAM_DEDUP_UMI:BAM_SORT_STATS_SAMTOOLS:BAM_STATS_SAMTOOLS:.*' { + ext.prefix = { "${meta.id}.transcriptome.sorted.bam" } + } + + withName: 'BAM_DEDUP_UMI:BAM_DEDUP_STATS_SAMTOOLS_UMI(COLLAPSE|TOOLS)_TRANSCRIPTOME:BAM_STATS_SAMTOOLS:.*' { + ext.prefix = { "${meta.id}.umi_dedup.transcriptome.sorted.bam" } + } + + withName: 'BAM_DEDUP_UMI:BAM_DEDUP_STATS_SAMTOOLS_UMI(COLLAPSE|TOOLS)_GENOME:BAM_STATS_SAMTOOLS:.*' { + ext.prefix = { "${meta.id}.umi_dedup.genome.sorted.bam" } + } +} diff --git a/workflows/rnaseq/main.nf b/workflows/rnaseq/main.nf index 63c3678de..508aeb5fb 100755 --- a/workflows/rnaseq/main.nf +++ b/workflows/rnaseq/main.nf @@ -17,8 +17,8 @@ include { MULTIQC_CUSTOM_BIOTYPE } from '../../modules/local/multiqc // include { ALIGN_STAR } from '../../subworkflows/local/align_star' include { QUANTIFY_RSEM } from '../../subworkflows/local/quantify_rsem' -include { BAM_DEDUP_UMI as BAM_DEDUP_UMI_STAR } from '../../subworkflows/local/bam_dedup_umi' -include { BAM_DEDUP_UMI as BAM_DEDUP_UMI_HISAT2 } from '../../subworkflows/local/bam_dedup_umi' +include { BAM_DEDUP_UMI as BAM_DEDUP_UMI_STAR } from '../../subworkflows/nf-core/bam_dedup_umi' +include { BAM_DEDUP_UMI as BAM_DEDUP_UMI_HISAT2 } from '../../subworkflows/nf-core/bam_dedup_umi' include { checkSamplesAfterGrouping } from '../../subworkflows/local/utils_nfcore_rnaseq_pipeline' include { multiqcTsvFromList } from '../../subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness' @@ -57,10 +57,6 @@ include { softwareVersionsToYAML } from '../../subworkflows/nf-core/ut include { FASTQ_ALIGN_HISAT2 } from '../../subworkflows/nf-core/fastq_align_hisat2' include { BAM_MARKDUPLICATES_PICARD } from '../../subworkflows/nf-core/bam_markduplicates_picard' include { BAM_RSEQC } from '../../subworkflows/nf-core/bam_rseqc' -include { BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE as BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_GENOME } from '../../subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse' -include { BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE as BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_TRANSCRIPTOME } from '../../subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse' -include { BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS as BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME } from '../../subworkflows/nf-core/bam_dedup_stats_samtools_umitools' -include { BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS as BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_TRANSCRIPTOME } from '../../subworkflows/nf-core/bam_dedup_stats_samtools_umitools' include { BEDGRAPH_BEDCLIP_BEDGRAPHTOBIGWIG as BEDGRAPH_BEDCLIP_BEDGRAPHTOBIGWIG_FORWARD } from '../../subworkflows/nf-core/bedgraph_bedclip_bedgraphtobigwig' include { BEDGRAPH_BEDCLIP_BEDGRAPHTOBIGWIG as BEDGRAPH_BEDCLIP_BEDGRAPHTOBIGWIG_REVERSE } from '../../subworkflows/nf-core/bedgraph_bedclip_bedgraphtobigwig' include { QUANTIFY_PSEUDO_ALIGNMENT as QUANTIFY_STAR_SALMON } from '../../subworkflows/nf-core/quantify_pseudo_alignment' From 0908456b28e2dc2ea3db86b10b6b67ea14ef874f Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Tue, 17 Dec 2024 17:01:25 +0000 Subject: [PATCH 077/107] linting docs fix --- docs/output.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/output.md b/docs/output.md index 3203f2e64..0bea48601 100644 --- a/docs/output.md +++ b/docs/output.md @@ -74,7 +74,7 @@ The pipeline is built using [Nextflow](https://www.nextflow.io/) and processes d If multiple libraries/runs have been provided for the same sample in the input samplesheet (e.g. to increase sequencing depth) then these will be merged at the very beginning of the pipeline in order to have consistent sample naming throughout the pipeline. Please refer to the [usage documentation](https://nf-co.re/rnaseq/usage#samplesheet-input) to see how to specify these samples in the input samplesheet. -# fq lint +### fq lint
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zXwhtT%ei{n#FzEH|C;yZ>+$!u_x#*+`=L8{b9SH^9&27u3G_Gxqxe`L2UJtdxghk z&-wzDFvLvW{chK5u3{n6GSKKy!P&C6w^IFpbD0bcp^A{{2lcLh_DXj@ybtYvc^;(2 M)78&qol`;+0Fu7JivR!s literal 0 HcmV?d00001 From 0761530e6f53a3d2f4cba806017df873375b7fb1 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Tue, 17 Dec 2024 17:20:54 +0000 Subject: [PATCH 079/107] Update CHANGELOG.md --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index e753f935d..4262b5d3b 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -19,6 +19,7 @@ Special thanks to the following for their contributions to the release: - [PR #1463](https://github.com/nf-core/rnaseq/pull/1463) - Move channel operations outside of the onComplete() block - [PR #1467](https://github.com/nf-core/rnaseq/pull/1467) - Add test suite for UMI handling functionality - [PR #1466](https://github.com/nf-core/rnaseq/pull/1466) - Factor out UMI handling +- [PR #1469](https://github.com/nf-core/rnaseq/pull/1469) - Minor docs fix ### Software dependencies From 68d4e81044146f0f61de2a46fa93dfc03863a965 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Tue, 17 Dec 2024 17:28:05 +0000 Subject: [PATCH 080/107] Update subworkflow to account for fix to bad argument handling --- modules.json | 2 +- .../nf-core/fastq_qc_trim_filter_setstrandedness/main.nf | 3 ++- .../nf-core/fastq_qc_trim_filter_setstrandedness/meta.yml | 4 ++++ .../fastq_qc_trim_filter_setstrandedness/tests/main.nf.test | 2 ++ workflows/rnaseq/main.nf | 3 ++- 5 files changed, 11 insertions(+), 3 deletions(-) diff --git a/modules.json b/modules.json index 8957c2ef4..663aa51fa 100644 --- a/modules.json +++ b/modules.json @@ -363,7 +363,7 @@ }, "fastq_qc_trim_filter_setstrandedness": { "branch": "master", - "git_sha": "2c0260ed80daeca9c6dfa477a4daf04ff336dc37", + "git_sha": "8a01490596dbe8443bd6bc6ae4207ae9485f33e3", "installed_by": ["subworkflows"] }, "fastq_subsample_fq_salmon": { diff --git a/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/main.nf b/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/main.nf index 7a4041c25..6149143ad 100644 --- a/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/main.nf +++ b/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/main.nf @@ -111,6 +111,7 @@ workflow FASTQ_QC_TRIM_FILTER_SETSTRANDEDNESS { stranded_threshold // float: The fraction of stranded reads that must be assigned to a strandedness for confident assignment. Must be at least 0.5 unstranded_threshold // float: The difference in fraction of stranded reads assigned to 'forward' and 'reverse' below which a sample is classified as 'unstranded' skip_linting // boolean: true/false + fastp_merge // boolean: true/false: whether to stitch paired end reads together in FASTP output main: @@ -191,7 +192,7 @@ workflow FASTQ_QC_TRIM_FILTER_SETSTRANDEDNESS { skip_trimming, [], save_trimmed, - save_trimmed, + fastp_merge, min_trimmed_reads ) ch_filtered_reads = FASTQ_FASTQC_UMITOOLS_FASTP.out.reads diff --git a/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/meta.yml b/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/meta.yml index 433837d70..4cf6a0096 100644 --- a/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/meta.yml +++ b/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/meta.yml @@ -142,6 +142,10 @@ input: - skip_linting: type: boolean description: Whether to skip linting of FastQ files + - fastp_merge: + type: boolean + description: | + For FASTP, save merged fastqs stitching together read1 and read2 for paired end reads output: - reads: diff --git a/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/tests/main.nf.test b/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/tests/main.nf.test index 99c4d931a..c5fc4aec9 100644 --- a/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/tests/main.nf.test +++ b/subworkflows/nf-core/fastq_qc_trim_filter_setstrandedness/tests/main.nf.test @@ -56,6 +56,7 @@ nextflow_workflow { input[20] = 0.8 // stranded_threshold input[21] = 0.1 // unstranded_threshold input[22] = false // skip_linting + input[23] = true // fastp_merge """ } } @@ -121,6 +122,7 @@ nextflow_workflow { input[20] = 0.8 // stranded_threshold input[21] = 0.1 // unstranded_threshold input[22] = false // skip_linting + input[23] = true // fastp_merge """ } } diff --git a/workflows/rnaseq/main.nf b/workflows/rnaseq/main.nf index 508aeb5fb..27c7e5639 100755 --- a/workflows/rnaseq/main.nf +++ b/workflows/rnaseq/main.nf @@ -155,7 +155,8 @@ workflow RNASEQ { params.umi_discard_read, params.stranded_threshold, params.unstranded_threshold, - params.skip_linting + params.skip_linting, + false ) ch_multiqc_files = ch_multiqc_files.mix(FASTQ_QC_TRIM_FILTER_SETSTRANDEDNESS.out.multiqc_files) From ca92fe71c8ed4a766e68aa83d6b1db5f4ab76b0a Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Tue, 17 Dec 2024 17:31:38 +0000 Subject: [PATCH 081/107] Update changelog --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index e753f935d..80b585084 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -19,6 +19,7 @@ Special thanks to the following for their contributions to the release: - [PR #1463](https://github.com/nf-core/rnaseq/pull/1463) - Move channel operations outside of the onComplete() block - [PR #1467](https://github.com/nf-core/rnaseq/pull/1467) - Add test suite for UMI handling functionality - [PR #1466](https://github.com/nf-core/rnaseq/pull/1466) - Factor out UMI handling +- [PR #1470](https://github.com/nf-core/rnaseq/pull/1470) - Update subworkflow to account for fix to bad argument handling ### Software dependencies From dd265360fcedd46450d61d17144051d04d0e0f09 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Tue, 17 Dec 2024 17:46:35 +0000 Subject: [PATCH 082/107] Update CHANGELOG.md --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index e753f935d..fe3025a4f 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -19,6 +19,7 @@ Special thanks to the following for their contributions to the release: - [PR #1463](https://github.com/nf-core/rnaseq/pull/1463) - Move channel operations outside of the onComplete() block - [PR #1467](https://github.com/nf-core/rnaseq/pull/1467) - Add test suite for UMI handling functionality - [PR #1466](https://github.com/nf-core/rnaseq/pull/1466) - Factor out UMI handling +- [PR #1459](https://github.com/nf-core/rnaseq/pull/1466) - Remove reference to unused "skip_sample_count" value in email templates ### Software dependencies From 254ad0466eafd702ed79c10096946ccdb5a1d1ab Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Wed, 18 Dec 2024 11:45:13 +0000 Subject: [PATCH 083/107] Fix prepare_genome subworkflow for sortmerna --- subworkflows/local/prepare_genome/main.nf | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/subworkflows/local/prepare_genome/main.nf b/subworkflows/local/prepare_genome/main.nf index 92f8cc04a..c1b8b4c66 100644 --- a/subworkflows/local/prepare_genome/main.nf +++ b/subworkflows/local/prepare_genome/main.nf @@ -233,16 +233,18 @@ workflow PREPARE_GENOME { if ('sortmerna' in prepare_tool_indices) { ribo_db = file(sortmerna_fasta_list) + // SortMeRNA needs the rRNAs even if we're providing the index + ch_rrna_fastas = Channel.from(ribo_db.readLines()) + .map { row -> file(row, checkIfExists: true) } + if (sortmerna_index) { if (sortmerna_index.endsWith('.tar.gz')) { ch_sortmerna_index = UNTAR_SORTMERNA_INDEX ( [ [:], sortmerna_index ] ).untar.map { it[1] } ch_versions = ch_versions.mix(UNTAR_SORTMERNA_INDEX.out.versions) } else { - ch_sortmerna_index = Channel.value(file(sortmerna_index)) + ch_sortmerna_index = Channel.value([[:], file(sortmerna_index)]) } } else { - ch_rrna_fastas = Channel.from(ribo_db.readLines()) - .map { row -> file(row, checkIfExists: true) } SORTMERNA_INDEX ( Channel.of([ [],[] ]), From ae6990ac300197bf9f499017b6aa63694c22d23b Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Wed, 18 Dec 2024 15:13:31 +0000 Subject: [PATCH 084/107] Update CHANGELOG.md --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index a1059f292..5f82b2dd1 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -21,6 +21,7 @@ Special thanks to the following for their contributions to the release: - [PR #1466](https://github.com/nf-core/rnaseq/pull/1466) - Factor out UMI handling - [PR #1469](https://github.com/nf-core/rnaseq/pull/1469) - Minor docs fix - [PR #1459](https://github.com/nf-core/rnaseq/pull/1466) - Remove reference to unused "skip_sample_count" value in email templates +- [PR #1471](https://github.com/nf-core/rnaseq/pull/1471) - Fix prepare_genome subworkflow for sortmerna ### Software dependencies From b23cca809ec74383a461497411abfb1bf53efaa3 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Wed, 18 Dec 2024 18:14:34 +0000 Subject: [PATCH 085/107] Bump STAR modules --- modules.json | 4 +- modules/nf-core/star/align/environment.yml | 4 +- modules/nf-core/star/align/main.nf | 2 +- modules/nf-core/star/align/star-align.diff | 16 ++-- .../star/align/tests/main.nf.test.snap | 90 +++++++++---------- .../star/genomegenerate/environment.yml | 4 +- modules/nf-core/star/genomegenerate/main.nf | 2 +- .../genomegenerate/star-genomegenerate.diff | 12 +-- .../genomegenerate/tests/main.nf.test.snap | 36 ++++---- 9 files changed, 85 insertions(+), 85 deletions(-) diff --git a/modules.json b/modules.json index 663aa51fa..496b6a7d9 100644 --- a/modules.json +++ b/modules.json @@ -227,13 +227,13 @@ }, "star/align": { "branch": "master", - "git_sha": "a5ad53288c79fa52c5ae708c317e09ec2dd149ab", + "git_sha": "6a367612c2c27aa659293c656afa764361223db4", "installed_by": ["modules"], "patch": "modules/nf-core/star/align/star-align.diff" }, "star/genomegenerate": { "branch": "master", - "git_sha": "a5ad53288c79fa52c5ae708c317e09ec2dd149ab", + "git_sha": "6a367612c2c27aa659293c656afa764361223db4", "installed_by": ["modules"], "patch": "modules/nf-core/star/genomegenerate/star-genomegenerate.diff" }, diff --git a/modules/nf-core/star/align/environment.yml b/modules/nf-core/star/align/environment.yml index 7c57530a3..9ed1940fa 100644 --- a/modules/nf-core/star/align/environment.yml +++ b/modules/nf-core/star/align/environment.yml @@ -3,7 +3,7 @@ channels: - bioconda dependencies: - - bioconda::htslib=1.20 - - bioconda::samtools=1.20 + - bioconda::htslib=1.21 + - bioconda::samtools=1.21 - bioconda::star=2.7.11b - conda-forge::gawk=5.1.0 diff --git a/modules/nf-core/star/align/main.nf b/modules/nf-core/star/align/main.nf index eadddb03f..c6b651bdd 100644 --- a/modules/nf-core/star/align/main.nf +++ b/modules/nf-core/star/align/main.nf @@ -3,7 +3,7 @@ process STAR_ALIGN { label 'process_high' conda "${moduleDir}/environment.yml" - container 'nf-core/star_samtools_htslib_gawk:10c6e8c834460019' + container 'nf-core/htslib_samtools_star_gawk:311d422a50e6d829' input: tuple val(meta), path(reads, stageAs: "input*/*") diff --git a/modules/nf-core/star/align/star-align.diff b/modules/nf-core/star/align/star-align.diff index ea28fb1aa..ed59490be 100644 --- a/modules/nf-core/star/align/star-align.diff +++ b/modules/nf-core/star/align/star-align.diff @@ -1,4 +1,6 @@ Changes in module 'nf-core/star/align' +'modules/nf-core/star/align/environment.yml' is unchanged +'modules/nf-core/star/align/meta.yml' is unchanged Changes in 'star/align/main.nf': --- modules/nf-core/star/align/main.nf +++ modules/nf-core/star/align/main.nf @@ -7,19 +9,18 @@ Changes in 'star/align/main.nf': conda "${moduleDir}/environment.yml" - container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container ? -- 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/b4/b425bc2a95806d878993f9a66dae3ae80ac2dafff4c208c5ae01b7a90a32fa91/data' : -- 'community.wave.seqera.io/library/star_samtools_htslib_gawk:10c6e8c834460019' }" -+ container 'nf-core/star_samtools_htslib_gawk:10c6e8c834460019' +- 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/10/101ea47973178f85ff66a34de6a7462aaf99d947d3924c27ce8a2d5a63009065/data' : +- 'community.wave.seqera.io/library/htslib_samtools_star_gawk:311d422a50e6d829' }" ++ container 'nf-core/htslib_samtools_star_gawk:311d422a50e6d829' input: tuple val(meta), path(reads, stageAs: "input*/*") -'modules/nf-core/star/align/meta.yml' is unchanged -'modules/nf-core/star/align/environment.yml' is unchanged 'modules/nf-core/star/align/tests/main.nf.test.snap' is unchanged -'modules/nf-core/star/align/tests/nextflow.config' is unchanged -'modules/nf-core/star/align/tests/nextflow.starfusion.config' is unchanged 'modules/nf-core/star/align/tests/nextflow.arriba.config' is unchanged +'modules/nf-core/star/align/tests/tags.yml' is unchanged +'modules/nf-core/star/align/tests/nextflow.starfusion.config' is unchanged +'modules/nf-core/star/align/tests/nextflow.config' is unchanged Changes in 'star/align/tests/main.nf.test': --- modules/nf-core/star/align/tests/main.nf.test +++ modules/nf-core/star/align/tests/main.nf.test @@ -36,5 +37,4 @@ Changes in 'star/align/tests/main.nf.test': test("homo_sapiens - single_end") { config "./nextflow.config" -'modules/nf-core/star/align/tests/tags.yml' was removed ************************************************************ diff --git a/modules/nf-core/star/align/tests/main.nf.test.snap b/modules/nf-core/star/align/tests/main.nf.test.snap index b533fb8b4..a1ec3a3d0 100644 --- a/modules/nf-core/star/align/tests/main.nf.test.snap +++ b/modules/nf-core/star/align/tests/main.nf.test.snap @@ -97,7 +97,7 @@ ] ], "3": [ - "versions.yml:md5,a149bba1dbb5194560abdd813c7848e3" + "versions.yml:md5,b416145d7b5b8a080e832a7f7cde4c44" ], "4": [ [ @@ -307,7 +307,7 @@ ] ], "versions": [ - "versions.yml:md5,a149bba1dbb5194560abdd813c7848e3" + "versions.yml:md5,b416145d7b5b8a080e832a7f7cde4c44" ], "wig": [ [ @@ -321,10 +321,10 @@ } ], "meta": { - "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nf-test": "0.9.2", + "nextflow": "24.10.2" }, - "timestamp": "2024-10-20T17:09:08.738074176" + "timestamp": "2024-12-11T16:28:39.242074494" }, "homo_sapiens - paired_end - arriba - stub": { "content": [ @@ -424,7 +424,7 @@ ] ], "3": [ - "versions.yml:md5,a149bba1dbb5194560abdd813c7848e3" + "versions.yml:md5,b416145d7b5b8a080e832a7f7cde4c44" ], "4": [ [ @@ -634,7 +634,7 @@ ] ], "versions": [ - "versions.yml:md5,a149bba1dbb5194560abdd813c7848e3" + "versions.yml:md5,b416145d7b5b8a080e832a7f7cde4c44" ], "wig": [ [ @@ -648,10 +648,10 @@ } ], "meta": { - "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nf-test": "0.9.2", + "nextflow": "24.10.2" }, - "timestamp": "2024-10-20T17:09:36.122131869" + "timestamp": "2024-12-11T16:29:08.392620556" }, "homo_sapiens - single_end": { "content": [ @@ -703,14 +703,14 @@ ], [ - "versions.yml:md5,a149bba1dbb5194560abdd813c7848e3" + "versions.yml:md5,b416145d7b5b8a080e832a7f7cde4c44" ] ], "meta": { - "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nf-test": "0.9.2", + "nextflow": "24.10.2" }, - "timestamp": "2024-10-20T17:01:22.197991909" + "timestamp": "2024-12-11T16:21:34.549483887" }, "homo_sapiens - paired_end": { "content": [ @@ -762,14 +762,14 @@ ], [ - "versions.yml:md5,a149bba1dbb5194560abdd813c7848e3" + "versions.yml:md5,b416145d7b5b8a080e832a7f7cde4c44" ] ], "meta": { - "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nf-test": "0.9.2", + "nextflow": "24.10.2" }, - "timestamp": "2024-10-20T17:02:06.988663857" + "timestamp": "2024-12-11T16:22:17.011253146" }, "homo_sapiens - paired_end - multiple - stub": { "content": [ @@ -869,7 +869,7 @@ ] ], "3": [ - "versions.yml:md5,a149bba1dbb5194560abdd813c7848e3" + "versions.yml:md5,b416145d7b5b8a080e832a7f7cde4c44" ], "4": [ [ @@ -1079,7 +1079,7 @@ ] ], "versions": [ - "versions.yml:md5,a149bba1dbb5194560abdd813c7848e3" + "versions.yml:md5,b416145d7b5b8a080e832a7f7cde4c44" ], "wig": [ [ @@ -1093,10 +1093,10 @@ } ], "meta": { - "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nf-test": "0.9.2", + "nextflow": "24.10.2" }, - "timestamp": "2024-10-20T17:10:12.005468781" + "timestamp": "2024-12-11T16:29:31.072104862" }, "homo_sapiens - paired_end - multiple": { "content": [ @@ -1148,14 +1148,14 @@ ], [ - "versions.yml:md5,a149bba1dbb5194560abdd813c7848e3" + "versions.yml:md5,b416145d7b5b8a080e832a7f7cde4c44" ] ], "meta": { - "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nf-test": "0.9.2", + "nextflow": "24.10.2" }, - "timestamp": "2024-10-20T17:08:54.877286681" + "timestamp": "2024-12-11T16:28:26.645008336" }, "homo_sapiens - paired_end - stub": { "content": [ @@ -1255,7 +1255,7 @@ ] ], "3": [ - "versions.yml:md5,a149bba1dbb5194560abdd813c7848e3" + "versions.yml:md5,b416145d7b5b8a080e832a7f7cde4c44" ], "4": [ [ @@ -1465,7 +1465,7 @@ ] ], "versions": [ - "versions.yml:md5,a149bba1dbb5194560abdd813c7848e3" + "versions.yml:md5,b416145d7b5b8a080e832a7f7cde4c44" ], "wig": [ [ @@ -1479,10 +1479,10 @@ } ], "meta": { - "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nf-test": "0.9.2", + "nextflow": "24.10.2" }, - "timestamp": "2024-10-20T17:09:20.911466345" + "timestamp": "2024-12-11T16:28:53.816280805" }, "homo_sapiens - paired_end - starfusion": { "content": [ @@ -1525,14 +1525,14 @@ ], [ - "versions.yml:md5,a149bba1dbb5194560abdd813c7848e3" + "versions.yml:md5,b416145d7b5b8a080e832a7f7cde4c44" ] ], "meta": { - "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nf-test": "0.9.2", + "nextflow": "24.10.2" }, - "timestamp": "2024-10-20T17:07:25.0639914" + "timestamp": "2024-12-11T16:27:14.136111964" }, "homo_sapiens - paired_end - arriba": { "content": [ @@ -1574,14 +1574,14 @@ ], [ - "versions.yml:md5,a149bba1dbb5194560abdd813c7848e3" + "versions.yml:md5,b416145d7b5b8a080e832a7f7cde4c44" ] ], "meta": { - "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nf-test": "0.9.2", + "nextflow": "24.10.2" }, - "timestamp": "2024-10-20T17:04:00.685784211" + "timestamp": "2024-12-11T16:24:00.829462526" }, "homo_sapiens - paired_end - starfusion - stub": { "content": [ @@ -1681,7 +1681,7 @@ ] ], "3": [ - "versions.yml:md5,a149bba1dbb5194560abdd813c7848e3" + "versions.yml:md5,b416145d7b5b8a080e832a7f7cde4c44" ], "4": [ [ @@ -1891,7 +1891,7 @@ ] ], "versions": [ - "versions.yml:md5,a149bba1dbb5194560abdd813c7848e3" + "versions.yml:md5,b416145d7b5b8a080e832a7f7cde4c44" ], "wig": [ [ @@ -1905,9 +1905,9 @@ } ], "meta": { - "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nf-test": "0.9.2", + "nextflow": "24.10.2" }, - "timestamp": "2024-10-20T17:09:53.173671551" + "timestamp": "2024-12-11T16:29:20.015372487" } } \ No newline at end of file diff --git a/modules/nf-core/star/genomegenerate/environment.yml b/modules/nf-core/star/genomegenerate/environment.yml index 7c57530a3..9ed1940fa 100644 --- a/modules/nf-core/star/genomegenerate/environment.yml +++ b/modules/nf-core/star/genomegenerate/environment.yml @@ -3,7 +3,7 @@ channels: - bioconda dependencies: - - bioconda::htslib=1.20 - - bioconda::samtools=1.20 + - bioconda::htslib=1.21 + - bioconda::samtools=1.21 - bioconda::star=2.7.11b - conda-forge::gawk=5.1.0 diff --git a/modules/nf-core/star/genomegenerate/main.nf b/modules/nf-core/star/genomegenerate/main.nf index af72f920b..4402ff963 100644 --- a/modules/nf-core/star/genomegenerate/main.nf +++ b/modules/nf-core/star/genomegenerate/main.nf @@ -3,7 +3,7 @@ process STAR_GENOMEGENERATE { label 'process_high' conda "${moduleDir}/environment.yml" - container 'nf-core/star_samtools_htslib_gawk:10c6e8c834460019' + container 'nf-core/htslib_samtools_star_gawk:311d422a50e6d829' input: tuple val(meta), path(fasta) diff --git a/modules/nf-core/star/genomegenerate/star-genomegenerate.diff b/modules/nf-core/star/genomegenerate/star-genomegenerate.diff index dea48619b..85f11ae52 100644 --- a/modules/nf-core/star/genomegenerate/star-genomegenerate.diff +++ b/modules/nf-core/star/genomegenerate/star-genomegenerate.diff @@ -1,4 +1,6 @@ Changes in module 'nf-core/star/genomegenerate' +'modules/nf-core/star/genomegenerate/environment.yml' is unchanged +'modules/nf-core/star/genomegenerate/meta.yml' is unchanged Changes in 'star/genomegenerate/main.nf': --- modules/nf-core/star/genomegenerate/main.nf +++ modules/nf-core/star/genomegenerate/main.nf @@ -7,16 +9,15 @@ Changes in 'star/genomegenerate/main.nf': conda "${moduleDir}/environment.yml" - container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container ? -- 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/b4/b425bc2a95806d878993f9a66dae3ae80ac2dafff4c208c5ae01b7a90a32fa91/data' : -- 'community.wave.seqera.io/library/star_samtools_htslib_gawk:10c6e8c834460019' }" -+ container 'nf-core/star_samtools_htslib_gawk:10c6e8c834460019' +- 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/10/101ea47973178f85ff66a34de6a7462aaf99d947d3924c27ce8a2d5a63009065/data' : +- 'community.wave.seqera.io/library/htslib_samtools_star_gawk:311d422a50e6d829' }" ++ container 'nf-core/htslib_samtools_star_gawk:311d422a50e6d829' input: tuple val(meta), path(fasta) -'modules/nf-core/star/genomegenerate/meta.yml' is unchanged -'modules/nf-core/star/genomegenerate/environment.yml' is unchanged 'modules/nf-core/star/genomegenerate/tests/main.nf.test.snap' is unchanged +'modules/nf-core/star/genomegenerate/tests/tags.yml' is unchanged Changes in 'star/genomegenerate/tests/main.nf.test': --- modules/nf-core/star/genomegenerate/tests/main.nf.test +++ modules/nf-core/star/genomegenerate/tests/main.nf.test @@ -32,5 +33,4 @@ Changes in 'star/genomegenerate/tests/main.nf.test': test("fasta_gtf") { -'modules/nf-core/star/genomegenerate/tests/tags.yml' was removed ************************************************************ diff --git a/modules/nf-core/star/genomegenerate/tests/main.nf.test.snap b/modules/nf-core/star/genomegenerate/tests/main.nf.test.snap index 3db256782..b79da991d 100644 --- a/modules/nf-core/star/genomegenerate/tests/main.nf.test.snap +++ b/modules/nf-core/star/genomegenerate/tests/main.nf.test.snap @@ -3,14 +3,14 @@ "content": [ "[Genome, Log.out, SA, SAindex, chrLength.txt, chrName.txt, chrNameLength.txt, chrStart.txt, exonGeTrInfo.tab, exonInfo.tab, geneInfo.tab, genomeParameters.txt, sjdbInfo.txt, sjdbList.fromGTF.out.tab, sjdbList.out.tab, transcriptInfo.tab]", [ - "versions.yml:md5,14b05d04c9eca568e9ed4888aaf26fa6" + "versions.yml:md5,0c6c5fab50cc8601f9072268a2f49aad" ] ], "meta": { - "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nf-test": "0.9.2", + "nextflow": "24.10.2" }, - "timestamp": "2024-10-19T20:37:47.410432728" + "timestamp": "2024-12-11T16:29:40.748676908" }, "fasta_gtf_stub": { "content": [ @@ -41,7 +41,7 @@ ] ], "1": [ - "versions.yml:md5,14b05d04c9eca568e9ed4888aaf26fa6" + "versions.yml:md5,0c6c5fab50cc8601f9072268a2f49aad" ], "index": [ [ @@ -69,15 +69,15 @@ ] ], "versions": [ - "versions.yml:md5,14b05d04c9eca568e9ed4888aaf26fa6" + "versions.yml:md5,0c6c5fab50cc8601f9072268a2f49aad" ] } ], "meta": { - "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nf-test": "0.9.2", + "nextflow": "24.10.2" }, - "timestamp": "2024-10-19T20:38:09.165234795" + "timestamp": "2024-12-11T16:30:11.024076742" }, "fasta_stub": { "content": [ @@ -101,7 +101,7 @@ ] ], "1": [ - "versions.yml:md5,14b05d04c9eca568e9ed4888aaf26fa6" + "versions.yml:md5,0c6c5fab50cc8601f9072268a2f49aad" ], "index": [ [ @@ -122,27 +122,27 @@ ] ], "versions": [ - "versions.yml:md5,14b05d04c9eca568e9ed4888aaf26fa6" + "versions.yml:md5,0c6c5fab50cc8601f9072268a2f49aad" ] } ], "meta": { - "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nf-test": "0.9.2", + "nextflow": "24.10.2" }, - "timestamp": "2024-10-19T20:38:19.530862664" + "timestamp": "2024-12-11T16:30:22.674364031" }, "fasta": { "content": [ "[Genome, Log.out, SA, SAindex, chrLength.txt, chrName.txt, chrNameLength.txt, chrStart.txt, genomeParameters.txt]", [ - "versions.yml:md5,14b05d04c9eca568e9ed4888aaf26fa6" + "versions.yml:md5,0c6c5fab50cc8601f9072268a2f49aad" ] ], "meta": { - "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nf-test": "0.9.2", + "nextflow": "24.10.2" }, - "timestamp": "2024-10-19T20:37:58.667436398" + "timestamp": "2024-12-11T16:29:57.958812354" } } \ No newline at end of file From b0c583aab6fe6808fb29b21b674e597f20eb71fa Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Wed, 18 Dec 2024 18:22:10 +0000 Subject: [PATCH 086/107] Update CHANGELOG.md --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 050166e35..c27d1b8d8 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -23,6 +23,7 @@ Special thanks to the following for their contributions to the release: - [PR #1469](https://github.com/nf-core/rnaseq/pull/1469) - Minor docs fix - [PR #1459](https://github.com/nf-core/rnaseq/pull/1466) - Remove reference to unused "skip_sample_count" value in email templates - [PR #1471](https://github.com/nf-core/rnaseq/pull/1471) - Fix prepare_genome subworkflow for sortmerna +- [PR #1473](https://github.com/nf-core/rnaseq/pull/1473) - Bump STAR modules ### Software dependencies From 7955857162c250f2e506bd742bf3ac52f6bbc047 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Thu, 19 Dec 2024 09:27:45 +0000 Subject: [PATCH 087/107] update samtools version --- tests/default.nf.test.snap | 6 +++--- tests/featurecounts_group_type.nf.test.snap | 6 +++--- tests/min_mapped_reads.nf.test.snap | 6 +++--- tests/remove_ribo_rna.nf.test.snap | 6 +++--- tests/skip_qc.nf.test.snap | 6 +++--- tests/skip_trimming.nf.test.snap | 4 ++-- tests/umi.nf.test.snap | 6 +++--- 7 files changed, 20 insertions(+), 20 deletions(-) diff --git a/tests/default.nf.test.snap b/tests/default.nf.test.snap index 0fde0cc42..083846538 100644 --- a/tests/default.nf.test.snap +++ b/tests/default.nf.test.snap @@ -35,7 +35,7 @@ }, "STAR_GENOMEGENERATE": { "star": "2.7.11b", - "samtools": 1.2, + "samtools": 1.21, "gawk": "5.1.0" }, "TRIMGALORE": { @@ -209,12 +209,12 @@ }, "STAR_ALIGN": { "star": "2.7.11b", - "samtools": 1.2, + "samtools": 1.21, "gawk": "5.1.0" }, "STAR_GENOMEGENERATE": { "star": "2.7.11b", - "samtools": 1.2, + "samtools": 1.21, "gawk": "5.1.0" }, "STRINGTIE_STRINGTIE": { diff --git a/tests/featurecounts_group_type.nf.test.snap b/tests/featurecounts_group_type.nf.test.snap index c1d285eb5..e2fb3b4bf 100644 --- a/tests/featurecounts_group_type.nf.test.snap +++ b/tests/featurecounts_group_type.nf.test.snap @@ -35,7 +35,7 @@ }, "STAR_GENOMEGENERATE": { "star": "2.7.11b", - "samtools": 1.2, + "samtools": 1.21, "gawk": "5.1.0" }, "TRIMGALORE": { @@ -206,12 +206,12 @@ }, "STAR_ALIGN": { "star": "2.7.11b", - "samtools": 1.2, + "samtools": 1.21, "gawk": "5.1.0" }, "STAR_GENOMEGENERATE": { "star": "2.7.11b", - "samtools": 1.2, + "samtools": 1.21, "gawk": "5.1.0" }, "STRINGTIE_STRINGTIE": { diff --git a/tests/min_mapped_reads.nf.test.snap b/tests/min_mapped_reads.nf.test.snap index e9a85eb85..1b0b0c03f 100644 --- a/tests/min_mapped_reads.nf.test.snap +++ b/tests/min_mapped_reads.nf.test.snap @@ -106,12 +106,12 @@ }, "STAR_ALIGN": { "star": "2.7.11b", - "samtools": 1.2, + "samtools": 1.21, "gawk": "5.1.0" }, "STAR_GENOMEGENERATE": { "star": "2.7.11b", - "samtools": 1.2, + "samtools": 1.21, "gawk": "5.1.0" }, "STRINGTIE_STRINGTIE": { @@ -1235,7 +1235,7 @@ }, "STAR_GENOMEGENERATE": { "star": "2.7.11b", - "samtools": 1.2, + "samtools": 1.21, "gawk": "5.1.0" }, "TRIMGALORE": { diff --git a/tests/remove_ribo_rna.nf.test.snap b/tests/remove_ribo_rna.nf.test.snap index 86a609166..0de885b42 100644 --- a/tests/remove_ribo_rna.nf.test.snap +++ b/tests/remove_ribo_rna.nf.test.snap @@ -112,12 +112,12 @@ }, "STAR_ALIGN": { "star": "2.7.11b", - "samtools": 1.2, + "samtools": 1.21, "gawk": "5.1.0" }, "STAR_GENOMEGENERATE": { "star": "2.7.11b", - "samtools": 1.2, + "samtools": 1.21, "gawk": "5.1.0" }, "STRINGTIE_STRINGTIE": { @@ -1449,7 +1449,7 @@ }, "STAR_GENOMEGENERATE": { "star": "2.7.11b", - "samtools": 1.2, + "samtools": 1.21, "gawk": "5.1.0" }, "TRIMGALORE": { diff --git a/tests/skip_qc.nf.test.snap b/tests/skip_qc.nf.test.snap index ca881d057..c93205278 100644 --- a/tests/skip_qc.nf.test.snap +++ b/tests/skip_qc.nf.test.snap @@ -32,7 +32,7 @@ }, "STAR_GENOMEGENERATE": { "star": "2.7.11b", - "samtools": 1.2, + "samtools": 1.21, "gawk": "5.1.0" }, "TRIMGALORE": { @@ -154,12 +154,12 @@ }, "STAR_ALIGN": { "star": "2.7.11b", - "samtools": 1.2, + "samtools": 1.21, "gawk": "5.1.0" }, "STAR_GENOMEGENERATE": { "star": "2.7.11b", - "samtools": 1.2, + "samtools": 1.21, "gawk": "5.1.0" }, "STRINGTIE_STRINGTIE": { diff --git a/tests/skip_trimming.nf.test.snap b/tests/skip_trimming.nf.test.snap index 35c14b87d..4f89bd3f0 100644 --- a/tests/skip_trimming.nf.test.snap +++ b/tests/skip_trimming.nf.test.snap @@ -106,12 +106,12 @@ }, "STAR_ALIGN": { "star": "2.7.11b", - "samtools": 1.2, + "samtools": 1.21, "gawk": "5.1.0" }, "STAR_GENOMEGENERATE": { "star": "2.7.11b", - "samtools": 1.2, + "samtools": 1.21, "gawk": "5.1.0" }, "STRINGTIE_STRINGTIE": { diff --git a/tests/umi.nf.test.snap b/tests/umi.nf.test.snap index fd8700918..5c9f34cb0 100644 --- a/tests/umi.nf.test.snap +++ b/tests/umi.nf.test.snap @@ -100,12 +100,12 @@ }, "STAR_ALIGN": { "star": "2.7.11b", - "samtools": 1.2, + "samtools": 1.21, "gawk": "5.1.0" }, "STAR_GENOMEGENERATE": { "star": "2.7.11b", - "samtools": 1.2, + "samtools": 1.21, "gawk": "5.1.0" }, "STRINGTIE_STRINGTIE": { @@ -2733,7 +2733,7 @@ }, "STAR_GENOMEGENERATE": { "star": "2.7.11b", - "samtools": 1.2, + "samtools": 1.21, "gawk": "5.1.0" }, "TRIMGALORE": { From d11b9af423958264d33011a7ce66b9fc059d7fbe Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Thu, 19 Dec 2024 09:52:21 +0000 Subject: [PATCH 088/107] Fix swf snaps --- .../local/align_star/tests/main.nf.test.snap | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/subworkflows/local/align_star/tests/main.nf.test.snap b/subworkflows/local/align_star/tests/main.nf.test.snap index b8224b7fd..992e9b231 100644 --- a/subworkflows/local/align_star/tests/main.nf.test.snap +++ b/subworkflows/local/align_star/tests/main.nf.test.snap @@ -230,8 +230,8 @@ ] ], [ + "versions.yml:md5,57e0ecbc0fd3d1f5cfc9d9ed285826e8", "versions.yml:md5,5ad5e88ee4e52ccabeef61dcd368d30b", - "versions.yml:md5,708098dd31c984730b066ead621a33f7", "versions.yml:md5,92f25606f0ee994dac8c00fe49676e5a", "versions.yml:md5,9611f602102b5aed0fe2b947f9d2270c", "versions.yml:md5,b92d6ad60847882b0089f551c5cfc366", @@ -239,10 +239,10 @@ ] ], "meta": { - "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nf-test": "0.9.2", + "nextflow": "24.10.3" }, - "timestamp": "2024-10-21T20:42:29.835710637" + "timestamp": "2024-12-19T09:49:19.874380799" }, "star - no igenomes": { "content": [ @@ -333,8 +333,8 @@ ] ], [ + "versions.yml:md5,57e0ecbc0fd3d1f5cfc9d9ed285826e8", "versions.yml:md5,5ad5e88ee4e52ccabeef61dcd368d30b", - "versions.yml:md5,708098dd31c984730b066ead621a33f7", "versions.yml:md5,92f25606f0ee994dac8c00fe49676e5a", "versions.yml:md5,9611f602102b5aed0fe2b947f9d2270c", "versions.yml:md5,b92d6ad60847882b0089f551c5cfc366", @@ -342,10 +342,10 @@ ] ], "meta": { - "nf-test": "0.9.0", - "nextflow": "24.04.4" + "nf-test": "0.9.2", + "nextflow": "24.10.3" }, - "timestamp": "2024-10-21T20:40:55.299198285" + "timestamp": "2024-12-19T09:47:39.98853048" }, "star - with igenomes - stub": { "content": [ From 859cfc377544998867abc908a20f2a4797d395a7 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Thu, 19 Dec 2024 10:59:41 +0000 Subject: [PATCH 089/107] Bump versions to 3.18.0 --- nextflow.config | 2 +- tests/default.nf.test.snap | 4 +-- tests/featurecounts_group_type.nf.test.snap | 4 +-- tests/hisat2.nf.test.snap | 4 +-- tests/kallisto.nf.test.snap | 4 +-- tests/min_mapped_reads.nf.test.snap | 4 +-- tests/remove_ribo_rna.nf.test.snap | 4 +-- tests/salmon.nf.test.snap | 4 +-- tests/skip_qc.nf.test.snap | 4 +-- tests/skip_trimming.nf.test.snap | 2 +- tests/star_rsem.nf.test.snap | 4 +-- tests/umi.nf.test.snap | 6 ++-- .../rnaseq/assets/multiqc/multiqc_config.yml | 31 ++++++++++++++----- 13 files changed, 47 insertions(+), 30 deletions(-) diff --git a/nextflow.config b/nextflow.config index bc528efed..aacf937c3 100644 --- a/nextflow.config +++ b/nextflow.config @@ -333,7 +333,7 @@ manifest { description = """RNA sequencing analysis pipeline for gene/isoform quantification and extensive quality control.""" mainScript = 'main.nf' nextflowVersion = '!>=24.04.2' - version = '3.18.0dev' + version = '3.18.0' doi = 'https://doi.org/10.5281/zenodo.1400710' } diff --git a/tests/default.nf.test.snap b/tests/default.nf.test.snap index 083846538..8fdef2729 100644 --- a/tests/default.nf.test.snap +++ b/tests/default.nf.test.snap @@ -46,7 +46,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0dev" + "nf-core/rnaseq": "v3.18.0" } }, [ @@ -240,7 +240,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0dev" + "nf-core/rnaseq": "v3.18.0" } }, [ diff --git a/tests/featurecounts_group_type.nf.test.snap b/tests/featurecounts_group_type.nf.test.snap index e2fb3b4bf..96f7b3778 100644 --- a/tests/featurecounts_group_type.nf.test.snap +++ b/tests/featurecounts_group_type.nf.test.snap @@ -46,7 +46,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0dev" + "nf-core/rnaseq": "v3.18.0" } }, [ @@ -234,7 +234,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0dev" + "nf-core/rnaseq": "v3.18.0" } }, [ diff --git a/tests/hisat2.nf.test.snap b/tests/hisat2.nf.test.snap index 3c943fb61..e765de22a 100644 --- a/tests/hisat2.nf.test.snap +++ b/tests/hisat2.nf.test.snap @@ -47,7 +47,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0dev" + "nf-core/rnaseq": "v3.18.0" } }, [ @@ -237,7 +237,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0dev" + "nf-core/rnaseq": "v3.18.0" } }, [ diff --git a/tests/kallisto.nf.test.snap b/tests/kallisto.nf.test.snap index d3a458f5d..192ddaedc 100644 --- a/tests/kallisto.nf.test.snap +++ b/tests/kallisto.nf.test.snap @@ -59,7 +59,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0dev" + "nf-core/rnaseq": "v3.18.0" } }, [ @@ -319,7 +319,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0dev" + "nf-core/rnaseq": "v3.18.0" } }, [ diff --git a/tests/min_mapped_reads.nf.test.snap b/tests/min_mapped_reads.nf.test.snap index 1b0b0c03f..7c0412a2e 100644 --- a/tests/min_mapped_reads.nf.test.snap +++ b/tests/min_mapped_reads.nf.test.snap @@ -137,7 +137,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0dev" + "nf-core/rnaseq": "v3.18.0" } }, [ @@ -1246,7 +1246,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0dev" + "nf-core/rnaseq": "v3.18.0" } }, [ diff --git a/tests/remove_ribo_rna.nf.test.snap b/tests/remove_ribo_rna.nf.test.snap index 0de885b42..6abd45320 100644 --- a/tests/remove_ribo_rna.nf.test.snap +++ b/tests/remove_ribo_rna.nf.test.snap @@ -143,7 +143,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0dev" + "nf-core/rnaseq": "v3.18.0" } }, [ @@ -1460,7 +1460,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0dev" + "nf-core/rnaseq": "v3.18.0" } }, [ diff --git a/tests/salmon.nf.test.snap b/tests/salmon.nf.test.snap index fd9043460..87a9a3e14 100644 --- a/tests/salmon.nf.test.snap +++ b/tests/salmon.nf.test.snap @@ -53,7 +53,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0dev" + "nf-core/rnaseq": "v3.18.0" } }, [ @@ -392,7 +392,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0dev" + "nf-core/rnaseq": "v3.18.0" } }, [ diff --git a/tests/skip_qc.nf.test.snap b/tests/skip_qc.nf.test.snap index c93205278..0c6fd0e1c 100644 --- a/tests/skip_qc.nf.test.snap +++ b/tests/skip_qc.nf.test.snap @@ -43,7 +43,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0dev" + "nf-core/rnaseq": "v3.18.0" } }, [ @@ -182,7 +182,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0dev" + "nf-core/rnaseq": "v3.18.0" } }, [ diff --git a/tests/skip_trimming.nf.test.snap b/tests/skip_trimming.nf.test.snap index 4f89bd3f0..7a9edbf1b 100644 --- a/tests/skip_trimming.nf.test.snap +++ b/tests/skip_trimming.nf.test.snap @@ -133,7 +133,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0dev" + "nf-core/rnaseq": "v3.18.0" } }, [ diff --git a/tests/star_rsem.nf.test.snap b/tests/star_rsem.nf.test.snap index 2b1b7eca7..ab6ed8d6c 100644 --- a/tests/star_rsem.nf.test.snap +++ b/tests/star_rsem.nf.test.snap @@ -137,7 +137,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0dev" + "nf-core/rnaseq": "v3.18.0" } }, [ @@ -1339,7 +1339,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0dev" + "nf-core/rnaseq": "v3.18.0" } }, [ diff --git a/tests/umi.nf.test.snap b/tests/umi.nf.test.snap index 5c9f34cb0..4e153a33b 100644 --- a/tests/umi.nf.test.snap +++ b/tests/umi.nf.test.snap @@ -140,7 +140,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0dev" + "nf-core/rnaseq": "v3.18.0" } }, [ @@ -1610,7 +1610,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0dev" + "nf-core/rnaseq": "v3.18.0" } }, [ @@ -2747,7 +2747,7 @@ "untar": 1.34 }, "Workflow": { - "nf-core/rnaseq": "v3.18.0dev" + "nf-core/rnaseq": "v3.18.0" } }, [ diff --git a/workflows/rnaseq/assets/multiqc/multiqc_config.yml b/workflows/rnaseq/assets/multiqc/multiqc_config.yml index 9b8ee731a..4af07dc67 100644 --- a/workflows/rnaseq/assets/multiqc/multiqc_config.yml +++ b/workflows/rnaseq/assets/multiqc/multiqc_config.yml @@ -1,5 +1,8 @@ report_comment: > - This report has been generated by the nf-core/rnaseq analysis pipeline. For information about how to interpret these results, please see the documentation. + This report has been generated by the nf-core/rnaseq analysis pipeline. For information about how + to interpret these results, please see the documentation. report_section_order: # Important checks and failures sample-status: @@ -117,9 +120,9 @@ module_order: # Don't show % Dups in the General Stats table (we have this from Picard) table_columns_visible: "FastQC (raw)": - percent_duplicates: False + percent_duplicates: false "FastQC (trimmed)": - percent_duplicates: False + percent_duplicates: false extra_fn_clean_exts: - ".umi_dedup" @@ -216,7 +219,9 @@ sp: custom_data: fail_trimmed_samples: section_name: "WARNING: Fail Trimming Check" - description: "List of samples that failed the minimum trimmed reads threshold specified via the '--min_trimmed_reads' parameter, and hence were ignored for the downstream processing steps." + description: "List of samples that failed the minimum trimmed reads threshold + specified via the '--min_trimmed_reads' parameter, and hence were ignored for + the downstream processing steps." plot_type: "table" pconfig: id: "fail_trimmed_samples_table" @@ -224,7 +229,9 @@ custom_data: namespace: "Samples failed trimming threshold" fail_mapped_samples: section_name: "WARNING: Fail Alignment Check" - description: "List of samples that failed the STAR minimum mapped reads threshold specified via the '--min_mapped_reads' parameter, and hence were ignored for the downstream processing steps." + description: "List of samples that failed the STAR minimum mapped reads threshold + specified via the '--min_mapped_reads' parameter, and hence were ignored for + the downstream processing steps." plot_type: "table" pconfig: id: "fail_mapped_samples_table" @@ -232,7 +239,16 @@ custom_data: namespace: "Samples failed mapping threshold" fail_strand_check: section_name: "Strandedness Checks" - description: "

    The strandedness used for analysis in this workflow can either be provided by the user or automatically inferred by Salmon using a sample of reads. In both cases, strandedness is verified at the end of the workflow using RSeQC's infer_experiment.py on genomic alignments. In this table, a pass indicates a match between the supplied strandedness (or that inferred by Salmon) and RSeQC results. A fail indicates a mismatch or 'undetermined' strandedness. 'Undetermined' strandedness can signal QC issues, including potential genomic DNA contamination.

    Note: Rows are duplicated for an 'auto' setting to allow comparison of statistics between inference methods.

    " + description: "

    The strandedness used for analysis in this workflow can either + be provided by the user or automatically inferred by Salmon using a sample + of reads. In both cases, strandedness is verified at the end of the workflow + using RSeQC's infer_experiment.py + on genomic alignments. In this table, a pass indicates a match between the supplied + strandedness (or that inferred by Salmon) and RSeQC results. A fail indicates + a mismatch or 'undetermined' strandedness. 'Undetermined' strandedness can signal + QC issues, including potential genomic DNA contamination.

    Note: + Rows are duplicated for an 'auto' setting to allow comparison of statistics + between inference methods.

    " plot_type: "table" pconfig: id: "fail_strand_check_table" @@ -240,7 +256,8 @@ custom_data: namespace: "Samples strandedness check" headers: Status: - description: Pass if the strandedness inferred by RSeQC matches that provided by the user or predicted by Salmon. Fail otherwise. + description: Pass if the strandedness inferred by RSeQC matches that provided + by the user or predicted by Salmon. Fail otherwise. Strand inference method: description: Tool used to detect strandedness Provided strandedness: From f41744ea90baf7173be0727959e94940747d9d88 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Thu, 19 Dec 2024 11:39:46 +0000 Subject: [PATCH 090/107] Update CHANGELOG.md --- CHANGELOG.md | 23 ++++++++++++++--------- 1 file changed, 14 insertions(+), 9 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index c27d1b8d8..9d593d2e7 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -3,13 +3,15 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). -# 3.18.0dev - xxxx-xx-xx +# 3.18.0 - 2024-12-19 ### Credits Special thanks to the following for their contributions to the release: - [Caitlin Winkler](https://github.com/oligomyeggo) +- [Jonathan Manning](https://github.com/pinin4fjords) +- [Maxime Garcia](https://github.com/maxulysse) - [Siddhartha Bagaria](https://github.com/siddharthab) ### Enhancements & fixes @@ -25,6 +27,12 @@ Special thanks to the following for their contributions to the release: - [PR #1471](https://github.com/nf-core/rnaseq/pull/1471) - Fix prepare_genome subworkflow for sortmerna - [PR #1473](https://github.com/nf-core/rnaseq/pull/1473) - Bump STAR modules +### Parameters + +| Old parameter | New parameter | +| ------------------------------------ | ---------------- | +| | `--skip_linting` | + ### Software dependencies | Dependency | Old version | New version | @@ -114,14 +122,11 @@ Special thanks to the following for their contributions to the release: ### Parameters -| Old parameter | New parameter | -| ------------------------------------ | --------------- | -| | `--help_full` | -| | `--show_hidden` | -| `--validationFailUnrecognisedParams` | | -| `--validationLenientMode` | | -| `--validationSchemaIgnoreParams` | | -| `--validationShowHiddenParams` | | +| Old parameter | New parameter | +| ------------- | --------------------- | +| | `--skip_linting` | +| | `--extra_fqlint_args` | +| | `--umi_dedup_tool` | > **NB:** Parameter has been **updated** if both old and new parameter information is present. > From 492efe9f3e19614136afc88c6e18c9420af997cd Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Thu, 19 Dec 2024 11:42:29 +0000 Subject: [PATCH 091/107] Revert "Update CHANGELOG.md" This reverts commit f41744ea90baf7173be0727959e94940747d9d88. --- CHANGELOG.md | 23 +++++++++-------------- 1 file changed, 9 insertions(+), 14 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 9d593d2e7..c27d1b8d8 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -3,15 +3,13 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). -# 3.18.0 - 2024-12-19 +# 3.18.0dev - xxxx-xx-xx ### Credits Special thanks to the following for their contributions to the release: - [Caitlin Winkler](https://github.com/oligomyeggo) -- [Jonathan Manning](https://github.com/pinin4fjords) -- [Maxime Garcia](https://github.com/maxulysse) - [Siddhartha Bagaria](https://github.com/siddharthab) ### Enhancements & fixes @@ -27,12 +25,6 @@ Special thanks to the following for their contributions to the release: - [PR #1471](https://github.com/nf-core/rnaseq/pull/1471) - Fix prepare_genome subworkflow for sortmerna - [PR #1473](https://github.com/nf-core/rnaseq/pull/1473) - Bump STAR modules -### Parameters - -| Old parameter | New parameter | -| ------------------------------------ | ---------------- | -| | `--skip_linting` | - ### Software dependencies | Dependency | Old version | New version | @@ -122,11 +114,14 @@ Special thanks to the following for their contributions to the release: ### Parameters -| Old parameter | New parameter | -| ------------- | --------------------- | -| | `--skip_linting` | -| | `--extra_fqlint_args` | -| | `--umi_dedup_tool` | +| Old parameter | New parameter | +| ------------------------------------ | --------------- | +| | `--help_full` | +| | `--show_hidden` | +| `--validationFailUnrecognisedParams` | | +| `--validationLenientMode` | | +| `--validationSchemaIgnoreParams` | | +| `--validationShowHiddenParams` | | > **NB:** Parameter has been **updated** if both old and new parameter information is present. > From 0cf9100ab62941ccb851db83ed9a6b9aa625f331 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Thu, 19 Dec 2024 11:44:43 +0000 Subject: [PATCH 092/107] Update changelog --- CHANGELOG.md | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index c27d1b8d8..cb3b78e7a 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -3,13 +3,15 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). -# 3.18.0dev - xxxx-xx-xx +# 3.18.0 - 2024-12-19 ### Credits Special thanks to the following for their contributions to the release: - [Caitlin Winkler](https://github.com/oligomyeggo) +- [Jonathan Manning](https://github.com/pinin4fjords) +- [Maxime Garcia](https://github.com/maxulysse) - [Siddhartha Bagaria](https://github.com/siddharthab) ### Enhancements & fixes @@ -24,6 +26,15 @@ Special thanks to the following for their contributions to the release: - [PR #1459](https://github.com/nf-core/rnaseq/pull/1466) - Remove reference to unused "skip_sample_count" value in email templates - [PR #1471](https://github.com/nf-core/rnaseq/pull/1471) - Fix prepare_genome subworkflow for sortmerna - [PR #1473](https://github.com/nf-core/rnaseq/pull/1473) - Bump STAR modules +- [PR #1474](https://github.com/nf-core/rnaseq/pull/1474) - Bump versions to 3.18.0 + +## Parameters + +| Old parameter | New parameter | +| ------------- | --------------------- | +| | `--skip_linting` | +| | `--extra_fqlint_args` | +| | `--umi_dedup_tool` | ### Software dependencies From f67ae86288c7bb8dd1c50153f97d98a6307d2af0 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Thu, 19 Dec 2024 12:53:02 +0000 Subject: [PATCH 093/107] Fix minor umi dedup log issue --- workflows/rnaseq/nextflow.config | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/workflows/rnaseq/nextflow.config b/workflows/rnaseq/nextflow.config index e7a6290fa..ed033088a 100644 --- a/workflows/rnaseq/nextflow.config +++ b/workflows/rnaseq/nextflow.config @@ -140,12 +140,7 @@ if (!params.skip_alignment && params.aligner == 'star_salmon') { [ path: { "${params.outdir}/${params.aligner}/umitools" }, mode: params.publish_dir_mode, - pattern: '*.tsv' - ], - [ - path: { "${params.outdir}/${params.aligner}/umitools/transcriptomic_dedup_log" }, - mode: params.publish_dir_mode, - pattern: '*.log' + pattern: '*.{log,tsv}' ], [ path: { params.save_align_intermeds || params.save_umi_intermeds ? "${params.outdir}/${params.aligner}" : params.outdir }, From 37b52b5b783e849497daa9728ba9b073b6fe99d0 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Thu, 19 Dec 2024 13:14:28 +0000 Subject: [PATCH 094/107] better log file fix --- conf/test.config | 4 +++ workflows/rnaseq/nextflow.config | 57 ++++++++++++++++++++++++++------ 2 files changed, 50 insertions(+), 11 deletions(-) diff --git a/conf/test.config b/conf/test.config index 153200306..23331e60a 100644 --- a/conf/test.config +++ b/conf/test.config @@ -41,6 +41,10 @@ params { skip_bbsplit = false pseudo_aligner = 'salmon' umitools_bc_pattern = 'NNNN' + + remove_ribo_rna= true + sortmerna_index = '/Users/jonathan.manning/projects/rnaseq/idx' + skip_multiqc = true } // When using RSEM, remove warning from STAR whilst building tiny indices diff --git a/workflows/rnaseq/nextflow.config b/workflows/rnaseq/nextflow.config index ed033088a..88a8749d7 100644 --- a/workflows/rnaseq/nextflow.config +++ b/workflows/rnaseq/nextflow.config @@ -137,11 +137,6 @@ if (!params.skip_alignment && params.aligner == 'star_salmon') { withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMI(COLLAPSE|TOOLS)_TRANSCRIPTOME:UMI(COLLAPSE|TOOLS_DEDUP)' { ext.prefix = { "${meta.id}.umi_dedup.transcriptome.sorted" } publishDir = [ - [ - path: { "${params.outdir}/${params.aligner}/umitools" }, - mode: params.publish_dir_mode, - pattern: '*.{log,tsv}' - ], [ path: { params.save_align_intermeds || params.save_umi_intermeds ? "${params.outdir}/${params.aligner}" : params.outdir }, mode: params.publish_dir_mode, @@ -240,20 +235,60 @@ if (!params.skip_alignment) { ext.prefix = { "${meta.id}.umi_dedup.sorted" } publishDir = [ [ - path: { "${params.outdir}/${params.aligner}/umitools" }, + path: { params.save_align_intermeds || params.with_umi || params.save_umi_intermeds ? "${params.outdir}/${params.aligner}" : params.outdir }, mode: params.publish_dir_mode, - pattern: '*.tsv' - ], + pattern: '*.bam', + saveAs: { params.save_align_intermeds || params.with_umi || params.save_umi_intermeds ? it : null } + ] + ] + } + + withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_GENOME:UMICOLLAPSE' { + publishDir = [ + [ + path: { "${params.outdir}/${params.aligner}/umicollapse/genomic_dedup_log" }, + mode: params.publish_dir_mode, + pattern: '*.log' + ] + ] + } + + withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_TRANSCRIPTOME:UMICOLLAPSE' { + publishDir = [ + [ + path: { "${params.outdir}/${params.aligner}/umicollapse/transcriptomic_dedup_log" }, + mode: params.publish_dir_mode, + pattern: '*.log' + ] + ] + } + + withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME:UMITOOLS' { + publishDir = [ [ path: { "${params.outdir}/${params.aligner}/umitools/genomic_dedup_log" }, mode: params.publish_dir_mode, pattern: '*.log' + ] + ] + } + + withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_TRANSCRIPTOME:UMITOOLS' { + publishDir = [ + [ + path: { "${params.outdir}/${params.aligner}/umitools/transcriptomic_dedup_log" }, + mode: params.publish_dir_mode, + pattern: '*.log' ], + ] + } + + withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_(GEN_TRANSCRIP)TOME:UMITOOLS' { + publishDir = [ [ - path: { params.save_align_intermeds || params.with_umi || params.save_umi_intermeds ? "${params.outdir}/${params.aligner}" : params.outdir }, + path: { "${params.outdir}/${params.aligner}/umitools" }, mode: params.publish_dir_mode, - pattern: '*.bam', - saveAs: { params.save_align_intermeds || params.with_umi || params.save_umi_intermeds ? it : null } + pattern: '*.tsv' ] ] } From e8459aa16fd592bd9072dda0da6c2d49501ddfe5 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Thu, 19 Dec 2024 13:15:26 +0000 Subject: [PATCH 095/107] undo config change --- conf/test.config | 4 ---- 1 file changed, 4 deletions(-) diff --git a/conf/test.config b/conf/test.config index 23331e60a..153200306 100644 --- a/conf/test.config +++ b/conf/test.config @@ -41,10 +41,6 @@ params { skip_bbsplit = false pseudo_aligner = 'salmon' umitools_bc_pattern = 'NNNN' - - remove_ribo_rna= true - sortmerna_index = '/Users/jonathan.manning/projects/rnaseq/idx' - skip_multiqc = true } // When using RSEM, remove warning from STAR whilst building tiny indices From 413827037aff62c2e0ceac5502fb163df295baf9 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Thu, 19 Dec 2024 13:30:20 +0000 Subject: [PATCH 096/107] tiny fix --- workflows/rnaseq/nextflow.config | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/workflows/rnaseq/nextflow.config b/workflows/rnaseq/nextflow.config index 88a8749d7..cdbddc317 100644 --- a/workflows/rnaseq/nextflow.config +++ b/workflows/rnaseq/nextflow.config @@ -283,7 +283,7 @@ if (!params.skip_alignment) { ] } - withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_(GEN_TRANSCRIP)TOME:UMITOOLS' { + withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_(GEN_TRANSCRIPT)OME:UMITOOLS' { publishDir = [ [ path: { "${params.outdir}/${params.aligner}/umitools" }, From 16148b9a8aa6cf5251f6e20d5406ab09dad7b4cd Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Thu, 19 Dec 2024 13:31:26 +0000 Subject: [PATCH 097/107] tiny fix --- workflows/rnaseq/nextflow.config | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/workflows/rnaseq/nextflow.config b/workflows/rnaseq/nextflow.config index cdbddc317..c9229a45f 100644 --- a/workflows/rnaseq/nextflow.config +++ b/workflows/rnaseq/nextflow.config @@ -283,7 +283,7 @@ if (!params.skip_alignment) { ] } - withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_(GEN_TRANSCRIPT)OME:UMITOOLS' { + withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_(GEN|TRANSCRIPT)OME:UMITOOLS' { publishDir = [ [ path: { "${params.outdir}/${params.aligner}/umitools" }, From 685c1b15ae7ea344c016ab139ea909fb57b734c8 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Thu, 19 Dec 2024 21:27:21 +0000 Subject: [PATCH 098/107] Tidy up umitools/ umicollapse config --- workflows/rnaseq/nextflow.config | 96 +++++++++++++++++++------------- 1 file changed, 58 insertions(+), 38 deletions(-) diff --git a/workflows/rnaseq/nextflow.config b/workflows/rnaseq/nextflow.config index c9229a45f..a621b3bbe 100644 --- a/workflows/rnaseq/nextflow.config +++ b/workflows/rnaseq/nextflow.config @@ -134,14 +134,49 @@ if (!params.skip_alignment && params.aligner == 'star_salmon') { ] } + // Use the same umi_dedup prefix for umitools and umicollapse + withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMI(COLLAPSE|TOOLS)_TRANSCRIPTOME:UMI(COLLAPSE|TOOLS_DEDUP)' { ext.prefix = { "${meta.id}.umi_dedup.transcriptome.sorted" } + } + + // Publishing logic for umitools: + + withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_TRANSCRIPTOME:UMITOOLS_DEDUP' { publishDir = [ [ path: { params.save_align_intermeds || params.save_umi_intermeds ? "${params.outdir}/${params.aligner}" : params.outdir }, mode: params.publish_dir_mode, pattern: '*.bam', saveAs: { params.save_align_intermeds || params.save_umi_intermeds ? it : null } + ], + [ + path: { "${params.outdir}/${params.aligner}/umitools/transcriptomic_dedup_log" }, + mode: params.publish_dir_mode, + pattern: '*.log' + ], + [ + path: { "${params.outdir}/${params.aligner}/umitools" }, + mode: params.publish_dir_mode, + pattern: '*.tsv' + ] + ] + } + + // Publishing logic for umicollapse + + withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_TRANSCRIPTOME:UMICOLLAPSE' { + publishDir = [ + [ + path: { params.save_align_intermeds || params.save_umi_intermeds ? "${params.outdir}/${params.aligner}" : params.outdir }, + mode: params.publish_dir_mode, + pattern: '*.bam', + saveAs: { params.save_align_intermeds || params.save_umi_intermeds ? it : null } + ], + [ + path: { "${params.outdir}/${params.aligner}/umicollapse/transcriptomic_dedup_log" }, + mode: params.publish_dir_mode, + pattern: '*.log' ] ] } @@ -231,64 +266,49 @@ if (!params.skip_alignment) { ].join(' ').trim()} } + // Use the same umi_dedup prefix for umitools and umicollapse + withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMI(COLLAPSE|TOOLS)_GENOME:UMI(COLLAPSE|TOOLS_DEDUP)' { ext.prefix = { "${meta.id}.umi_dedup.sorted" } - publishDir = [ - [ - path: { params.save_align_intermeds || params.with_umi || params.save_umi_intermeds ? "${params.outdir}/${params.aligner}" : params.outdir }, - mode: params.publish_dir_mode, - pattern: '*.bam', - saveAs: { params.save_align_intermeds || params.with_umi || params.save_umi_intermeds ? it : null } - ] - ] } - withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_GENOME:UMICOLLAPSE' { - publishDir = [ - [ - path: { "${params.outdir}/${params.aligner}/umicollapse/genomic_dedup_log" }, - mode: params.publish_dir_mode, - pattern: '*.log' - ] - ] - } + // Publishing logic for umitools: - withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_TRANSCRIPTOME:UMICOLLAPSE' { + withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME:UMITOOLS_DEDUP' { publishDir = [ [ - path: { "${params.outdir}/${params.aligner}/umicollapse/transcriptomic_dedup_log" }, + path: { params.save_align_intermeds || params.save_umi_intermeds ? "${params.outdir}/${params.aligner}" : params.outdir }, mode: params.publish_dir_mode, - pattern: '*.log' - ] - ] - } - - withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME:UMITOOLS' { - publishDir = [ + pattern: '*.bam', + saveAs: { params.save_align_intermeds || params.save_umi_intermeds ? it : null } + ], [ path: { "${params.outdir}/${params.aligner}/umitools/genomic_dedup_log" }, mode: params.publish_dir_mode, pattern: '*.log' + ], + [ + path: { "${params.outdir}/${params.aligner}/umitools" }, + mode: params.publish_dir_mode, + pattern: '*.tsv' ] ] } - withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_TRANSCRIPTOME:UMITOOLS' { + // Publishing logic for umicollapse + + withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_GENOME:UMICOLLAPSE' { publishDir = [ [ - path: { "${params.outdir}/${params.aligner}/umitools/transcriptomic_dedup_log" }, + path: { params.save_align_intermeds || params.save_umi_intermeds ? "${params.outdir}/${params.aligner}" : params.outdir }, mode: params.publish_dir_mode, - pattern: '*.log' + pattern: '*.bam', + saveAs: { params.save_align_intermeds || params.save_umi_intermeds ? it : null } ], - ] - } - - withName: '.*:BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_(GEN|TRANSCRIPT)OME:UMITOOLS' { - publishDir = [ [ - path: { "${params.outdir}/${params.aligner}/umitools" }, + path: { "${params.outdir}/${params.aligner}/umicollapse/genomic_dedup_log" }, mode: params.publish_dir_mode, - pattern: '*.tsv' + pattern: '*.log' ] ] } @@ -297,10 +317,10 @@ if (!params.skip_alignment) { ext.args = { params.bam_csi_index ? '-c' : '' } ext.prefix = { "${meta.id}.umi_dedup.sorted" } publishDir = [ - path: { params.save_align_intermeds || params.with_umi || params.save_umi_intermeds ? "${params.outdir}/${params.aligner}" : params.outdir }, + path: { params.save_align_intermeds || params.save_umi_intermeds ? "${params.outdir}/${params.aligner}" : params.outdir }, mode: params.publish_dir_mode, pattern: '*.{bai,csi}', - saveAs: { params.save_align_intermeds || params.with_umi || params.save_umi_intermeds ? it : null } + saveAs: { params.save_align_intermeds || params.save_umi_intermeds ? it : null } ] } From cd97229b9d954931fe7388a201c1bf675a2e5a95 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Thu, 19 Dec 2024 21:29:49 +0000 Subject: [PATCH 099/107] update changelog --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index cb3b78e7a..18f790c09 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -27,6 +27,7 @@ Special thanks to the following for their contributions to the release: - [PR #1471](https://github.com/nf-core/rnaseq/pull/1471) - Fix prepare_genome subworkflow for sortmerna - [PR #1473](https://github.com/nf-core/rnaseq/pull/1473) - Bump STAR modules - [PR #1474](https://github.com/nf-core/rnaseq/pull/1474) - Bump versions to 3.18.0 +- [PR #1475](https://github.com/nf-core/rnaseq/pull/1475) - Fix log publishing around umitools/ umicollapse ## Parameters From bdcd760196c62fa41a3a3fe8b8c29b7ea240149d Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Thu, 19 Dec 2024 22:16:10 +0000 Subject: [PATCH 100/107] Update UMI test --- tests/umi.nf.test | 2 + tests/umi.nf.test.snap | 122 ++++++++++++++++++++++++++++++++--------- 2 files changed, 97 insertions(+), 27 deletions(-) diff --git a/tests/umi.nf.test b/tests/umi.nf.test index dba4c07fe..234db5908 100644 --- a/tests/umi.nf.test +++ b/tests/umi.nf.test @@ -14,7 +14,9 @@ nextflow_pipeline { skip_bbsplit = true umi_dedup_tool = 'umicollapse' aligner = 'hisat2' + publish_dir_mode = 'link' outdir = "$outputDir" + save_umi_intermeds = true } } diff --git a/tests/umi.nf.test.snap b/tests/umi.nf.test.snap index 4e153a33b..e9d1efe10 100644 --- a/tests/umi.nf.test.snap +++ b/tests/umi.nf.test.snap @@ -612,6 +612,10 @@ "star_salmon/RAP1_IAA_30M_REP1", "star_salmon/RAP1_IAA_30M_REP1.umi_dedup.sorted.bam", "star_salmon/RAP1_IAA_30M_REP1.umi_dedup.sorted.bam.bai", + "star_salmon/RAP1_IAA_30M_REP1.umi_dedup.transcriptome.bam", + "star_salmon/RAP1_IAA_30M_REP1.umi_dedup.transcriptome.filtered.bam", + "star_salmon/RAP1_IAA_30M_REP1.umi_dedup.transcriptome.sorted.bam", + "star_salmon/RAP1_IAA_30M_REP1.umi_dedup.transcriptome.sorted.bam.bai", "star_salmon/RAP1_IAA_30M_REP1/aux_info", "star_salmon/RAP1_IAA_30M_REP1/aux_info/ambig_info.tsv", "star_salmon/RAP1_IAA_30M_REP1/aux_info/expected_bias.gz", @@ -629,6 +633,9 @@ "star_salmon/RAP1_UNINDUCED_REP1", "star_salmon/RAP1_UNINDUCED_REP1.umi_dedup.sorted.bam", "star_salmon/RAP1_UNINDUCED_REP1.umi_dedup.sorted.bam.bai", + "star_salmon/RAP1_UNINDUCED_REP1.umi_dedup.transcriptome.bam", + "star_salmon/RAP1_UNINDUCED_REP1.umi_dedup.transcriptome.sorted.bam", + "star_salmon/RAP1_UNINDUCED_REP1.umi_dedup.transcriptome.sorted.bam.bai", "star_salmon/RAP1_UNINDUCED_REP1/aux_info", "star_salmon/RAP1_UNINDUCED_REP1/aux_info/ambig_info.tsv", "star_salmon/RAP1_UNINDUCED_REP1/aux_info/expected_bias.gz", @@ -646,6 +653,9 @@ "star_salmon/RAP1_UNINDUCED_REP2", "star_salmon/RAP1_UNINDUCED_REP2.umi_dedup.sorted.bam", "star_salmon/RAP1_UNINDUCED_REP2.umi_dedup.sorted.bam.bai", + "star_salmon/RAP1_UNINDUCED_REP2.umi_dedup.transcriptome.bam", + "star_salmon/RAP1_UNINDUCED_REP2.umi_dedup.transcriptome.sorted.bam", + "star_salmon/RAP1_UNINDUCED_REP2.umi_dedup.transcriptome.sorted.bam.bai", "star_salmon/RAP1_UNINDUCED_REP2/aux_info", "star_salmon/RAP1_UNINDUCED_REP2/aux_info/ambig_info.tsv", "star_salmon/RAP1_UNINDUCED_REP2/aux_info/expected_bias.gz", @@ -663,6 +673,10 @@ "star_salmon/WT_REP1", "star_salmon/WT_REP1.umi_dedup.sorted.bam", "star_salmon/WT_REP1.umi_dedup.sorted.bam.bai", + "star_salmon/WT_REP1.umi_dedup.transcriptome.bam", + "star_salmon/WT_REP1.umi_dedup.transcriptome.filtered.bam", + "star_salmon/WT_REP1.umi_dedup.transcriptome.sorted.bam", + "star_salmon/WT_REP1.umi_dedup.transcriptome.sorted.bam.bai", "star_salmon/WT_REP1/aux_info", "star_salmon/WT_REP1/aux_info/ambig_info.tsv", "star_salmon/WT_REP1/aux_info/expected_bias.gz", @@ -680,6 +694,10 @@ "star_salmon/WT_REP2", "star_salmon/WT_REP2.umi_dedup.sorted.bam", "star_salmon/WT_REP2.umi_dedup.sorted.bam.bai", + "star_salmon/WT_REP2.umi_dedup.transcriptome.bam", + "star_salmon/WT_REP2.umi_dedup.transcriptome.filtered.bam", + "star_salmon/WT_REP2.umi_dedup.transcriptome.sorted.bam", + "star_salmon/WT_REP2.umi_dedup.transcriptome.sorted.bam.bai", "star_salmon/WT_REP2/aux_info", "star_salmon/WT_REP2/aux_info/ambig_info.tsv", "star_salmon/WT_REP2/aux_info/expected_bias.gz", @@ -1261,10 +1279,18 @@ "trimgalore/WT_REP2_trimmed_2.fastq.gz_trimming_report.txt", "umitools", "umitools/RAP1_IAA_30M_REP1.umi_extract.log", + "umitools/RAP1_IAA_30M_REP1.umi_extract_1.fastq.gz", + "umitools/RAP1_IAA_30M_REP1.umi_extract_2.fastq.gz", + "umitools/RAP1_UNINDUCED_REP1.umi_extract.fastq.gz", "umitools/RAP1_UNINDUCED_REP1.umi_extract.log", + "umitools/RAP1_UNINDUCED_REP2.umi_extract.fastq.gz", "umitools/RAP1_UNINDUCED_REP2.umi_extract.log", "umitools/WT_REP1.umi_extract.log", - "umitools/WT_REP2.umi_extract.log" + "umitools/WT_REP1.umi_extract_1.fastq.gz", + "umitools/WT_REP1.umi_extract_2.fastq.gz", + "umitools/WT_REP2.umi_extract.log", + "umitools/WT_REP2.umi_extract_1.fastq.gz", + "umitools/WT_REP2.umi_extract_2.fastq.gz" ], [ "genome_gfp.fasta:md5,e23e302af63736a199985a169fdac055", @@ -1467,14 +1493,22 @@ "WT_REP2.umi_dedup.sorted_per_umi_per_position.tsv:md5,6f5656947a7f0076df446e6f40430027", "WT_REP2.umi_dedup.transcriptome.sorted_edit_distance.tsv:md5,3e3c6a7e8996e566350742e9911366d3", "WT_REP2.umi_dedup.transcriptome.sorted_per_umi.tsv:md5,0c986c4cb7a77f650a19e2c454b9b179", - "WT_REP2.umi_dedup.transcriptome.sorted_per_umi_per_position.tsv:md5,af9028dbdab81de3854a32cd1d19ac8b" + "WT_REP2.umi_dedup.transcriptome.sorted_per_umi_per_position.tsv:md5,af9028dbdab81de3854a32cd1d19ac8b", + "RAP1_IAA_30M_REP1.umi_extract_1.fastq.gz:md5,e83d7f738fbbfaa541a2e71fe4663447", + "RAP1_IAA_30M_REP1.umi_extract_2.fastq.gz:md5,4f2873cbf584d6e84187238a4ae2b8fa", + "RAP1_UNINDUCED_REP1.umi_extract.fastq.gz:md5,9e42242fd68baac592140f63a8a716ce", + "RAP1_UNINDUCED_REP2.umi_extract.fastq.gz:md5,5a92b642927b8603c4765e5305e23e9c", + "WT_REP1.umi_extract_1.fastq.gz:md5,f312fac9c384a889ae4f959839263604", + "WT_REP1.umi_extract_2.fastq.gz:md5,ffca24924108fd54151620b7538b9e1a", + "WT_REP2.umi_extract_1.fastq.gz:md5,c3180451a24ce51fc35c1684521ae287", + "WT_REP2.umi_extract_2.fastq.gz:md5,067ff23f8d1307ad241cd70bc186b5c1" ] ], "meta": { - "nf-test": "0.9.0", - "nextflow": "24.10.2" + "nf-test": "0.9.2", + "nextflow": "24.10.3" }, - "timestamp": "2024-12-11T18:07:55.751564456" + "timestamp": "2024-12-19T21:54:27.627773754" }, "Params: --aligner hisat2 --umi_dedup_tool 'umicollapse'": { "content": [ @@ -2130,13 +2164,13 @@ "hisat2/stringtie/WT_REP2.coverage.gtf", "hisat2/stringtie/WT_REP2.gene.abundance.txt", "hisat2/stringtie/WT_REP2.transcripts.gtf", - "hisat2/umitools", - "hisat2/umitools/genomic_dedup_log", - "hisat2/umitools/genomic_dedup_log/RAP1_IAA_30M_REP1.umi_dedup.sorted_UMICollapse.log", - "hisat2/umitools/genomic_dedup_log/RAP1_UNINDUCED_REP1.umi_dedup.sorted_UMICollapse.log", - "hisat2/umitools/genomic_dedup_log/RAP1_UNINDUCED_REP2.umi_dedup.sorted_UMICollapse.log", - "hisat2/umitools/genomic_dedup_log/WT_REP1.umi_dedup.sorted_UMICollapse.log", - "hisat2/umitools/genomic_dedup_log/WT_REP2.umi_dedup.sorted_UMICollapse.log", + "hisat2/umicollapse", + "hisat2/umicollapse/genomic_dedup_log", + "hisat2/umicollapse/genomic_dedup_log/RAP1_IAA_30M_REP1.umi_dedup.sorted_UMICollapse.log", + "hisat2/umicollapse/genomic_dedup_log/RAP1_UNINDUCED_REP1.umi_dedup.sorted_UMICollapse.log", + "hisat2/umicollapse/genomic_dedup_log/RAP1_UNINDUCED_REP2.umi_dedup.sorted_UMICollapse.log", + "hisat2/umicollapse/genomic_dedup_log/WT_REP1.umi_dedup.sorted_UMICollapse.log", + "hisat2/umicollapse/genomic_dedup_log/WT_REP2.umi_dedup.sorted_UMICollapse.log", "multiqc", "multiqc/hisat2", "multiqc/hisat2/multiqc_report.html", @@ -2548,10 +2582,18 @@ "trimgalore/WT_REP2_trimmed_2.fastq.gz_trimming_report.txt", "umitools", "umitools/RAP1_IAA_30M_REP1.umi_extract.log", + "umitools/RAP1_IAA_30M_REP1.umi_extract_1.fastq.gz", + "umitools/RAP1_IAA_30M_REP1.umi_extract_2.fastq.gz", + "umitools/RAP1_UNINDUCED_REP1.umi_extract.fastq.gz", "umitools/RAP1_UNINDUCED_REP1.umi_extract.log", + "umitools/RAP1_UNINDUCED_REP2.umi_extract.fastq.gz", "umitools/RAP1_UNINDUCED_REP2.umi_extract.log", "umitools/WT_REP1.umi_extract.log", - "umitools/WT_REP2.umi_extract.log" + "umitools/WT_REP1.umi_extract_1.fastq.gz", + "umitools/WT_REP1.umi_extract_2.fastq.gz", + "umitools/WT_REP2.umi_extract.log", + "umitools/WT_REP2.umi_extract_1.fastq.gz", + "umitools/WT_REP2.umi_extract_2.fastq.gz" ], [ "genome_gfp.fasta:md5,e23e302af63736a199985a169fdac055", @@ -2621,6 +2663,11 @@ "e_data.ctab:md5,593daf0b84f008c0c64319990ef2fd42", "i2t.ctab:md5,dda3d3ccd7d4184d947c654ae73efb7b", "i_data.ctab:md5,01c675638f918a70ea689f618a2610bc", + "RAP1_IAA_30M_REP1.umi_dedup.sorted_UMICollapse.log:md5,d6eaaabd7053bef88f3af2b6fc2df45b", + "RAP1_UNINDUCED_REP1.umi_dedup.sorted_UMICollapse.log:md5,441be871ac12aa94f871fab9e8b6b4bf", + "RAP1_UNINDUCED_REP2.umi_dedup.sorted_UMICollapse.log:md5,8a0025a58969badeb3dc35a7c1bdd68e", + "WT_REP1.umi_dedup.sorted_UMICollapse.log:md5,75e6da19996c3ea6fde5cdd4be2dcfda", + "WT_REP2.umi_dedup.sorted_UMICollapse.log:md5,87cdd1eadc3db19bd9f45bb941b1e1a7", "cutadapt_filtered_reads_plot.txt:md5,3f122969fa288888e5abef061b7963f2", "cutadapt_trimmed_sequences_plot_3_Counts.txt:md5,5e8a821c9a4deb46c11bc65969b8864f", "cutadapt_trimmed_sequences_plot_3_Obs_Exp.txt:md5,bf8abefa7c5f2f1e1140749983279d9d", @@ -2688,22 +2735,27 @@ "cmd_info.json:md5,809380ddce725a8fab75dd7741b64bf6", "lib_format_counts.json:md5,d231ba7624b67eb654989f69530e2925", "R_sessionInfo.log:md5,fb0da0d7ad6994ed66a8e68348b19676", - "tx2gene.tsv:md5,0e2418a69d2eba45097ebffc2f700bfe" + "tx2gene.tsv:md5,0e2418a69d2eba45097ebffc2f700bfe", + "RAP1_IAA_30M_REP1.umi_extract_1.fastq.gz:md5,e83d7f738fbbfaa541a2e71fe4663447", + "RAP1_IAA_30M_REP1.umi_extract_2.fastq.gz:md5,4f2873cbf584d6e84187238a4ae2b8fa", + "RAP1_UNINDUCED_REP1.umi_extract.fastq.gz:md5,9e42242fd68baac592140f63a8a716ce", + "RAP1_UNINDUCED_REP2.umi_extract.fastq.gz:md5,5a92b642927b8603c4765e5305e23e9c", + "WT_REP1.umi_extract_1.fastq.gz:md5,f312fac9c384a889ae4f959839263604", + "WT_REP1.umi_extract_2.fastq.gz:md5,ffca24924108fd54151620b7538b9e1a", + "WT_REP2.umi_extract_1.fastq.gz:md5,c3180451a24ce51fc35c1684521ae287", + "WT_REP2.umi_extract_2.fastq.gz:md5,067ff23f8d1307ad241cd70bc186b5c1" ] ], "meta": { - "nf-test": "0.9.0", - "nextflow": "24.10.2" + "nf-test": "0.9.2", + "nextflow": "24.10.3" }, - "timestamp": "2024-12-11T18:01:45.228731692" + "timestamp": "2024-12-19T22:07:42.25625667" }, "--umi_dedup_tool 'umitools - stub": { "content": [ - 32, + 31, { - "BBMAP_BBSPLIT": { - "bbmap": 39.1 - }, "CAT_FASTQ": { "cat": 9.5 }, @@ -2793,20 +2845,36 @@ "trimgalore/WT_REP2_trimmed_2.fastq.gz_trimming_report.txt", "umitools", "umitools/RAP1_IAA_30M_REP1.umi_extract.log", + "umitools/RAP1_IAA_30M_REP1.umi_extract_1.fastq.gz", + "umitools/RAP1_IAA_30M_REP1.umi_extract_2.fastq.gz", + "umitools/RAP1_UNINDUCED_REP1.umi_extract.fastq.gz", "umitools/RAP1_UNINDUCED_REP1.umi_extract.log", + "umitools/RAP1_UNINDUCED_REP2.umi_extract.fastq.gz", "umitools/RAP1_UNINDUCED_REP2.umi_extract.log", "umitools/WT_REP1.umi_extract.log", - "umitools/WT_REP2.umi_extract.log" + "umitools/WT_REP1.umi_extract_1.fastq.gz", + "umitools/WT_REP1.umi_extract_2.fastq.gz", + "umitools/WT_REP2.umi_extract.log", + "umitools/WT_REP2.umi_extract_1.fastq.gz", + "umitools/WT_REP2.umi_extract_2.fastq.gz" ], [ "genome_transcriptome.fasta:md5,d41d8cd98f00b204e9800998ecf8427e", - "genome_transcriptome.gtf:md5,d41d8cd98f00b204e9800998ecf8427e" + "genome_transcriptome.gtf:md5,d41d8cd98f00b204e9800998ecf8427e", + "RAP1_IAA_30M_REP1.umi_extract_1.fastq.gz:md5,68b329da9893e34099c7d8ad5cb9c940", + "RAP1_IAA_30M_REP1.umi_extract_2.fastq.gz:md5,68b329da9893e34099c7d8ad5cb9c940", + "RAP1_UNINDUCED_REP1.umi_extract.fastq.gz:md5,68b329da9893e34099c7d8ad5cb9c940", + "RAP1_UNINDUCED_REP2.umi_extract.fastq.gz:md5,68b329da9893e34099c7d8ad5cb9c940", + "WT_REP1.umi_extract_1.fastq.gz:md5,68b329da9893e34099c7d8ad5cb9c940", + "WT_REP1.umi_extract_2.fastq.gz:md5,68b329da9893e34099c7d8ad5cb9c940", + "WT_REP2.umi_extract_1.fastq.gz:md5,68b329da9893e34099c7d8ad5cb9c940", + "WT_REP2.umi_extract_2.fastq.gz:md5,68b329da9893e34099c7d8ad5cb9c940" ] ], "meta": { - "nf-test": "0.9.0", - "nextflow": "24.10.2" + "nf-test": "0.9.2", + "nextflow": "24.10.3" }, - "timestamp": "2024-12-11T18:08:48.404716766" + "timestamp": "2024-12-19T21:55:24.38516136" } -} +} \ No newline at end of file From b799aa3ed291741e0221ea9a7bd2a21f34e5ca40 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Thu, 19 Dec 2024 22:17:37 +0000 Subject: [PATCH 101/107] Update umi.nf.test --- tests/umi.nf.test | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/umi.nf.test b/tests/umi.nf.test index 234db5908..5ca3d2420 100644 --- a/tests/umi.nf.test +++ b/tests/umi.nf.test @@ -14,7 +14,6 @@ nextflow_pipeline { skip_bbsplit = true umi_dedup_tool = 'umicollapse' aligner = 'hisat2' - publish_dir_mode = 'link' outdir = "$outputDir" save_umi_intermeds = true } From 6ce70962cbc05b309d57acb3211985ca96b236f7 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Thu, 19 Dec 2024 22:36:45 +0000 Subject: [PATCH 102/107] Exclude umi logs --- tests/.nftignore | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/.nftignore b/tests/.nftignore index 7f3fad699..8043747d3 100644 --- a/tests/.nftignore +++ b/tests/.nftignore @@ -32,7 +32,7 @@ umitools/*.umi_extract.log {hisat2,star_rsem,star_salmon}/stringtie/*.ballgown/t_data.ctab {hisat2,star_rsem,star_salmon}/stringtie/*.gene.abundance.txt {hisat2,star_rsem,star_salmon}/stringtie/*.{coverage,transcripts}.gtf -{hisat2,star_rsem,star_salmon}/umitools/genomic_dedup_log/*_UMICollapse.log +{hisat2,star_rsem,star_salmon}/{umitools,umicollapse}/{genomic,transcriptomic}_dedup_log/*.log {multiqc,multiqc/**}/multiqc_report.html {multiqc,multiqc/**}/multiqc_report_data/fastqc_{raw,trimmed}_top_overrepresented_sequences_table.txt {multiqc,multiqc/**}/multiqc_report_data/hisat2_pe_plot.txt From cb59092581424f893baeddd999176ade4d73ede7 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Thu, 19 Dec 2024 22:45:18 +0000 Subject: [PATCH 103/107] Update outputs in docs --- docs/output.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/output.md b/docs/output.md index 0bea48601..34326ea23 100644 --- a/docs/output.md +++ b/docs/output.md @@ -120,7 +120,7 @@ If multiple libraries/runs have been provided for the same sample in the input s
    -[UMI-tools](https://github.com/CGATOxford/UMI-tools) deduplicates reads based on unique molecular identifiers (UMIs) to address PCR-bias. Firstly, the UMI-tools `extract` command removes the UMI barcode information from the read sequence and adds it to the read name. Secondly, reads are deduplicated based on UMI identifier after mapping as highlighted in the [UMI-tools dedup](#umi-tools-dedup) section. +[UMI-tools](https://github.com/CGATOxford/UMI-tools) and [UMICollapse](https://github.com/Daniel-Liu-c0deb0t/UMICollapse) deduplicate reads based on unique molecular identifiers (UMIs) to address PCR-bias. Firstly, the UMI-tools `extract` command removes the UMI barcode information from the read sequence and adds it to the read name. Secondly, reads are deduplicated based on UMI identifier after mapping as highlighted in the [UMI dedup](#umi-dedup) section. To facilitate processing of input data which has the UMI barcode already embedded in the read name from the start, `--skip_umi_extract` can be specified in conjunction with `--with_umi`. @@ -305,7 +305,7 @@ The original BAM files generated by the selected alignment algorithm are further ![MultiQC - SAMtools mapped reads per contig plot](images/mqc_samtools_idxstats.png) -### UMI-tools dedup +### UMI dedup
    Output files @@ -314,7 +314,7 @@ The original BAM files generated by the selected alignment algorithm are further - `.umi_dedup.sorted.bam`: If `--save_umi_intermeds` is specified the UMI deduplicated, coordinate sorted BAM file containing read alignments will be placed in this directory. - `.umi_dedup.sorted.bam.bai`: If `--save_umi_intermeds` is specified the BAI index file for the UMI deduplicated, coordinate sorted BAM file will be placed in this directory. - `.umi_dedup.sorted.bam.csi`: If `--save_umi_intermeds --bam_csi_index` is specified the CSI index file for the UMI deduplicated, coordinate sorted BAM file will be placed in this directory. -- `/umitools/` +- `/umitools/` (UMI-tools only) - `*_edit_distance.tsv`: Reports the (binned) average edit distance between the UMIs at each position. - `*_per_umi.tsv`: UMI-level summary statistics. - `*_per_umi_per_position.tsv`: Tabulates the counts for unique combinations of UMI and position. @@ -323,7 +323,7 @@ The content of the files above is explained in more detail in the [UMI-tools doc
    -After extracting the UMI information from the read sequence (see [UMI-tools extract](#umi-tools-extract)), the second step in the removal of UMI barcodes involves deduplicating the reads based on both mapping and UMI barcode information using the UMI-tools `dedup` command. This will generate a filtered BAM file after the removal of PCR duplicates. +After extracting the UMI information from the read sequence (see [UMI-tools extract](#umi-tools-extract)), the second step in the removal of UMI barcodes involves deduplicating the reads based on both mapping and UMI barcode information. UMI deduplication can be carried out either with [UMI-tools](https://github.com/CGATOxford/UMI-tools) or [UMICollapse](https://github.com/Daniel-Liu-c0deb0t/UMICollapse), set via the `umi_dedup_tool` parameter. The output BAM files are the same, though UMI-tools has some additional outputs, as described above. Either method will generate a filtered BAM file after the removal of PCR duplicates. ### picard MarkDuplicates From c98f8f3db35fcfbf50fc650b16a5be972c4c9dfb Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Thu, 19 Dec 2024 22:45:49 +0000 Subject: [PATCH 104/107] Fix snapshot after log exclusion --- tests/umi.nf.test.snap | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/tests/umi.nf.test.snap b/tests/umi.nf.test.snap index e9d1efe10..a5d06e069 100644 --- a/tests/umi.nf.test.snap +++ b/tests/umi.nf.test.snap @@ -2663,11 +2663,6 @@ "e_data.ctab:md5,593daf0b84f008c0c64319990ef2fd42", "i2t.ctab:md5,dda3d3ccd7d4184d947c654ae73efb7b", "i_data.ctab:md5,01c675638f918a70ea689f618a2610bc", - "RAP1_IAA_30M_REP1.umi_dedup.sorted_UMICollapse.log:md5,d6eaaabd7053bef88f3af2b6fc2df45b", - "RAP1_UNINDUCED_REP1.umi_dedup.sorted_UMICollapse.log:md5,441be871ac12aa94f871fab9e8b6b4bf", - "RAP1_UNINDUCED_REP2.umi_dedup.sorted_UMICollapse.log:md5,8a0025a58969badeb3dc35a7c1bdd68e", - "WT_REP1.umi_dedup.sorted_UMICollapse.log:md5,75e6da19996c3ea6fde5cdd4be2dcfda", - "WT_REP2.umi_dedup.sorted_UMICollapse.log:md5,87cdd1eadc3db19bd9f45bb941b1e1a7", "cutadapt_filtered_reads_plot.txt:md5,3f122969fa288888e5abef061b7963f2", "cutadapt_trimmed_sequences_plot_3_Counts.txt:md5,5e8a821c9a4deb46c11bc65969b8864f", "cutadapt_trimmed_sequences_plot_3_Obs_Exp.txt:md5,bf8abefa7c5f2f1e1140749983279d9d", @@ -2750,7 +2745,7 @@ "nf-test": "0.9.2", "nextflow": "24.10.3" }, - "timestamp": "2024-12-19T22:07:42.25625667" + "timestamp": "2024-12-19T22:33:42.012684597" }, "--umi_dedup_tool 'umitools - stub": { "content": [ From 311b63396f4343a99ac9b060244da3d8897df87b Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Thu, 19 Dec 2024 23:31:52 +0000 Subject: [PATCH 105/107] Add keep intermeds to test file --- tests/umi.nf.test | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/umi.nf.test b/tests/umi.nf.test index 5ca3d2420..3a7083a28 100644 --- a/tests/umi.nf.test +++ b/tests/umi.nf.test @@ -50,6 +50,7 @@ nextflow_pipeline { umitools_dedup_stats = true skip_bbsplit = true outdir = "$outputDir" + save_umi_intermeds = true } } From 0a21c3f117ae3d6d850aca9781a2b49e42714d40 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Fri, 20 Dec 2024 00:03:58 +0000 Subject: [PATCH 106/107] Fix snapshot --- tests/umi.nf.test.snap | 29 ++++++++--------------------- 1 file changed, 8 insertions(+), 21 deletions(-) diff --git a/tests/umi.nf.test.snap b/tests/umi.nf.test.snap index a5d06e069..1d0df4de3 100644 --- a/tests/umi.nf.test.snap +++ b/tests/umi.nf.test.snap @@ -1508,7 +1508,7 @@ "nf-test": "0.9.2", "nextflow": "24.10.3" }, - "timestamp": "2024-12-19T21:54:27.627773754" + "timestamp": "2024-12-20T00:02:04.611696704" }, "Params: --aligner hisat2 --umi_dedup_tool 'umicollapse'": { "content": [ @@ -2749,8 +2749,11 @@ }, "--umi_dedup_tool 'umitools - stub": { "content": [ - 31, + 32, { + "BBMAP_BBSPLIT": { + "bbmap": 39.1 + }, "CAT_FASTQ": { "cat": 9.5 }, @@ -2840,36 +2843,20 @@ "trimgalore/WT_REP2_trimmed_2.fastq.gz_trimming_report.txt", "umitools", "umitools/RAP1_IAA_30M_REP1.umi_extract.log", - "umitools/RAP1_IAA_30M_REP1.umi_extract_1.fastq.gz", - "umitools/RAP1_IAA_30M_REP1.umi_extract_2.fastq.gz", - "umitools/RAP1_UNINDUCED_REP1.umi_extract.fastq.gz", "umitools/RAP1_UNINDUCED_REP1.umi_extract.log", - "umitools/RAP1_UNINDUCED_REP2.umi_extract.fastq.gz", "umitools/RAP1_UNINDUCED_REP2.umi_extract.log", "umitools/WT_REP1.umi_extract.log", - "umitools/WT_REP1.umi_extract_1.fastq.gz", - "umitools/WT_REP1.umi_extract_2.fastq.gz", - "umitools/WT_REP2.umi_extract.log", - "umitools/WT_REP2.umi_extract_1.fastq.gz", - "umitools/WT_REP2.umi_extract_2.fastq.gz" + "umitools/WT_REP2.umi_extract.log" ], [ "genome_transcriptome.fasta:md5,d41d8cd98f00b204e9800998ecf8427e", - "genome_transcriptome.gtf:md5,d41d8cd98f00b204e9800998ecf8427e", - "RAP1_IAA_30M_REP1.umi_extract_1.fastq.gz:md5,68b329da9893e34099c7d8ad5cb9c940", - "RAP1_IAA_30M_REP1.umi_extract_2.fastq.gz:md5,68b329da9893e34099c7d8ad5cb9c940", - "RAP1_UNINDUCED_REP1.umi_extract.fastq.gz:md5,68b329da9893e34099c7d8ad5cb9c940", - "RAP1_UNINDUCED_REP2.umi_extract.fastq.gz:md5,68b329da9893e34099c7d8ad5cb9c940", - "WT_REP1.umi_extract_1.fastq.gz:md5,68b329da9893e34099c7d8ad5cb9c940", - "WT_REP1.umi_extract_2.fastq.gz:md5,68b329da9893e34099c7d8ad5cb9c940", - "WT_REP2.umi_extract_1.fastq.gz:md5,68b329da9893e34099c7d8ad5cb9c940", - "WT_REP2.umi_extract_2.fastq.gz:md5,68b329da9893e34099c7d8ad5cb9c940" + "genome_transcriptome.gtf:md5,d41d8cd98f00b204e9800998ecf8427e" ] ], "meta": { "nf-test": "0.9.2", "nextflow": "24.10.3" }, - "timestamp": "2024-12-19T21:55:24.38516136" + "timestamp": "2024-12-19T23:28:01.570835895" } } \ No newline at end of file From eb7d7640aa066189b4d98565cf37657fdb55d377 Mon Sep 17 00:00:00 2001 From: Jonathan Manning Date: Fri, 20 Dec 2024 15:54:19 +0000 Subject: [PATCH 107/107] Update CHANGELOG.md --- CHANGELOG.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 18f790c09..5019c34f2 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -11,6 +11,7 @@ Special thanks to the following for their contributions to the release: - [Caitlin Winkler](https://github.com/oligomyeggo) - [Jonathan Manning](https://github.com/pinin4fjords) +- [Lorenzo Sola](https://github.com/LorenzoS96) - [Maxime Garcia](https://github.com/maxulysse) - [Siddhartha Bagaria](https://github.com/siddharthab) @@ -28,6 +29,7 @@ Special thanks to the following for their contributions to the release: - [PR #1473](https://github.com/nf-core/rnaseq/pull/1473) - Bump STAR modules - [PR #1474](https://github.com/nf-core/rnaseq/pull/1474) - Bump versions to 3.18.0 - [PR #1475](https://github.com/nf-core/rnaseq/pull/1475) - Fix log publishing around umitools/ umicollapse +- [PR #1447](https://github.com/nf-core/rnaseq/pull/1447) - Add tutorial series for analysing count data ## Parameters

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zuc{)L*P3#oSv?I%8Z)ml#-hg}r9OM|yaA&I9Tys7;~O?s)}SZhk&&kAWHQbhvG`vV*xUViQxT#2)kjC$8zomizi!zv zFINaczE9+HDO7Jyy@s0Q)(pENp1t+>?!Hq%*}t)|(J)cH5X4oT?fU8yq$Gp0@k1AF z#PIj;In2nz!^f^9Q$z){back*Ec@j#)z|Se&Xpbql4h&!ZCuvZS$s9#+v|eN#aWtL z*~iD%!(#>M1uZup+E<0Vo5}iWZ9#=KJXJm0KF1c~O48`ESChu?n&;~qA}NK$$$e_4 zGcLBdWliUHdCHc`r#>9ZlI0 ze3o*0EbG_hlUKIJ%b$%-dSm5ZQii5ED#=aETzcL3Ub`IjPSZRb#Txy{#q6o)jp6w99rU-uQJ9)s%RHrRay!z&QS12ArN-79f z#8`sj5^E~^X$$3$KWcd1`qL2w+N70`@A;C?xf(OM(R1r@ z??M%h7IzBw6w{l_i{G>mPP@2Y@NLS-l7GOypNMmN-QePV;o+Z@qTi8WyK6lgQ-$I=Lse_)=%)7iehP8Mb1cdu5W zzp*p%I<-|CGiOm*94`uHZ|Sldf8$Bza|a%o{6D|4;O|0}(3Jk?FMoZ9U3bSftQQM= zOzfX<{)*}CwW;_BOPY(I{vG9i{tS1Ql88JC=L{~>?YG9FP_cWTvOJ~uPp^OXFT4VJ zrxX%a!TPN+B{+CU-aUrDqx^SRz<m|IKfW<$ mean). + + +![RNAseq](./img/overdispersion.png) + +DESeq2 addresses this issue by employing the **negative binomial distribution**, which generalises the Poisson distribution by introducing an additional dispersion parameter. This parameter quantifies the extra variability present in RNA-seq data, providing a more realistic representation than the Poisson distribution, which assumes equal mean and variance. DESeq2 starts by estimating the **common dispersion**, a single estimate of dispersion applicable to all genes in the dataset. This estimate provides a baseline for variability across all genes in the dataset. Next, DESeq2 estimates **gene-wise dispersion**, a separate estimate of dispersion for each individual gene, taking into account that different genes may exhibit varying levels of expression variability due to biological differences. The dispersion parameter (α) is related to the mean (μ) and variance of the data, as described by the equation: + +`Var = μ + α ⋅ μ²` + +A key feature of DESeq2's dispersion estimates is their negative correlation with the mean and positive correlation with variance. Genes with low expression have higher dispersion values, while genes with high expression tend to have lower dispersion. Additionally, genes sharing similar mean expression levels can exhibit different dispersion estimates based on their variance. To improve the accuracy of dispersion estimates, DESeq2 assumes that genes with similar expression profiles share similar dispersion patterns and leverages this information to refine the estimates. + +- **Mean-dispersion relationship**: this process, known as dispersion fitting, models the relationship between the mean expression level of a gene and its dispersion. In this process, DESeq2 identifies a trend in the dispersion estimates across genes. The fitted curve, typically a smooth curve, describes how dispersion changes as a function of the mean expression level. + +- **Final dispersion estimates**: DESeq2 refines the gene-wise dispersion by shrinking it towards the fitted curve. The "shrinkage" helps control for overfitting, particularly in genes with low counts or few replicates, and makes the dispersion estimates more reliable. However, genes with exceptionally high dispersion values are not shrunk because they likely deviate from the modelling assumptions, exhibiting elevated variability due to biological or technical factors. Shrinking these values could lead to false positives. + +3) **Fitting model and testing**: the initial step in hypothesis testing involves defining a **null hypothesis** for each gene. Here, the null hypothesis states that there is no difference in expression between the two sample groups (LFC == 0). Next, DESeq applies a statistical test to assess whether the null hypothesis is true. +DESeq2 fits a generalised linear model (GLM) to the normalised counts using the calculated size factors and final dispersion estimates. A GLM is a flexible extension of linear regression that models the relationship between a response variable (normalised counts) and predictors (e.g., condition). +A GLM is used because it provides flexibility in modelling RNA-seq data, which has unique characteristics, such as discrete counts and overdispersion (variance exceeding the mean). By using a **negative binomial distribution**, DESeq2's GLM can handle the additional variability in gene expression counts that often occurs in RNA-seq data. +Once the model is fit, coefficients are estimated for each sample group along with their standard errors. These coefficients represent the estimated log2 fold changes between groups and serve as the basis for hypothesis testing, using either a **Wald test** or a **Likelihood Ratio Test (LRT)**, depending on the experimental design: + +- **Wald Test**: The Wald test is ideal for simpler experimental designs, such as comparing two conditions (e.g., treated vs untreated). It tests whether the estimated effect size (log2 fold change) of a predictor variable (like treatment) is significantly different from zero. This test provides direct estimates of fold changes with associated p-values, making it computationally efficient for straightforward comparisons. + +- **Likelihood Ratio Test (LRT)**: The LRT is more suitable for complex experimental designs involving multiple variables (e.g., time points, treatments and batches). It compares the fit of two nested models: one with the factor of interest (e.g., treatment) and one without it to assess whether including that factor significantly improves model fit. This approach allows DESeq2 to account for confounding variables and to isolate the effect of specific variables on gene expression, offering flexibility for multi-factor analyses. + +When performing multiple tests, such as in the case of RNA-seq data where thousands of genes are tested, the risk of false positives increases. To account for this, DESeq2 employs multiple test correction methods (the Benjamini-Hochberg procedure is the default) to adjust the p-values and control the false discovery rate (FDR). The FDR is the expected proportion of false positives among the identified significant results. + +!!! note + + For example, by setting the FDR cutoff to < 0.05, 5% of genes identified as differentially expressed are expected to be false positives. For instance, if 400 genes are identified as differentially expressed with an FDR cutoff of 0.05, you would expect 20 of them to be false positives. + +After identifying DE genes using DESeq2, it is essential to interpret the biological significance of these genes through functional analysis. This involves examining the roles of the differentially expressed genes in various biological processes, molecular functions, and pathways, providing insights into the underlying mechanisms driving the observed changes in gene expression. This interpretation can help in discovering pathways involved in disease or identifying potential therapeutic targets. Different tools are available to carry out these functional analyses, such as [Gene Ontology](https://geneontology.org), [Reactome](https://reactome.org/), [KEGG](https://www.genome.jp/kegg), [clusterProfiler](https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html), [g:Profiler](https://biit.cs.ut.ee/gprofiler), and [WikiPathways](https://www.wikipathways.org). From 72e6e2eac76ebf635a413b0f7b41a9ee130f3dd8 Mon Sep 17 00:00:00 2001 From: LorenzoS96 <47034913+LorenzoS96@users.noreply.github.com> Date: Tue, 29 Oct 2024 15:03:49 +0100 Subject: [PATCH 004/107] prettier check --- docs/usage/DE_analysis/de_rstudio.md | 14 +++++++------- docs/usage/DE_analysis/interpretation.md | 6 +----- docs/usage/DE_analysis/intro.md | 5 ----- docs/usage/DE_analysis/rnaseq.md | 14 +++----------- docs/usage/DE_analysis/theory.md | 18 +++++++----------- 5 files changed, 18 insertions(+), 39 deletions(-) diff --git a/docs/usage/DE_analysis/de_rstudio.md b/docs/usage/DE_analysis/de_rstudio.md index de58d8bcc..7334fd2fd 100644 --- a/docs/usage/DE_analysis/de_rstudio.md +++ b/docs/usage/DE_analysis/de_rstudio.md @@ -6,7 +6,6 @@ order: 4 In this section of the tutorial, we will guide you through the practical steps necessary to set up the RStudio environment, load the required libraries and data, and execute the DESeq2 analysis. By the end of this section, you will have a fully functional DESeq2 analysis pipeline set up in RStudio, ready to uncover the differentially expressed genes in your dataset. - ## Launching the RStudio environment Once the nf-core/rnaseq pipeline is terminated, the resulting data are stored in the folder `results_star_salmon`. Now, we can analyse the results by running DESeq2 on RStudio. First of all, we need to launch it: @@ -32,18 +31,17 @@ This command will keep the gitpod session active for exactly 2 hours, providing Now come back to our **RStudio session**. - ## Differential Expression Analysis As in all analysis, firstly we need to create a new project: -1) Go to the **File** menu and select **New Project**; +1. Go to the **File** menu and select **New Project**; -2) Select **New Directory**, **New Project**, name the project as shown below and click on **Create Project**; +2. Select **New Directory**, **New Project**, name the project as shown below and click on **Create Project**; ![RNAseq](./img/project_R.png) -3) The new project will be automatically opened in RStudio +3. The new project will be automatically opened in RStudio We can check whether we are in the correct working directory with `getwd()` The path `/workspace/gitpod/training/DE_analysis/` should appear on your console. To store our results in an organized way, we will create a folder named **de_results** using the **New Folder** button in the bottom right panel. We will save all our resulting tables and plots in this folder. Next, go to the **File menu**, select **New File** and then **R Script** to create a script editor in which we will save all commands required for the analysis. In the editor type: @@ -95,6 +93,7 @@ load("/workspace/gitpod/training/results_star_salmon/star_salmon/deseq2_qc/deseq In this tutorial we will analyse the `dds` object generated by running the alignment with **STAR** and the quantification with **Salmon**. Alternatively, a user could choose to analyse the the `dds` object generated by running only **Salmon** for both lightweight alignment and quantification. The file is stored in `/workspace/gitpod/training/results_star_salmon/salmon/deseq2_qc/deseq2.dds.RData`. In DESEq2, the `dds` object is a central data structure that contains the following components: + - `countData`: a matrix of raw count data, where each row represents a gene and each column represents a sample; - `colData`: a data frame containing information about the samples, such as the experimental design, treatment and other relevant metadata; - `design`: a formula specifying the experimental design utilised to estimate the dispersion and the log2foldchange. @@ -194,6 +193,7 @@ dds_final <- DESeq(dds_filtered) The `DESeq()` function is a high-level wrapper that simplifies the process of differential expression analysis by combining multiple steps into a single function call. This makes the workflow more user-friendly and ensures that all necessary preprocessing and statistical steps are executed in the correct order. The key functions that **DESeq2** calls include: + - **estimateSizeFactors**: to normalise the count data; - **estimateDispersion**: to estimate the dispersion; - **nbinomWaldTest**: to perform differential expression test. @@ -221,6 +221,7 @@ rld <- rlog(dds_final, blind = TRUE) ``` The `rlog` and the `vst` transformations have an argument, **blind** that can be set to: + - **TRUE** (default): useful for QC analysis because it re-estimates the dispersion, allowing for comparison of samples in an unbiased manner with respect to experimental conditions; - **FALSE**: the function utilizes the already estimated dispersion, generally applied when differences in counts are expected to be due to the experimental design. @@ -348,7 +349,7 @@ res_viz <- as_tibble(res_viz) %>% write.csv(res_viz, file = "de_results/de_result_table.csv") ``` -In the *Experimental Design* section, we emphasised the importance of estimating the log2 fold change threshold using a statistical power calculation, rather than selecting it arbitrarily. This approach ensures that the chosen threshold is statistically appropriate and tailored to the specifics of the experiment. However, since we are working with simulated data for demonstration purposes, we will use a padj threshold of 0.05 and consider genes with a log2 fold change greater than 1 or lower than -1 as differentially expressed. +In the _Experimental Design_ section, we emphasised the importance of estimating the log2 fold change threshold using a statistical power calculation, rather than selecting it arbitrarily. This approach ensures that the chosen threshold is statistically appropriate and tailored to the specifics of the experiment. However, since we are working with simulated data for demonstration purposes, we will use a padj threshold of 0.05 and consider genes with a log2 fold change greater than 1 or lower than -1 as differentially expressed. ```r #### Extract significant DE genes from the results #### @@ -468,7 +469,6 @@ ggplot(data = res_tb, aes(x = log2FoldChange, y = -log10(padj), col = diffexpres ``` - ## Functional analysis The output of the differential expression analysis is a list of significant DE genes. To uncover the underlying biological mechanisms, various downstream analyses can be performed, such as functional enrichment analysis (identify overrepresented biological processes, molecular functions, cellular components or pathways) and network analysis (group genes based on similar expression patterns to identify novel interactions). To facilitate the interpretation of the resulting list of DE genes, a range of freely available web- and R-based tools can be employed. diff --git a/docs/usage/DE_analysis/interpretation.md b/docs/usage/DE_analysis/interpretation.md index c196472f8..a771d64c3 100644 --- a/docs/usage/DE_analysis/interpretation.md +++ b/docs/usage/DE_analysis/interpretation.md @@ -10,7 +10,6 @@ Once DE genes have been identified, the next crucial step is to interpret the re The results illustrated in this section might show slight variations compared to your runs due to randomness in the STAR algorithm. This randomness arises from using variable seed values and parallel processing, leading to minor differences in results between runs on the same data. These small discrepancies are not biologically significant and may affect counts and subsequent plots (such as PCA and count plots). However, the overall patterns and main findings should remain consistent. While exact reproducibility is ideal, minor variations are acceptable in practice, as long as they do not impact the main conclusions of the study. - ## Quality control plots The first plot we will examine is the Principal Component Analysis (PCA) plot. Since we're working with simulated data, our metadata is relatively simple, consisting of just three variables: sample, condition and replica. In a typical RNA-seq experiment, however, metadata can be complex and encompass a wide range of variables that could contribute to sample variation, such as sex, age and developmental stage. @@ -27,7 +26,6 @@ Remember that to create this plot, we utilized the `dist()` function, so in the Overall, the integration of these plots suggests that we are working with high-quality data and we can confidently proceed to the differential expression analysis. - ## Differential expression results From this point, we will examine plots that are generated after the differential expression analysis. These plots are not quality control (QC) plots, but rather plots that help us to interpret the results. @@ -58,7 +56,7 @@ After the identification of DE genes, it's informative to visualise the expressi ![RNAseq](./img/plotCounts.png) -In our example, post-treatment, we observe a significant increase in the expression of the *ENSG00000142192* gene, highlighting its responsiveness to the experimental conditions. +In our example, post-treatment, we observe a significant increase in the expression of the _ENSG00000142192_ gene, highlighting its responsiveness to the experimental conditions. Finally, we can create a heatmap using the normalised expression counts of DE genes. The resulting heatmap visualises how the expression of significant genes varies across samples. Each row represents a gene, and each column represents a sample. The color intensity in the heatmap reflects the normalised expression levels: red colors indicate higher expression, while blue colors indicate lower expression. @@ -66,7 +64,6 @@ Finally, we can create a heatmap using the normalised expression counts of DE ge By examining the heatmap, we can visually identify the expression patterns of our five significant differentially expressed genes. This visualization allows us to identify how these genes respond to the treatment. The heatmap provides a clear and intuitive way to explore gene expression dynamics. - ## Over Representation Analysis (ORA) Finally, we can attempt to assign biological significance to our differentially expressed genes through Enrichment Analysis (ORA). The ORA analysis identifies specific biological pathways, molecular functions and cellular processes, according to the Gene Ontology (GO) database, that are enriched with our differentially expressed genes. @@ -75,7 +72,6 @@ Finally, we can attempt to assign biological significance to our differentially The enrichment analysis reveals a possible involvement of cellular structures and processes, including "clathrin-coated pit", "dendritic spine", "neuron spine" and "endoplasmic reticulum lumen". These terms suggest a focus on cellular transport, structural integrity and protein processing, especially in neural contexts. This pattern points to pathways related to cellular organization and maintenance, possibly playing an important role in the biological condition under study. - ## Conclusions In this tutorial, we have walked through the steps of performing differential expression analysis using DESeq2, from preparing the data to interpreting the results. We have seen how to identify differentially expressed genes, visualise the results and perform enrichment analysis to gain insights into the biological significance of the results. By following this tutorial, you should now be able to perform differential expression analysis using DESeq2 and interpret the results in the context of your own research question. diff --git a/docs/usage/DE_analysis/intro.md b/docs/usage/DE_analysis/intro.md index 1fa3b4be5..681ec8885 100644 --- a/docs/usage/DE_analysis/intro.md +++ b/docs/usage/DE_analysis/intro.md @@ -19,7 +19,6 @@ By the end of this workshop, you will be able to: Let's get started! - ## Running with Gitpod In order to run this using GitPod, please make sure: @@ -29,17 +28,13 @@ In order to run this using GitPod, please make sure: Now you're all set and can use the following button to launch the service: - [![Open in GitPod](https://img.shields.io/badge/Gitpod-%20Open%20in%20Gitpod-908a85?logo=gitpod)](https://gitpod.io/#https://github.com/lescai-teaching/rnaseq-tutorial) - - ## Additional documentation - You can find detailed documentation on **Nextflow** [here](https://www.nextflow.io/docs/latest/) - You can find additional training on [these pages](https://training.nextflow.io) - ## Credits & Copyright This training material has been written and completed during the [nf-core](https://nf-co.re) Hackathon in Barcellona, 2024, by Lorenzo Sola, Francesco Lescai, Mariangela Santorsola and Victoria Cepeda. The tutorial is aimed at anyone who is interested in using nf-core pipelines for their studies or research activities. diff --git a/docs/usage/DE_analysis/rnaseq.md b/docs/usage/DE_analysis/rnaseq.md index 0500039ca..79eed7edb 100644 --- a/docs/usage/DE_analysis/rnaseq.md +++ b/docs/usage/DE_analysis/rnaseq.md @@ -6,7 +6,6 @@ order: 3 In order to carry out a RNA-Seq analysis we will use the nf-core pipeline [rnaseq](https://nf-co.re/rnaseq/3.14.0). - ## Overview The pipeline is organised following the diffent blocks: pre-processing, alignment (or lightweight alignment) and quantification, post-processing and final QC. @@ -14,10 +13,10 @@ The pipeline is organised following the diffent blocks: pre-processing, alignmen ![RNAseq](./img/nf-core-rnaseq_metro_map_grey.png) In each process, the users can choose among a range of different options. Importantly, the users can decide to follow one of the two different routes in the alignment and quantification step: + - alignment and quantification (stage 2); - pseudoalignment and quantification (stage 3). - ## Experimental Design The number of reads and the number of biological replicates are two critical factors that researchers need to carefully consider during the design of a RNA-seq experiment. While it may seem intuitive that having a large number of reads is always desirable, an excessive number can lead to unnecessary costs and computational burdens, without providing significant improvements in quality of the data. Instead, it is often more beneficial to prioritise the number of biological replicates, as it allows to capture the natural biological variation of the data. Biological replicates involve collecting and sequencing RNA from distinct biological samples (e.g., different individuals, tissues, or time points), helping to detect genuine changes in gene expression. @@ -26,14 +25,12 @@ The number of reads and the number of biological replicates are two critical fac This concept must not be confused with technical replicates that asses the technical variability of the sequencing platform by sequencing the same RNA sample multiple time. -To obtain optimal results, it is crucial to balance the number of biological replicates and sequencing depth. While deeper sequencing improves the detection of lowly expressed genes, it reaches a plateau, beyond which no additional benefits are gained. Statistical power calculations can inform experimental design by estimating the optimal number of reads and replicates required. For instance, this approach helps establish a suitable log2 fold change (log2FC) threshold for the DE analysis. By incorporating multiple biological replicates into the design and optimizing sequencing depth, researchers can enhance the statistical power of the analysis, reducing the number of false positive results and increasing the reliability of the findings. - +To obtain optimal results, it is crucial to balance the number of biological replicates and sequencing depth. While deeper sequencing improves the detection of lowly expressed genes, it reaches a plateau, beyond which no additional benefits are gained. Statistical power calculations can inform experimental design by estimating the optimal number of reads and replicates required. For instance, this approach helps establish a suitable log2 fold change (log2FC) threshold for the DE analysis. By incorporating multiple biological replicates into the design and optimizing sequencing depth, researchers can enhance the statistical power of the analysis, reducing the number of false positive results and increasing the reliability of the findings. ## Library design RNA-seq library design involves critical decisions, including the choice between paired-end and single-end sequencing. Paired-end sequencing provides valuable information on structural variations and transcript isoforms, improving mapping accuracy, especially for longer transcripts and repetitive regions. In contrast, single-end sequencing, where only one end of the fragment is sequenced, can be more cost-effective while still providing high-quality data for gene expression analysis. The decision between paired-end and single-end sequencing ultimately depends on the research question and experimental goals. Paired-end sequencing is preferred for novel transcript identification or isoform characterization, while single-end sequencing is sufficient for gene expression quantification. The type of RNA (e.g., mRNA or total RNA), read length, budget and computational resources can impact the choice. - ## Reference genome nf-core pipelines make use of the Illumina iGenomes collection as [reference genomes](https://nf-co.re/docs/usage/reference_genomes). @@ -44,7 +41,6 @@ One might also need to use custom files: in this case the users might either pro We will follow this specific approach in this tutorial, since the data we will be using have been simulated on chromosome 21 of the Human GRCh38 reference, and we have prepared genome fasta and genome index containing only this chromosome locally. The two files are `/workspace/gitpod/training/data/refs/Homo_sapiens_assembly38_chr21.fa` and `/workspace/gitpod/training/data/refs/Homo_sapiens_assembly38_chr21.fa.fai`, respectively. - ## Reference annoation The reference annotation plays a crucial role in the RNA-seq analysis. Without a high-quality reference annotation, RNA-seq analysis would result in inaccurate or incomplete results. The reference annotation provides a precise guide for aligning sequencing reads to specific genomic regions, allowing to identify genes, transcripts and regulatory elements. This is particularly important for identifying novel transcripts and alternative splicing events. @@ -54,23 +50,21 @@ The reference annotations are vastly out of date with respect to current annotat Similarly to the approach utilised for the genome, in this tutorial we will follow this approach. The annotation files include only the annotated transcripts on chromosome 21 of the Human GRCh38 reference genome and we have already prepared these files locally. The two files are `/workspace/gitpod/training/data/refs/gencode_v29_chr21.gff` and `/workspace/gitpod/training/data/refs/gencode_v29_transcripts_chr21.fa`, respectively. - ## Input files The input data should be provided in a CSV file, according to a format that is largely common for nf-core pipelines. The format is described in the [rnaseq usage page](https://nf-co.re/rnaseq/3.14.0/docs/usage). In the tutorial, the input file is `/workspace/gitpod/training/data/reads/rnaseq_samplesheet.csv`. - ## Running nf-core/rnaseq In the following sections we will: + - prepare our references; - set our computational resources in order to be able to run the pipeline on a gitpod VM; - edit the optional settings; - run the pipeline. - ## Reference and annotation files Following the considerations above, we will first of all edit the `nextflow.config` file in our working directory to add a new genome. @@ -92,7 +86,6 @@ genomes { To speed up the analysis we will include the `star_index` and the `salmon_index` in the config. These files have already been created locally. - ## Computing resources Based on the choices we made when starting up the gitpod environment, we recommend to use the following additional parameters. @@ -106,7 +99,6 @@ params { } ``` - ## Launching the pipeline Now we are ready to launch the pipeline, and we can use the following command line: diff --git a/docs/usage/DE_analysis/theory.md b/docs/usage/DE_analysis/theory.md index 7cfcf7096..e05501be3 100644 --- a/docs/usage/DE_analysis/theory.md +++ b/docs/usage/DE_analysis/theory.md @@ -29,6 +29,7 @@ In the RNA-seq workflow, the alignment step involves mapping sequencing reads to Errors, gaps, regions of poor sequence quality, insertions/deletions (INDELs), as well as duplicated and repetitive regions in the reference sequence, make this step challenging. Addressing these issues by choosing a high-quality reference and an appropriate aligner is essential for accurate results. A crucial component in the alignment step is the [annotation](https://www.ncbi.nlm.nih.gov/genbank/genomes_gff) file, which can be in General Feature Format (GFF) or Gene Transfer Format (GTF). These files contain key information about the location and structure of genes and transcripts, playing a crucial role in accurate mapping and gene expression quantification. Additionally, RNA-seq data often includes reads that span exon-exon junctions, and the annotation files provide information about splice junctions, allowing for the detection of different isoforms. The alignment and quantification steps can follow two different approaches depending on user preferences: + - traditional alignment and quantification; - lightweight alignment and quantification. @@ -52,7 +53,6 @@ Finally, it is often beneficial to filter out genes that are unlikely to show di The `vst` or `rlog` transformations are applied to the normalised counts stored in the `dds` object, which is generated by running either the `DESeq()` or `estimateSizeFactors()` function. Since the estimation of size factors is an early step in the `DESeq()` function, the transformations are applied immediately afterwards. - ### Design Formula The design formula specifies the sources of variation that the statistical model needs to account for. It defines how the counts will be related to the experimental conditions, allowing the software to model the relationship between gene expression and the factors of interest, such as treatment, time points, or batch effects. It is important to specify the main factor of interest as well as additional covariates in the design formula. @@ -75,19 +75,16 @@ In R, the tilde (`~`) is used in formula syntax to specify relationships between The results will not be affected by the order of variables, but the common practice is to specify the main source of variation in the last position of the design formula. - ### Differential Expression Analysis RNA-seq data typically contain a large number of genes with low expression counts, indicating that many genes are expressed at very low levels across samples. At the same time, RNA-seq data exhibit a skewed distribution with a long right tail due to the absence of an upper limit for gene expression levels. This means that while most genes have low to moderate expression levels, a small number are expressed at high levels. Accurate statistical modelling must therefore account for this distribution to avoid misleading conclusions. - ![RNAseq](./img/count_distribution.png) The analysis begins with the input data, which generally consist of a matrix obtained during the alignment and quantification step, summarising the expression levels of the different genes in each sample of the dataset. The rows of the matrix typically correspond to genes, and the columns represent the samples. Another essential prerequisite is a metadata table describing the samples. The core of the differential expression analysis is the `DESeq()` function, a wrapper that streamlines several key steps into a single command. The different functions are listed below: - ![RNAseq](./img/DESeq_function.png) !!! note @@ -96,7 +93,7 @@ The core of the differential expression analysis is the `DESeq()` function, a wr The different steps are explained in detail below: -1) **Normalisation**: since DESeq2 compares counts between sample groups for the same gene, it does not need to adjust for gene length. However, it is essential to account for variations in sequencing depth and RNA composition among samples. To normalise the data, DESeq2 utilises size factors, which correct for these variations in library sizes and RNA composition. +1. **Normalisation**: since DESeq2 compares counts between sample groups for the same gene, it does not need to adjust for gene length. However, it is essential to account for variations in sequencing depth and RNA composition among samples. To normalise the data, DESeq2 utilises size factors, which correct for these variations in library sizes and RNA composition. The size factors are calculated using the **median ratio** method: @@ -111,8 +108,7 @@ The median ratio method assumes that not all genes are differentially expressed, While normalised counts are useful for downstream visualisation of results, they should not be used as input for DESeq2. Instead, DESeq2 requires count data in the form of a matrix of integer values. -2) **Estimate dispersion and gene-wise dispersion**: the dispersion is a measure of how much the variance deviates from the mean. The estimation of dispersion is essential to model the variance of the count data. Importantly, RNA-seq data are characterised by **overdispersion**, where the variance in gene expression levels often exceeds the mean (variance > mean). - +2. **Estimate dispersion and gene-wise dispersion**: the dispersion is a measure of how much the variance deviates from the mean. The estimation of dispersion is essential to model the variance of the count data. Importantly, RNA-seq data are characterised by **overdispersion**, where the variance in gene expression levels often exceeds the mean (variance > mean). ![RNAseq](./img/overdispersion.png) @@ -126,10 +122,10 @@ A key feature of DESeq2's dispersion estimates is their negative correlation wit - **Final dispersion estimates**: DESeq2 refines the gene-wise dispersion by shrinking it towards the fitted curve. The "shrinkage" helps control for overfitting, particularly in genes with low counts or few replicates, and makes the dispersion estimates more reliable. However, genes with exceptionally high dispersion values are not shrunk because they likely deviate from the modelling assumptions, exhibiting elevated variability due to biological or technical factors. Shrinking these values could lead to false positives. -3) **Fitting model and testing**: the initial step in hypothesis testing involves defining a **null hypothesis** for each gene. Here, the null hypothesis states that there is no difference in expression between the two sample groups (LFC == 0). Next, DESeq applies a statistical test to assess whether the null hypothesis is true. -DESeq2 fits a generalised linear model (GLM) to the normalised counts using the calculated size factors and final dispersion estimates. A GLM is a flexible extension of linear regression that models the relationship between a response variable (normalised counts) and predictors (e.g., condition). -A GLM is used because it provides flexibility in modelling RNA-seq data, which has unique characteristics, such as discrete counts and overdispersion (variance exceeding the mean). By using a **negative binomial distribution**, DESeq2's GLM can handle the additional variability in gene expression counts that often occurs in RNA-seq data. -Once the model is fit, coefficients are estimated for each sample group along with their standard errors. These coefficients represent the estimated log2 fold changes between groups and serve as the basis for hypothesis testing, using either a **Wald test** or a **Likelihood Ratio Test (LRT)**, depending on the experimental design: +3. **Fitting model and testing**: the initial step in hypothesis testing involves defining a **null hypothesis** for each gene. Here, the null hypothesis states that there is no difference in expression between the two sample groups (LFC == 0). Next, DESeq applies a statistical test to assess whether the null hypothesis is true. + DESeq2 fits a generalised linear model (GLM) to the normalised counts using the calculated size factors and final dispersion estimates. A GLM is a flexible extension of linear regression that models the relationship between a response variable (normalised counts) and predictors (e.g., condition). + A GLM is used because it provides flexibility in modelling RNA-seq data, which has unique characteristics, such as discrete counts and overdispersion (variance exceeding the mean). By using a **negative binomial distribution**, DESeq2's GLM can handle the additional variability in gene expression counts that often occurs in RNA-seq data. + Once the model is fit, coefficients are estimated for each sample group along with their standard errors. These coefficients represent the estimated log2 fold changes between groups and serve as the basis for hypothesis testing, using either a **Wald test** or a **Likelihood Ratio Test (LRT)**, depending on the experimental design: - **Wald Test**: The Wald test is ideal for simpler experimental designs, such as comparing two conditions (e.g., treated vs untreated). It tests whether the estimated effect size (log2 fold change) of a predictor variable (like treatment) is significantly different from zero. This test provides direct estimates of fold changes with associated p-values, making it computationally efficient for straightforward comparisons. From e86e9a691550bd39778fd204fa1a7242d6f3141c Mon Sep 17 00:00:00 2001 From: Siddhartha Bagaria <1929612+siddharthab@users.noreply.github.com> Date: Tue, 29 Oct 2024 15:07:32 -0700 Subject: [PATCH 005/107] update umicollapse to 1.1.0 --- modules.json | 4 +- modules/nf-core/umicollapse/environment.yml | 2 - modules/nf-core/umicollapse/meta.yml | 94 +++++++++++-------- .../tests/main.nf.test.snap | 22 ++--- 4 files changed, 69 insertions(+), 53 deletions(-) diff --git a/modules.json b/modules.json index 06066cd25..f51a6431a 100644 --- a/modules.json +++ b/modules.json @@ -269,7 +269,7 @@ }, "umicollapse": { "branch": "master", - "git_sha": "18e452a645bd56d1a539f53c7656e3d2915246f7", + "git_sha": "666652151335353eef2fcd58880bcef5bc2928e1", "installed_by": ["bam_dedup_stats_samtools_umicollapse"] }, "umitools/dedup": { @@ -298,7 +298,7 @@ "nf-core": { "bam_dedup_stats_samtools_umicollapse": { "branch": "master", - "git_sha": "ab80a04707104a4baf39341581dfbced5da05479", + "git_sha": "763d4b5c05ffda3ac1ac969dc67f7458cfb2eb1d", "installed_by": ["subworkflows"] }, "bam_dedup_stats_samtools_umitools": { diff --git a/modules/nf-core/umicollapse/environment.yml b/modules/nf-core/umicollapse/environment.yml index 8dbc65dc4..3847980dd 100644 --- a/modules/nf-core/umicollapse/environment.yml +++ b/modules/nf-core/umicollapse/environment.yml @@ -1,7 +1,5 @@ -name: umicollapse channels: - conda-forge - bioconda - - defaults dependencies: - bioconda::umicollapse=1.0.0 diff --git a/modules/nf-core/umicollapse/meta.yml b/modules/nf-core/umicollapse/meta.yml index c1361f9ad..8b366c244 100644 --- a/modules/nf-core/umicollapse/meta.yml +++ b/modules/nf-core/umicollapse/meta.yml @@ -1,58 +1,76 @@ ---- name: "umicollapse" -description: Deduplicate reads based on the mapping co-ordinate and the UMI attached to the read. +description: Deduplicate reads based on the mapping co-ordinate and the UMI attached + to the read. keywords: - umicollapse - deduplication - genomics tools: - "umicollapse": - description: "UMICollapse contains tools for dealing with Unique Molecular Identifiers (UMIs)/Random Molecular Tags (RMTs)." + description: "UMICollapse contains tools for dealing with Unique Molecular Identifiers + (UMIs)/Random Molecular Tags (RMTs)." homepage: "https://github.com/Daniel-Liu-c0deb0t/UMICollapse" documentation: "https://github.com/Daniel-Liu-c0deb0t/UMICollapse" tool_dev_url: "https://github.com/Daniel-Liu-c0deb0t/UMICollapse" doi: "10.7717/peerj.8275" licence: ["MIT"] + identifier: "" input: - - meta: - type: map - description: | - Groovy Map containing sample information - e.g. [ id:'test', single_end:false ] - - bam: - type: file - description: | - BAM file containing reads to be deduplicated via UMIs. - pattern: "*.{bam}" - - bai: - type: file - description: | - BAM index files corresponding to the input BAM file. Optionally can be skipped using [] when using FastQ input. - pattern: "*.{bai}" - - mode: - type: string - description: | - Selects the mode of Umicollapse - either fastq or bam need to be provided. - pattern: "{fastq,bam}" - + - - meta: + type: map + description: | + Groovy Map containing sample information + e.g. [ id:'test', single_end:false ] + - input: + type: file + description: Input bam file + pattern: "*.bam" + - bai: + type: file + description: | + BAM index files corresponding to the input BAM file. Optionally can be skipped using [] when using FastQ input. + pattern: "*.{bai}" + - - mode: + type: string + description: | + Selects the mode of Umicollapse - either fastq or bam need to be provided. + pattern: "{fastq,bam}" output: - - meta: - type: map - description: | - Groovy Map containing sample information - e.g. [ id:'test', single_end:false ] - bam: - type: file - description: BAM file with deduplicated UMIs. - pattern: "*.{bam}" + - meta: + type: map + description: | + Groovy Map containing sample information + e.g. [ id:'test', single_end:false ] + - "*.bam": + type: file + description: BAM file with deduplicated UMIs. + pattern: "*.{bam}" + - fastq: + - meta: + type: map + description: | + Groovy Map containing sample information + e.g. [ id:'test', single_end:false ] + - "*dedup*fastq.gz": + type: file + description: FASTQ file with deduplicated UMIs. + pattern: "*dedup*fastq.gz" - log: - type: file - description: A log file with the deduplication statistics. - pattern: "*_{UMICollapse.log}" + - meta: + type: map + description: | + Groovy Map containing sample information + e.g. [ id:'test', single_end:false ] + - "*_UMICollapse.log": + type: file + description: A log file with the deduplication statistics. + pattern: "*_{UMICollapse.log}" - versions: - type: file - description: File containing software versions - pattern: "versions.yml" + - versions.yml: + type: file + description: File containing software versions + pattern: "versions.yml" authors: - "@CharlotteAnne" - "@chris-cheshire" diff --git a/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/main.nf.test.snap b/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/main.nf.test.snap index e0c32a77e..ccb965763 100644 --- a/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/main.nf.test.snap +++ b/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/main.nf.test.snap @@ -7,15 +7,15 @@ "id": "test", "single_end": false }, - "test.stats:md5,34e1f13bd6e6b78bb8a589560222e343" + "test.stats:md5,498621f92e86d55e4f7ae93170e6e733" ] ] ], "meta": { - "nf-test": "0.8.4", - "nextflow": "23.10.1" + "nf-test": "0.9.0", + "nextflow": "24.04.4" }, - "timestamp": "2024-05-29T07:42:00.089356713" + "timestamp": "2024-09-16T08:04:02.179870196" }, "test_bam_dedup_stats_samtools_umicollapse_flagstats": { "content": [ @@ -47,18 +47,18 @@ ] ], [ - "versions.yml:md5,15f852de3d9e3fb11c786d15873c5d16", + "versions.yml:md5,20605eb79c410c0ed179ba660d82f75b", + "versions.yml:md5,23617661d2c899996bee2b05db027e25", "versions.yml:md5,268e43f34038d4c6146ed050630f95b4", - "versions.yml:md5,543112ba38e0fae7ae41be77cf1e0791", - "versions.yml:md5,7f6271d4e25dc8c739f28a15399787db", - "versions.yml:md5,d241a309a3e0eecfe1d3fe6efa65515c" + "versions.yml:md5,e02a62a393a833778e16542eeed0d148", + "versions.yml:md5,ef00762e264b99ac45713dc0dedf4060" ] ], "meta": { - "nf-test": "0.8.4", - "nextflow": "23.10.1" + "nf-test": "0.9.0", + "nextflow": "24.04.4" }, - "timestamp": "2024-05-29T07:42:00.053427512" + "timestamp": "2024-09-16T08:04:02.126366857" }, "test_bam_dedup_stats_samtools_umicollapse_idxstats": { "content": [ From 28b10ac240bc547f90014cb5c7befdf895d164bf Mon Sep 17 00:00:00 2001 From: Siddhartha Bagaria <1929612+siddharthab@users.noreply.github.com> Date: Tue, 29 Oct 2024 15:15:00 -0700 Subject: [PATCH 006/107] update CHANGELOG --- CHANGELOG.md | 451 +++------------------------------------------------ 1 file changed, 25 insertions(+), 426 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 39e77a767..25d35d657 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -3,6 +3,30 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). +## dev + +### Credits + +Special thanks to the following for their contributions to the release: + +- [Siddhartha Bagaria](https://github.com/siddharthab) + +### Enhancements & fixes + +- [PR #1369](https://github.com/nf-core/rnaseq/pull/1369) - Add umicollapse as an alternative to umi-tools + +### Software dependencies + +| Dependency | Old version | New version | +| -------------- | ----------- | ----------- | +| `UMICollapse` | | 1.1.0 | + +> **NB:** Dependency has been **updated** if both old and new version information is present. +> +> **NB:** Dependency has been **added** if just the new version information is present. +> +> **NB:** Dependency has been **removed** if new version information isn't present. + ## [[3.17.0](https://github.com/nf-core/rnaseq/releases/tag/3.17.0)] - 2024-10-23 ### Credits @@ -981,429 +1005,4 @@ Note, since the pipeline is now using Nextflow DSL2, each process will be run wi ### :warning: Major enhancements - You will need to install Nextflow `>=20.11.0-edge` to run the pipeline. If you are using Singularity, then features introduced in that release now enable the pipeline to directly download Singularity images hosted by Biocontainers as opposed to performing a conversion from Docker images (see [#496](https://github.com/nf-core/rnaseq/issues/496)). -- The previous default of aligning BAM files using STAR and quantifying using featureCounts (`--aligner star`) has been removed. The new default is to align with STAR and quantify using Salmon (`--aligner star_salmon`). - - This decision was made primarily because of the limitations of featureCounts to appropriately quantify gene expression data. Please see [Zhao et al., 2015](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0141910#pone-0141910-t001) and [Soneson et al., 2015](https://f1000research.com/articles/4-1521/v1)). -- For similar reasons, **quantification will not be performed** if using `--aligner hisat2` due to the lack of an appropriate option to calculate accurate expression estimates from HISAT2 derived genomic alignments. - - This pipeline option is still available for those who have a preference for the alignment, QC and other types of downstream analysis compatible with the output of HISAT2. No gene-level quantification results will be generated. - - In a future release we hope to add back quantitation for HISAT2 using different tools. - -### Enhancements & fixes - -- Updated pipeline template to nf-core/tools `1.12.1` -- Bumped Nextflow version `20.07.1` -> `20.11.0-edge` -- Added UCSC `bedClip` module to restrict bedGraph file coordinates to chromosome boundaries -- Check if Bioconda and conda-forge channels are set-up correctly when running with `-profile conda` -- Use `rsem-prepare-reference` and not `gffread` to create transcriptome fasta file -- [[#494](https://github.com/nf-core/rnaseq/issues/494)] - Issue running rnaseq v2.0 (DSL2) with test profile -- [[#496](https://github.com/nf-core/rnaseq/issues/496)] - Direct download of Singularity images via HTTPS -- [[#498](https://github.com/nf-core/rnaseq/issues/498)] - Significantly different versions of STAR in star_rsem (2.7.6a) and star (2.6.1d) -- [[#499](https://github.com/nf-core/rnaseq/issues/499)] - Use of salmon counts for DESeq2 -- [[#500](https://github.com/nf-core/rnaseq/issues/500), [#509](https://github.com/nf-core/rnaseq/issues/509)] - Error with AWS batch params -- [[#511](https://github.com/nf-core/rnaseq/issues/511)] - rsem/star index fails with large genome -- [[#515](https://github.com/nf-core/rnaseq/issues/515)] - Add decoy-aware indexing for salmon -- [[#516](https://github.com/nf-core/rnaseq/issues/516)] - Unexpected error [InvocationTargetException] -- [[#525](https://github.com/nf-core/rnaseq/issues/525)] - sra_ids_to_runinfo.py UnicodeEncodeError -- [[#550](https://github.com/nf-core/rnaseq/issues/525)] - handle samplesheets with replicate=0 - -### Parameters - -| Old parameter | New parameter | -| --------------------------- | -------------------------------------- | -| `--fc_extra_attributes` | `--gtf_extra_attributes` | -|  `--fc_group_features` |  `--gtf_group_features` | -|  `--fc_count_type` |  `--gtf_count_type` | -|  `--fc_group_features_type` |  `--gtf_group_features_type` | -|   |  `--singularity_pull_docker_container` | -|  `--skip_featurecounts` |   | - -> **NB:** Parameter has been **updated** if both old and new parameter information is present. -> **NB:** Parameter has been **added** if just the new parameter information is present. -> **NB:** Parameter has been **removed** if parameter information isn't present. - -### Software dependencies - -Note, since the pipeline is now using Nextflow DSL2, each process will be run with its own [Biocontainer](https://biocontainers.pro/#/registry). This means that on occasion it is entirely possible for the pipeline to be using different versions of the same tool. However, the overall software dependency changes compared to the last release have been listed below for reference. - -| Dependency | Old version | New version | -| ----------------------------------- | ----------- | ----------- | -| `bioconductor-summarizedexperiment` | 1.18.1 | 1.20.0 | -| `bioconductor-tximeta` | 1.6.3 | 1.8.0 | -| `picard` | 2.23.8 | 2.23.9 | -| `requests` | | 2.24.0 | -| `salmon` | 1.3.0 | 1.4.0 | -| `ucsc-bedclip` | | 377 | -| `umi_tools` | 1.0.1 | 1.1.1 | - -> **NB:** Dependency has been **updated** if both old and new version information is present. -> **NB:** Dependency has been **added** if just the new version information is present. -> **NB:** Dependency has been **removed** if version information isn't present. - -## [[2.0](https://github.com/nf-core/rnaseq/releases/tag/2.0)] - 2020-11-12 - -### Major enhancements - -- Pipeline has been re-implemented in [Nextflow DSL2](https://www.nextflow.io/docs/latest/dsl2.html) -- All software containers are now exclusively obtained from [Biocontainers](https://biocontainers.pro/#/registry) -- Added a separate workflow to download FastQ files via SRA, ENA or GEO ids and to auto-create the input samplesheet ([`ENA FTP`](https://ena-docs.readthedocs.io/en/latest/retrieval/file-download.html); see [`--public_data_ids`](https://nf-co.re/rnaseq/parameters#public_data_ids) parameter) -- Added and refined a Groovy `lib/` of functions that include the automatic rendering of parameters defined in the JSON schema for the help and summary log information -- Replace [edgeR](https://bioconductor.org/packages/release/bioc/html/edgeR.html) with [DESeq2](https://bioconductor.org/packages/release/bioc/html/DESeq2.html) for the generation of PCA and heatmaps (also included in the MultiQC report) -- Creation of bigWig coverage files using [BEDTools](https://github.com/arq5x/bedtools2/) and [bedGraphToBigWig](http://hgdownload.soe.ucsc.edu/admin/exe/) -- [[#70](https://github.com/nf-core/rnaseq/issues/70)] - Added new genome mapping and quantification route with [RSEM](https://github.com/deweylab/RSEM) via the `--aligner star_rsem` parameter -- [[#72](https://github.com/nf-core/rnaseq/issues/72)] - Samples skipped due to low alignment reported in the MultiQC report -- [[#73](https://github.com/nf-core/rnaseq/issues/73), [#435](https://github.com/nf-core/rnaseq/pull/435)] - UMI barcode support -- [[#91](https://github.com/nf-core/rnaseq/issues/91)] - Ability to concatenate multiple runs of the same samples via the input samplesheet -- [[#123](https://github.com/nf-core/rnaseq/issues/123)] - The primary input for the pipeline has changed from `--reads` glob to samplesheet `--input`. See [usage docs](https://nf-co.re/rnaseq/docs/usage#introduction). -- [[#197](https://github.com/nf-core/rnaseq/issues/197)] - Samples failing strand-specificity checks reported in the MultiQC report -- [[#227](https://github.com/nf-core/rnaseq/issues/227)] - Removal of ribosomal RNA via [SortMeRNA](https://github.com/biocore/sortmerna) -- [[#419](https://github.com/nf-core/rnaseq/pull/419)] - Add `--additional_fasta` parameter to provide ERCC spike-ins, transgenes such as GFP or CAR-T as additional sequences to align to - -### Other enhancements & fixes - -- Updated pipeline template to nf-core/tools `1.11` -- Optimise MultiQC configuration for faster run-time on huge sample numbers -- Add information about SILVA licensing when removing rRNA to `usage.md` -- Fixed ansi colours for pipeline summary, added summary logs of alignment results -- [[#281](https://github.com/nf-core/rnaseq/issues/281)] - Add nag to cite the pipeline in summary -- [[#302](https://github.com/nf-core/rnaseq/issues/302)] - Fixed MDS plot axis labels -- [[#338](https://github.com/nf-core/rnaseq/issues/338)] - Add option for turning on/off STAR command line option (--sjdbGTFfile) -- [[#344](https://github.com/nf-core/rnaseq/issues/344)] - Added multi-core TrimGalore support -- [[#351](https://github.com/nf-core/rnaseq/issues/351)] - Fixes missing Qualimap parameter `-p` -- [[#353](https://github.com/nf-core/rnaseq/issues/353)] - Fixes an issue where MultiQC fails to run with `--skip_biotype_qc` option -- [[#357](https://github.com/nf-core/rnaseq/issues/357)] - Fixes broken links -- [[#362](https://github.com/nf-core/rnaseq/issues/362)] - Fix error with gzipped annotation file -- [[#384](https://github.com/nf-core/rnaseq/issues/384)] - Changed SortMeRNA reference dbs path to use stable URLs (v4.2.0) -- [[#396](https://github.com/nf-core/rnaseq/issues/396)] - Deterministic mapping for STAR aligner -- [[#412](https://github.com/nf-core/rnaseq/issues/412)] - Fix Qualimap not being passed on correct strand-specificity parameter -- [[#413](https://github.com/nf-core/rnaseq/issues/413)] - Fix STAR unmapped reads not output -- [[#434](https://github.com/nf-core/rnaseq/issues/434)] - Fix typo reported for work-dir -- [[#437](https://github.com/nf-core/rnaseq/issues/434)] - FastQC uses correct number of threads now -- [[#440](https://github.com/nf-core/rnaseq/issues/440)] - Fixed issue where featureCounts process fails when setting `--fc_count_type` to gene -- [[#452](https://github.com/nf-core/rnaseq/issues/452)] - Fix `--gff` input bug -- [[#345](https://github.com/nf-core/rnaseq/pull/345)] - Fixes label name in FastQC process -- [[#391](https://github.com/nf-core/rnaseq/pull/391)] - Make publishDir mode configurable -- [[#431](https://github.com/nf-core/rnaseq/pull/431)] - Update AWS GitHub actions workflow with organization level secrets -- [[#435](https://github.com/nf-core/rnaseq/pull/435)] - Fix a bug where gzipped references were not extracted when `--additional_fasta` was not specified -- [[#435](https://github.com/nf-core/rnaseq/pull/435)] - Fix a bug where merging of RSEM output would fail if only one fastq provided as input -- [[#435](https://github.com/nf-core/rnaseq/pull/435)] - Correct RSEM output name (was saving counts but calling them TPMs; now saving both properly labelled) -- [[#436](https://github.com/nf-core/rnaseq/pull/436)] - Fix a bug where the RSEM reference could not be built -- [[#458](https://github.com/nf-core/rnaseq/pull/458)] - Fix `TMP_DIR` for process MarkDuplicates and Qualimap - -### Parameters - -#### Updated - -| Old parameter | New parameter | -| ----------------------------- | --------------------------- | -| `--reads` | `--input` | -|  `--igenomesIgnore` |  `--igenomes_ignore` | -|  `--removeRiboRNA` |  `--remove_ribo_rna` | -|  `--rRNA_database_manifest` |  `--ribo_database_manifest` | -|  `--save_nonrRNA_reads` |  `--save_non_ribo_reads` | -|  `--saveAlignedIntermediates` |  `--save_align_intermeds` | -|  `--saveReference` |  `--save_reference` | -|  `--saveTrimmed` |  `--save_trimmed` | -|  `--saveUnaligned` |  `--save_unaligned` | -|  `--skipAlignment` |  `--skip_alignment` | -|  `--skipBiotypeQC` |  `--skip_biotype_qc` | -|  `--skipDupRadar` |  `--skip_dupradar` | -|  `--skipFastQC` |  `--skip_fastqc` | -|  `--skipMultiQC` |  `--skip_multiqc` | -|  `--skipPreseq` |  `--skip_preseq` | -|  `--skipQC` |  `--skip_qc` | -|  `--skipQualimap` |  `--skip_qualimap` | -|  `--skipRseQC` |  `--skip_rseqc` | -|  `--skipTrimming` |  `--skip_trimming` | -|  `--stringTieIgnoreGTF` |  `--stringtie_ignore_gtf` | - -#### Added - -- `--additional_fasta` - FASTA file to concatenate to genome FASTA file e.g. containing spike-in sequences -- `--deseq2_vst` - Use vst transformation instead of rlog with DESeq2 -- `--enable_conda` - Run this workflow with Conda. You can also use '-profile conda' instead of providing this parameter -- `--min_mapped_reads` - Minimum percentage of uniquely mapped reads below which samples are removed from further processing -- `--multiqc_title` - MultiQC report title. Printed as page header, used for filename if not otherwise specified -- `--public_data_ids` - File containing SRA/ENA/GEO identifiers one per line in order to download their associated FastQ files -- `--publish_dir_mode` - Method used to save pipeline results to output directory -- `--rsem_index` - Path to directory or tar.gz archive for pre-built RSEM index -- `--rseqc_modules` - Specify the RSeQC modules to run -- `--save_merged_fastq` - Save FastQ files after merging re-sequenced libraries in the results directory -- `--save_umi_intermeds` - If this option is specified, intermediate FastQ and BAM files produced by UMI-tools are also saved in the results directory -- `--skip_bigwig` - Skip bigWig file creation -- `--skip_deseq2_qc` - Skip DESeq2 PCA and heatmap plotting -- `--skip_featurecounts` - Skip featureCounts -- `--skip_markduplicates` - Skip picard MarkDuplicates step -- `--skip_sra_fastq_download` - Only download metadata for public data database ids and don't download the FastQ files -- `--skip_stringtie` - Skip StringTie -- `--star_ignore_sjdbgtf` - See [#338](https://github.com/nf-core/rnaseq/issues/338) -- `--umitools_bc_pattern` - The UMI barcode pattern to use e.g. 'NNNNNN' indicates that the first 6 nucleotides of the read are from the UMI -- `--umitools_extract_method` - UMI pattern to use. Can be either 'string' (default) or 'regex' -- `--with_umi` - Enable UMI-based read deduplication - -#### Removed - -- `--awsqueue` can now be provided via nf-core/configs if using AWS -- `--awsregion` can now be provided via nf-core/configs if using AWS -- `--compressedReference` now auto-detected -- `--markdup_java_options` in favour of updating centrally on nf-core/modules -- `--project` parameter from old NGI template -- `--readPaths` is not required since these are provided from the input samplesheet -- `--sampleLevel` not required -- `--singleEnd` is now auto-detected from the input samplesheet -- `--skipEdgeR` qc not performed by DESeq2 instead -- `--star_memory` in favour of updating centrally on nf-core/modules if required -- Strandedness is now specified at the sample-level via the input samplesheet - - `--forwardStranded` - - `--reverseStranded` - - `--unStranded` - - `--pico` - -### Software dependencies - -Note, since the pipeline is now using Nextflow DSL2, each process will be run with its own [Biocontainer](https://biocontainers.pro/#/registry). This means that on occasion it is entirely possible for the pipeline to be using different versions of the same tool. However, the overall software dependency changes compared to the last release have been listed below for reference. - -| Dependency | Old version | New version | -| ----------------------------------- | ----------- | ----------- | -| `bioconductor-dupradar` | 1.14.0 | 1.18.0 | -| `bioconductor-summarizedexperiment` | 1.14.0 | 1.18.1 | -| `bioconductor-tximeta` | 1.2.2 | 1.6.3 | -| `fastqc` | 0.11.8 | 0.11.9 | -| `gffread` | 0.11.4 | 0.12.1 | -| `hisat2` | 2.1.0 | 2.2.0 | -| `multiqc` | 1.7 | 1.9 | -| `picard` | 2.21.1 | 2.23.8 | -| `qualimap` | 2.2.2c | 2.2.2d | -| `r-base` | 3.6.1 | 4.0.3 | -| `salmon` | 0.14.2 | 1.3.0 | -| `samtools` | 1.9 | 1.10 | -| `sortmerna` | 2.1b | 4.2.0 | -| `stringtie` | 2.0 | 2.1.4 | -| `subread` | 1.6.4 | 2.0.1 | -| `trim-galore` | 0.6.4 | 0.6.6 | -| `bedtools` | - | 2.29.2 | -| `bioconductor-biocparallel` | - | 1.22.0 | -| `bioconductor-complexheatmap` | - | 2.4.2 | -| `bioconductor-deseq2` | - | 1.28.0 | -| `bioconductor-tximport` | - | 1.16.0 | -| `perl` | - | 5.26.2 | -| `python` | - | 3.8.3 | -| `r-ggplot2` | - | 3.3.2 | -| `r-optparse` | - | 1.6.6 | -| `r-pheatmap` | - | 1.0.12 | -| `r-rcolorbrewer` | - | 1.1_2 | -| `rsem` | - | 1.3.3 | -| `ucsc-bedgraphtobigwig` | - | 377 | -| `umi_tools` | - | 1.0.1 | -| `bioconductor-edger` | - | - | -| `deeptools` | - | - | -| `matplotlib` | - | - | -| `r-data.table` | - | - | -| `r-gplots` | - | - | -| `r-markdown` | - | - | - -> **NB:** Dependency has been **updated** if both old and new version information is present. -> **NB:** Dependency has been **added** if just the new version information is present. -> **NB:** Dependency has been **removed** if version information isn't present. - -## [[1.4.2](https://github.com/nf-core/rnaseq/releases/tag/1.4.2)] - 2019-10-18 - -- Minor version release for keeping Git History in sync -- No changes with respect to 1.4.1 on pipeline level - -## [[1.4.1](https://github.com/nf-core/rnaseq/releases/tag/1.4.1)] - 2019-10-17 - -Major novel changes include: - -- Update `igenomes.config` with NCBI `GRCh38` and most recent UCSC genomes -- Set `autoMounts = true` by default for `singularity` profile - -### Pipeline enhancements & fixes - -- Fixed parameter warnings [#316](https://github.com/nf-core/rnaseq/issues/316) and [318](https://github.com/nf-core/rnaseq/issues/318) -- Fixed [#307](https://github.com/nf-core/rnaseq/issues/307) - Confusing Info Printout about GFF and GTF - -## [[1.4](https://github.com/nf-core/rnaseq/releases/tag/1.4)] - 2019-10-15 - -Major novel changes include: - -- Support for Salmon as an alternative method to STAR and HISAT2 -- Several improvements in `featureCounts` handling of types other than `exon`. It is possible now to handle nuclearRNAseq data. Nuclear RNA has un-spliced RNA, and the whole transcript, including the introns, needs to be counted, e.g. by specifying `--fc_count_type transcript`. -- Support for [outputting unaligned data](https://github.com/nf-core/rnaseq/issues/277) to results folders. -- Added options to skip several steps - - Skip trimming using `--skipTrimming` - - Skip BiotypeQC using `--skipBiotypeQC` - - Skip Alignment using `--skipAlignment` to only use pseudoalignment using Salmon - -### Documentation updates - -- Adjust wording of skipped samples [in pipeline output](https://github.com/nf-core/rnaseq/issues/290) -- Fixed link to guidelines [#203](https://github.com/nf-core/rnaseq/issues/203) -- Add `Citation` and `Quick Start` section to `README.md` -- Add in documentation of the `--gff` parameter - -### Reporting Updates - -- Generate MultiQC plots in the results directory [#200](https://github.com/nf-core/rnaseq/issues/200) -- Get MultiQC to save plots as [standalone files](https://github.com/nf-core/rnaseq/issues/183) -- Get MultiQC to write out the software versions in a `.csv` file [#185](https://github.com/nf-core/rnaseq/issues/185) -- Use `file` instead of `new File` to create `pipeline_report.{html,txt}` files, and properly create subfolders - -### Pipeline enhancements & fixes - -- Restore `SummarizedExperimment` object creation in the salmon_merge process avoiding increasing memory with sample size. -- Fix sample names in feature counts and dupRadar to remove suffixes added in other processes -- Removed `genebody_coverage` process [#195](https://github.com/nf-core/rnaseq/issues/195) -- Implemented Pearsons correlation instead of Euclidean distance [#146](https://github.com/nf-core/rnaseq/issues/146) -- Add `--stringTieIgnoreGTF` parameter [#206](https://github.com/nf-core/rnaseq/issues/206) -- Removed unused `stringtie` channels for `MultiQC` -- Integrate changes in `nf-core/tools v1.6` template which resolved [#90](https://github.com/nf-core/rnaseq/issues/90) -- Moved process `convertGFFtoGTF` before `makeSTARindex` [#215](https://github.com/nf-core/rnaseq/issues/215) -- Change all boolean parameters from `snake_case` to `camelCase` and vice versa for value parameters -- Add SM ReadGroup info for QualiMap compatibility[#238](https://github.com/nf-core/rnaseq/issues/238) -- Obtain edgeR + dupRadar version information [#198](https://github.com/nf-core/rnaseq/issues/198) and [#112](https://github.com/nf-core/rnaseq/issues/112) -- Add `--gencode` option for compatibility of Salmon and featureCounts biotypes with GENCODE gene annotations -- Added functionality to accept compressed reference data in the pipeline -- Check that gtf features are on chromosomes that exist in the genome fasta file [#274](https://github.com/nf-core/rnaseq/pull/274) -- Maintain all gff features upon gtf conversion (keeps `gene_biotype` or `gene_type` to make `featureCounts` happy) -- Add SortMeRNA as an optional step to allow rRNA removal [#280](https://github.com/nf-core/rnaseq/issues/280) -- Minimal adjustment of memory and CPU constraints for clusters with locked memory / CPU relation -- Cleaned up usage, `parameters.settings.json` and the `nextflow.config` - -### Dependency Updates - -- Dependency list is now sorted appropriately -- Force matplotlib=3.0.3 - -#### Updated Packages - -- Picard 2.20.0 -> 2.21.1 -- bioconductor-dupradar 1.12.1 -> 1.14.0 -- bioconductor-edger 3.24.3 -> 3.26.5 -- gffread 0.9.12 -> 0.11.4 -- trim-galore 0.6.1 -> 0.6.4 -- gffread 0.9.12 -> 0.11.4 -- rseqc 3.0.0 -> 3.0.1 -- R-Base 3.5 -> 3.6.1 - -#### Added / Removed Packages - -- Dropped CSVtk in favor of Unix's simple `cut` and `paste` utilities -- Added Salmon 0.14.2 -- Added TXIMeta 1.2.2 -- Added SummarizedExperiment 1.14.0 -- Added SortMeRNA 2.1b -- Add tximport and summarizedexperiment dependency [#171](https://github.com/nf-core/rnaseq/issues/171) -- Add Qualimap dependency [#202](https://github.com/nf-core/rnaseq/issues/202) - -## [[1.3](https://github.com/nf-core/rnaseq/releases/tag/1.3)] - 2019-03-26 - -### Pipeline Updates - -- Added configurable options to specify group attributes for featureCounts [#144](https://github.com/nf-core/rnaseq/issues/144) -- Added support for RSeqC 3.0 [#148](https://github.com/nf-core/rnaseq/issues/148) -- Added a `parameters.settings.json` file for use with the new `nf-core launch` helper tool. -- Centralized all configuration profiles using [nf-core/configs](https://github.com/nf-core/configs) -- Fixed all centralized configs [for offline usage](https://github.com/nf-core/rnaseq/issues/163) -- Hide %dup in [multiqc report](https://github.com/nf-core/rnaseq/issues/150) -- Add option for Trimming NextSeq data properly ([@jburos work](https://github.com/jburos)) - -### Bug fixes - -- Fixing HISAT2 Index Building for large reference genomes [#153](https://github.com/nf-core/rnaseq/issues/153) -- Fixing HISAT2 BAM sorting using more memory than available on the system -- Fixing MarkDuplicates memory consumption issues following [#179](https://github.com/nf-core/rnaseq/pull/179) -- Use `file` instead of `new File` to create the `pipeline_report.{html,txt}` files to avoid creating local directories when outputting to AWS S3 folders -- Fix SortMeRNA default rRNA db paths specified in assets/rrna-db-defaults.txt - -### Dependency Updates - -- RSeQC 2.6.4 -> 3.0.0 -- Picard 2.18.15 -> 2.20.0 -- r-data.table 1.11.4 -> 1.12.2 -- bioconductor-edger 3.24.1 -> 3.24.3 -- r-markdown 0.8 -> 0.9 -- csvtk 0.15.0 -> 0.17.0 -- stringtie 1.3.4 -> 1.3.6 -- subread 1.6.2 -> 1.6.4 -- gffread 0.9.9 -> 0.9.12 -- multiqc 1.6 -> 1.7 -- deeptools 3.2.0 -> 3.2.1 -- trim-galore 0.5.0 -> 0.6.1 -- qualimap 2.2.2b -- matplotlib 3.0.3 -- r-base 3.5.1 - -## [[1.2](https://github.com/nf-core/rnaseq/releases/tag/1.2)] - 2018-12-12 - -### Pipeline updates - -- Removed some outdated documentation about non-existent features -- Config refactoring and code cleaning -- Added a `--fcExtraAttributes` option to specify more than ENSEMBL gene names in `featureCounts` -- Remove legacy rseqc `strandRule` config code. [#119](https://github.com/nf-core/rnaseq/issues/119) -- Added STRINGTIE ballgown output to results folder [#125](https://github.com/nf-core/rnaseq/issues/125) -- HiSAT index build now requests `200GB` memory, enough to use the exons / splice junction option for building. - - Added documentation about the `--hisatBuildMemory` option. -- BAM indices are stored and re-used between processes [#71](https://github.com/nf-core/rnaseq/issues/71) - -### Bug Fixes - -- Fixed conda bug which caused problems with environment resolution due to changes in bioconda [#113](https://github.com/nf-core/rnaseq/issues/113) -- Fixed wrong gffread command line [#117](https://github.com/nf-core/rnaseq/issues/117) -- Added `cpus = 1` to `workflow summary process` [#130](https://github.com/nf-core/rnaseq/issues/130) - -## [[1.1](https://github.com/nf-core/rnaseq/releases/tag/1.1)] - 2018-10-05 - -### Pipeline updates - -- Wrote docs and made minor tweaks to the `--skip_qc` and associated options -- Removed the depreciated `uppmax-modules` config profile -- Updated the `hebbe` config profile to use the new `withName` syntax too -- Use new `workflow.manifest` variables in the pipeline script -- Updated minimum nextflow version to `0.32.0` - -### Bug Fixes - -- [#77](https://github.com/nf-core/rnaseq/issues/77): Added back `executor = 'local'` for the `workflow_summary_mqc` -- [#95](https://github.com/nf-core/rnaseq/issues/95): Check if task.memory is false instead of null -- [#97](https://github.com/nf-core/rnaseq/issues/97): Resolved edge-case where numeric sample IDs are parsed as numbers causing some samples to be incorrectly overwritten. - -## [[1.0](https://github.com/nf-core/rnaseq/releases/tag/1.0)] - 2018-08-20 - -This release marks the point where the pipeline was moved from [SciLifeLab/NGI-RNAseq](https://github.com/SciLifeLab/NGI-RNAseq) -over to the new [nf-core](http://nf-co.re/) community, at [nf-core/rnaseq](https://github.com/nf-core/rnaseq). - -View the previous changelog at [SciLifeLab/NGI-RNAseq/CHANGELOG.md](https://github.com/SciLifeLab/NGI-RNAseq/blob/master/CHANGELOG.md) - -In addition to porting to the new nf-core community, the pipeline has had a number of major changes in this version. -There have been 157 commits by 16 different contributors covering 70 different files in the pipeline: 7,357 additions and 8,236 deletions! - -In summary, the main changes are: - -- Rebranding and renaming throughout the pipeline to nf-core -- Updating many parts of the pipeline config and style to meet nf-core standards -- Support for GFF files in addition to GTF files - - Just use `--gff` instead of `--gtf` when specifying a file path -- New command line options to skip various quality control steps -- More safety checks when launching a pipeline - - Several new sanity checks - for example, that the specified reference genome exists -- Improved performance with memory usage (especially STAR and Picard) -- New BigWig file outputs for plotting coverage across the genome -- Refactored gene body coverage calculation, now much faster and using much less memory -- Bugfixes in the MultiQC process to avoid edge cases where it wouldn't run -- MultiQC report now automatically attached to the email sent when the pipeline completes -- New testing method, with data on GitHub - - Now run pipeline with `-profile test` instead of using bash scripts -- Rewritten continuous integration tests with Travis CI -- New explicit support for Singularity containers -- Improved MultiQC support for DupRadar and featureCounts - - Now works for all users instead of just NGI Stockholm -- New configuration for use on AWS batch -- Updated config syntax to support latest versions of Nextflow -- Built-in support for a number of new local HPC systems - - CCGA, GIS, UCT HEX, updates to UPPMAX, CFC, BINAC, Hebbe, c3se -- Slightly improved documentation (more updates to come) -- Updated software packages - -...and many more minor tweaks. - -Thanks to everyone who has worked on this 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+0100 Subject: [PATCH 008/107] modify md files --- docs/usage/DEanalysis/de_rstudio.md | 114 +++++++++---------- docs/usage/DEanalysis/{intro.md => index.md} | 12 +- docs/usage/DEanalysis/interpretation.md | 34 +++--- docs/usage/DEanalysis/rnaseq.md | 30 ++--- docs/usage/DEanalysis/theory.md | 57 +++++----- 5 files changed, 126 insertions(+), 121 deletions(-) rename docs/usage/DEanalysis/{intro.md => index.md} (85%) diff --git a/docs/usage/DEanalysis/de_rstudio.md b/docs/usage/DEanalysis/de_rstudio.md index 7334fd2fd..78be7c10b 100644 --- a/docs/usage/DEanalysis/de_rstudio.md +++ b/docs/usage/DEanalysis/de_rstudio.md @@ -1,11 +1,8 @@ ---- -order: 4 ---- - # Differential Analysis with DESeq2 In this section of the tutorial, we will guide you through the practical steps necessary to set up the RStudio environment, load the required libraries and data, and execute the DESeq2 analysis. By the end of this section, you will have a fully functional DESeq2 analysis pipeline set up in RStudio, ready to uncover the differentially expressed genes in your dataset. + ## Launching the RStudio environment Once the nf-core/rnaseq pipeline is terminated, the resulting data are stored in the folder `results_star_salmon`. Now, we can analyse the results by running DESeq2 on RStudio. First of all, we need to launch it: @@ -31,17 +28,18 @@ This command will keep the gitpod session active for exactly 2 hours, providing Now come back to our **RStudio session**. + ## Differential Expression Analysis As in all analysis, firstly we need to create a new project: -1. Go to the **File** menu and select **New Project**; +1) Go to the **File** menu and select **New Project**; -2. Select **New Directory**, **New Project**, name the project as shown below and click on **Create Project**; +2) Select **New Directory**, **New Project**, name the project as shown below and click on **Create Project**; -![RNAseq](./img/project_R.png) +![R_project](./img/project_R.png) -3. The new project will be automatically opened in RStudio +3) The new project will be automatically opened in RStudio We can check whether we are in the correct working directory with `getwd()` The path `/workspace/gitpod/training/DE_analysis/` should appear on your console. To store our results in an organized way, we will create a folder named **de_results** using the **New Folder** button in the bottom right panel. We will save all our resulting tables and plots in this folder. Next, go to the **File menu**, select **New File** and then **R Script** to create a script editor in which we will save all commands required for the analysis. In the editor type: @@ -52,7 +50,7 @@ To store our results in an organized way, we will create a folder named **de_res and save the file as **de_script.R**. From now on, each command described in the tutorial can be added to your script. The resulting working directory should look like this: -![RNAseq](./img/workdir_RStudio.png) +![Workdir](./img/workdir_RStudio.png) The analysis requires several R packages. To utilise them, we need to load the following libraries: @@ -92,8 +90,7 @@ load("/workspace/gitpod/training/results_star_salmon/star_salmon/deseq2_qc/deseq In this tutorial we will analyse the `dds` object generated by running the alignment with **STAR** and the quantification with **Salmon**. Alternatively, a user could choose to analyse the the `dds` object generated by running only **Salmon** for both lightweight alignment and quantification. The file is stored in `/workspace/gitpod/training/results_star_salmon/salmon/deseq2_qc/deseq2.dds.RData`. -In DESEq2, the `dds` object is a central data structure that contains the following components: - +In DESEq2, the `dds` object is a central data structure that contains the following components: - `countData`: a matrix of raw count data, where each row represents a gene and each column represents a sample; - `colData`: a data frame containing information about the samples, such as the experimental design, treatment and other relevant metadata; - `design`: a formula specifying the experimental design utilised to estimate the dispersion and the log2foldchange. @@ -110,7 +107,7 @@ colData(dds) # to check the sample info design(dds) # to check the design formula ``` -The `colData` and the `design` are the ones created within the nfcore/rnaseq pipeline and must be reorganised prior to the analysis. With the following commands we will create our metadata starting from the info stored in the `dds`. We will rename the column of the `colData`, we will ensure that the rownames of the metadata are present in the same order as the column names and finally we will update the `colData` of the `dds` object with our newly created metadata. +The `colData` and the `design` are the ones created within the nfcore/rnaseq pipeline and must be reorganised prior to the analysis. With the following commands we will create our metadata starting from the info stored in the `dds`. We will rename the column of the `colData`, we will ensure that the rownames of the metadata are present in the same order as the column names and finally we will update the `colData` of the `dds` object with our newly created metadata. ```r #### Creation of metadata starting from the dds colData #### @@ -151,7 +148,7 @@ Now that everything is setted up, we can proceed to generate a new DESeq2 object dds_new <- DESeqDataSet(dds, design = ~ condition) -# dds inspection +# dds inspection head(counts(dds_new)) # to check the raw counts @@ -162,7 +159,7 @@ design(dds_new) # to check the design formula Comparing the structure of the newly created dds (`dds_new`) with the one automatically produced by the pipeline (`dds`), we can observe the differences: -![RNAseq](./img/dds_comparison.png) +![Comparison_dds](./img/dds_comparison.png) Before running the different steps of the analysis, a good practice consists in pre-filtering the genes to remove those with very low counts. This is useful to improve computional efficiency and enhance interpretability. In general, it is reasonable to keep only genes with a sum counts of at least 10 for a minimal number of 3 samples: @@ -171,15 +168,15 @@ Before running the different steps of the analysis, a good practice consists in # Select a minimal number of samples = 3 -smallestGroupSize <- 3 +smallestGroupSize <- 3 # Select genes with a sum counts of at least 10 in 3 samples -keep <- rowSums(counts(dds_new) >= 10) >= smallestGroupSize +keep <- rowSums(counts(dds_new) >= 10) >= smallestGroupSize # Keep only the genes that pass the threshold -dds_filtered <- dds_new[keep,] +dds_filtered <- dds_new[keep,] ``` Now, it is time to run the differential expression analysis with the `DESeq()` function: @@ -192,8 +189,7 @@ dds_final <- DESeq(dds_filtered) The `DESeq()` function is a high-level wrapper that simplifies the process of differential expression analysis by combining multiple steps into a single function call. -This makes the workflow more user-friendly and ensures that all necessary preprocessing and statistical steps are executed in the correct order. The key functions that **DESeq2** calls include: - +This makes the workflow more user-friendly and ensures that all necessary preprocessing and statistical steps are executed in the correct order. The key functions that **DESeq2** calls include: - **estimateSizeFactors**: to normalise the count data; - **estimateDispersion**: to estimate the dispersion; - **nbinomWaldTest**: to perform differential expression test. @@ -221,12 +217,11 @@ rld <- rlog(dds_final, blind = TRUE) ``` The `rlog` and the `vst` transformations have an argument, **blind** that can be set to: - - **TRUE** (default): useful for QC analysis because it re-estimates the dispersion, allowing for comparison of samples in an unbiased manner with respect to experimental conditions; - **FALSE**: the function utilizes the already estimated dispersion, generally applied when differences in counts are expected to be due to the experimental design. Next, we perform Principal Component Analysis (PCA) to explore the data. DESeq2 provides a built-in function, `plotPCA()`, which uses [ggplot2](https://ggplot2.tidyverse.org) for visualisation, taking the `rld` (or the `vst`) object as input. -Since the **treatment** is the principal condition of interest in our metadata, we will use the `condition` information from our metadata to plot the PCA: +Since the **treatment** is the principal condition of interest in our metadata, we will use the `condition` information from our metadata to plot the PCA: ```r #### Plot PCA #### @@ -246,7 +241,7 @@ sampleDists <- dist(t(assay(rld))) # Calculate pairwise distances between sampl # Convert distances to a matrix -sampleDistMatrix <- as.matrix(sampleDists) +sampleDistMatrix <- as.matrix(sampleDists) # Set the row and column names of the distance matrix @@ -260,11 +255,11 @@ colors <- colorRampPalette(rev(brewer.pal(9, "Greens")))(255) # function from RC # Create the heatmap -pheatmap(sampleDistMatrix, - clustering_distance_rows = sampleDists, - clustering_distance_cols = sampleDists, - col = colors, - fontsize_col = 8, +pheatmap(sampleDistMatrix, + clustering_distance_rows = sampleDists, + clustering_distance_cols = sampleDists, + col = colors, + fontsize_col = 8, fontsize_row = 8) ``` @@ -291,10 +286,10 @@ normalised_counts$gene <- rownames(counts(dds_final)) # Relocate the gene column to the first position -normalised_counts <- normalised_counts %>% +normalised_counts <- normalised_counts %>% relocate(gene, .before = control_rep1) -# Save normalised counts +# Save normalised counts write.csv(normalised_counts, file = "de_results/normalised_counts.csv") ``` @@ -308,7 +303,7 @@ The `results()` function in DESeq2 is used to extract the results of the DE anal - **pvalue**: the p-value from the Wald test indicates the probability of observing the measured difference in gene expression (log2 fold change) by chance, assuming no true difference exists (null hypothesis). A low p-value suggests that the observed expression change between samples is unlikely due to random chance, so we can reject the null hypothesis; - **padj**: the adjusted p-value, which takes into account multiple testing corrections, using the Benjamini-Hochberg method to control the false discovery rate; -By default, the `results()` function returns the results for all genes in the analysis with an adjusted p-value below a specific FDR cutoff, set by default to 0.1. This threshold can be modified with the parameter `alpha`. The `results()` function can also be customised to filter the results based on certain criteria (log2 fold change or padj) or to set a specific contrast. A contrast is a specific comparison between two or more levels of a factor, such as the comparison between the treatment and control groups. The order of the contrast names determines the direction of the fold change that is reported in the results. Specifically, the first level of the contrast is the condition of interest and the second level is the reference level. +By default, the `results()` function returns the results for all genes in the analysis with an adjusted p-value below a specific FDR cutoff, set by default to 0.1. This threshold can be modified with the parameter `alpha`. The `results()` function can also be customised to filter the results based on certain criteria (log2 fold change or padj) or to set a specific contrast. A contrast is a specific comparison between two or more levels of a factor, such as the comparison between the treatment and control groups. The order of the contrast names determines the direction of the fold change that is reported in the results. Specifically, the first level of the contrast is the condition of interest and the second level is the reference level. Notice that in this tutorial the contrast is already correctly specified. ```r @@ -318,15 +313,15 @@ res <- results(dds_final) # Visualise the results -head(res) +head(res) -# Summarise the results showing the number of tested genes (genes with non-zero total read count), the genes up- and down-regulated at the selected threshold (alpha) and the number of genes excluded by the multiple testing due to a low mean count +# Summarise the results showing the number of tested genes (genes with non-zero total read count), the genes up- and down-regulated at the selected threshold (alpha) and the number of genes excluded by the multiple testing due to a low mean count summary(res) # DESeq2 function to extract the name of the contrast -resultsNames(dds_final) +resultsNames(dds_final) # res <- results(dds, contrast = c("design_formula", "condition_of_interest", "reference_condition")) # Command to set the contrast, if necessary @@ -341,37 +336,37 @@ res_viz$gene <- rownames(res) # Convert the results to a tibble for easier manipulation and relocate the gene column to the first position -res_viz <- as_tibble(res_viz) %>% +res_viz <- as_tibble(res_viz) %>% relocate(gene, .before = baseMean) -# Save the results table +# Save the results table write.csv(res_viz, file = "de_results/de_result_table.csv") ``` -In the _Experimental Design_ section, we emphasised the importance of estimating the log2 fold change threshold using a statistical power calculation, rather than selecting it arbitrarily. This approach ensures that the chosen threshold is statistically appropriate and tailored to the specifics of the experiment. However, since we are working with simulated data for demonstration purposes, we will use a padj threshold of 0.05 and consider genes with a log2 fold change greater than 1 or lower than -1 as differentially expressed. +In the *Experimental Design* section, we emphasised the importance of estimating the log2 fold change threshold using a statistical power calculation, rather than selecting it arbitrarily. This approach ensures that the chosen threshold is statistically appropriate and tailored to the specifics of the experiment. However, since we are working with simulated data for demonstration purposes, we will use a padj threshold of 0.05 and consider genes with a log2 fold change greater than 1 or lower than -1 as differentially expressed. ```r #### Extract significant DE genes from the results #### # Filter the results to include only significantly differentially expressed genes with an adjusted p-value (padj) less than 0.05 and a log2foldchange greater than 1 or less than -1 -resSig <- subset(res_viz, padj < 0.05 & abs(log2FoldChange) > 1) +resSig <- subset(res_viz, padj < 0.05 & abs(log2FoldChange) > 1) # Convert the results to a tibble for easier manipulation and relocate the 'Gene' column to be the first column -resSig <- as_tibble(resSig) %>% - relocate(gene, .before = baseMean) +resSig <- as_tibble(resSig) %>% + relocate(gene, .before = baseMean) # Order the significant genes by their adjusted p-value (padj) in ascending order -resSig <- resSig[order(resSig$padj),] +resSig <- resSig[order(resSig$padj),] # Display the final results table of significant genes -resSig +resSig -# Save the significant DE genes +# Save the significant DE genes write.csv(resSig, file = "de_results/sig_de_genes.csv") ``` @@ -385,7 +380,7 @@ Now that we have obtained the results of the differential expression analysis, i plotMA(res, ylim = c(-2,2)) ``` - + - **counts plot**: it plots the normalised counts for a single gene across the different conditions in your experiment. It’s particularly useful for visualising the expression levels of specific genes of interest and comparing them across sample groups. ```r @@ -404,14 +399,14 @@ Notiche that this plot is based on the normalised counts. significant_genes <- resSig[, 1] -# Extract normalised counts for significant genes from the normalised counts matrix and convert the "gene" column to row names +# Extract normalised counts for significant genes from the normalised counts matrix and convert the "gene" column to row names -significant_counts <- inner_join(normalised_counts, significant_genes, by = "gene") %>% +significant_counts <- inner_join(normalised_counts, significant_genes, by = "gene") %>% column_to_rownames("gene") # Create the heatmap using pheatmap -pheatmap(significant_counts, +pheatmap(significant_counts, cluster_rows = TRUE, fontsize = 8, scale = "row", @@ -427,48 +422,49 @@ pheatmap(significant_counts, # Convert the results to a tibble and add a column indicating differential expression status -res_tb <- as_tibble(res) %>% +res_tb <- as_tibble(res) %>% mutate(diffexpressed = case_when( - log2FoldChange > 1 & padj < 0.05 ~ 'upregulated', + log2FoldChange > 1 & padj < 0.05 ~ 'upregulated', log2FoldChange < -1 & padj < 0.05 ~ 'downregulated', TRUE ~ 'not_de')) # Add a new column with gene names -res_tb$gene <- rownames(res) +res_tb$gene <- rownames(res) # Relocate the 'gene' column to be the first column -res_tb <- res_tb %>% +res_tb <- res_tb %>% relocate(gene, .before = baseMean) # Order the table by adjusted p-value (padj) and add a new column for gene labels -res_tb <- res_tb %>% arrange(padj) %>% +res_tb <- res_tb %>% arrange(padj) %>% mutate(genelabels = "") - + # Label the top 5 most significant genes res_tb$genelabels[1:5] <- res_tb$gene[1:5] # Create a volcano plot using ggplot2 -ggplot(data = res_tb, aes(x = log2FoldChange, y = -log10(padj), col = diffexpressed)) + - geom_point(size = 0.6) + +ggplot(data = res_tb, aes(x = log2FoldChange, y = -log10(padj), col = diffexpressed)) + + geom_point(size = 0.6) + geom_text_repel(aes(label = genelabels), size = 2.5, max.overlaps = Inf) + - ggtitle("DE genes treatment versus control") + + ggtitle("DE genes treatment versus control") + geom_vline(xintercept = c(-1, 1), col = "black", linetype = 'dashed', linewidth = 0.2) + geom_hline(yintercept = -log10(0.05), col = "black", linetype = 'dashed', linewidth = 0.2) + theme(plot.title = element_text(size = rel(1.25), hjust = 0.5), axis.title = element_text(size = rel(1))) + - scale_color_manual(values = c("upregulated" = "red", - "downregulated" = "blue", + scale_color_manual(values = c("upregulated" = "red", + "downregulated" = "blue", "not_de" = "grey")) + labs(color = 'DE genes') + xlim(-3,5) ``` + ## Functional analysis The output of the differential expression analysis is a list of significant DE genes. To uncover the underlying biological mechanisms, various downstream analyses can be performed, such as functional enrichment analysis (identify overrepresented biological processes, molecular functions, cellular components or pathways) and network analysis (group genes based on similar expression patterns to identify novel interactions). To facilitate the interpretation of the resulting list of DE genes, a range of freely available web- and R-based tools can be employed. @@ -534,7 +530,7 @@ go_enrich <- enrichGO( # Create a bar plot of the top enriched GO terms barplot <- barplot( - go_enrich, + go_enrich, title = "Enrichment analysis barplot", font.size = 8 ) @@ -550,4 +546,4 @@ dotplot <- dotplot( # Combine the bar plot and dot plot into a single plot grid plot_grid(barplot, dotplot, nrow = 2) -``` +``` \ No newline at end of file diff --git a/docs/usage/DEanalysis/intro.md b/docs/usage/DEanalysis/index.md similarity index 85% rename from docs/usage/DEanalysis/intro.md rename to docs/usage/DEanalysis/index.md index 681ec8885..8bab2769b 100644 --- a/docs/usage/DEanalysis/intro.md +++ b/docs/usage/DEanalysis/index.md @@ -1,5 +1,6 @@ --- -order: 1 +enableToc: false +title: "nf-core/rnaseq tutorial" --- ## Welcome @@ -19,6 +20,7 @@ By the end of this workshop, you will be able to: Let's get started! + ## Running with Gitpod In order to run this using GitPod, please make sure: @@ -28,17 +30,21 @@ In order to run this using GitPod, please make sure: Now you're all set and can use the following button to launch the service: + [![Open in GitPod](https://img.shields.io/badge/Gitpod-%20Open%20in%20Gitpod-908a85?logo=gitpod)](https://gitpod.io/#https://github.com/lescai-teaching/rnaseq-tutorial) + + ## Additional documentation - You can find detailed documentation on **Nextflow** [here](https://www.nextflow.io/docs/latest/) - You can find additional training on [these pages](https://training.nextflow.io) + ## Credits & Copyright -This training material has been written and completed during the [nf-core](https://nf-co.re) Hackathon in Barcellona, 2024, by Lorenzo Sola, Francesco Lescai, Mariangela Santorsola and Victoria Cepeda. The tutorial is aimed at anyone who is interested in using nf-core pipelines for their studies or research activities. +This training material has been written and completed during the [nf-core](https://nf-co.re) Hackathon in Barcellona, 2024, by Lorenzo Sola, Francesco Lescai and Mariangela Santorsola, with special thanks to Victoria Cepeda for her contributions to the tutorial's revision. The tutorial is aimed at anyone who is interested in using nf-core pipelines for their studies or research activities. The Docker image and Gitpod environment used in this repository have been created by [Seqera](https://seqera.io) but have been made open-source ([CC BY-NC-ND](https://creativecommons.org/licenses/by-nc-nd/4.0/)) for the community. -All examples and descriptions are licensed under the [Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License](http://creativecommons.org/licenses/by-nc-nd/4.0/). +All examples and descriptions are licensed under the [Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License](http://creativecommons.org/licenses/by-nc-nd/4.0/). \ No newline at end of file diff --git a/docs/usage/DEanalysis/interpretation.md b/docs/usage/DEanalysis/interpretation.md index a771d64c3..7ed680ec2 100644 --- a/docs/usage/DEanalysis/interpretation.md +++ b/docs/usage/DEanalysis/interpretation.md @@ -1,7 +1,3 @@ ---- -order: 5 ---- - # Interpretation Once DE genes have been identified, the next crucial step is to interpret the results. This involves the inspection of tables and plots generated during the analysis to understand the biological significance of the data. In this part of the tutorial, we will delve into the results by discussing the significant DE genes and we will explore various plots generated during the analysis. @@ -10,26 +6,28 @@ Once DE genes have been identified, the next crucial step is to interpret the re The results illustrated in this section might show slight variations compared to your runs due to randomness in the STAR algorithm. This randomness arises from using variable seed values and parallel processing, leading to minor differences in results between runs on the same data. These small discrepancies are not biologically significant and may affect counts and subsequent plots (such as PCA and count plots). However, the overall patterns and main findings should remain consistent. While exact reproducibility is ideal, minor variations are acceptable in practice, as long as they do not impact the main conclusions of the study. + ## Quality control plots -The first plot we will examine is the Principal Component Analysis (PCA) plot. Since we're working with simulated data, our metadata is relatively simple, consisting of just three variables: sample, condition and replica. In a typical RNA-seq experiment, however, metadata can be complex and encompass a wide range of variables that could contribute to sample variation, such as sex, age and developmental stage. +The first plot we will examine is the Principal Component Analysis (PCA) plot. Since we're working with simulated data, our metadata is relatively simple, consisting of just three variables: sample, condition and replica. In a typical RNA-seq experiment, however, metadata can be complex and encompass a wide range of variables that could contribute to sample variation, such as sex, age and developmental stage. -![RNAseq](./img/PCA.png) +![PCA](./img/PCA.png) By plotting the PCA on the PC1 and PC2 axes, using `condition` as the main variable of interest, we can quickly identify the primary source of variation in our data. By accounting for this variation in our design model, we should be able to detect more differentially expressed genes related to `condition`. When working with real data, it's often useful to plot the data using different variables to explore how much variation is explained by the first two PCs. Depending on the results, it may be informative to examine variation on additional PC axes, such as PC3 and PC4, to gain a more comprehensive understanding of the data. Next, we will examine the hierarchical clustering plot to explore the relationships between samples based on their gene expression profiles. The heatmap is organized such that samples with similar expression profiles are close to each other, allowing us to identify patterns in the data. -![RNAseq](./img/hierarchical_clustering.png) +![Cluster](./img/hierarchical_clustering.png) -Remember that to create this plot, we utilized the `dist()` function, so in the legend on the right, a value of 0 corresponds to high correlation, while a value of 5 corresponds to very low correlation. Similar to PCA, we can see that samples tend to cluster together according to `condition`, indeed we can observe a high degree of correlation between the three control samples and between the three treated samples. +Remember that to create this plot, we utilized the `dist()` function, so in the legend on the right, a value of 0 corresponds to high correlation, while a value of 5 corresponds to very low correlation. Similar to PCA, we can see that samples tend to cluster together according to `condition`, indeed we can observe a high degree of correlation between the three control samples and between the three treated samples. Overall, the integration of these plots suggests that we are working with high-quality data and we can confidently proceed to the differential expression analysis. + ## Differential expression results -From this point, we will examine plots that are generated after the differential expression analysis. These plots are not quality control (QC) plots, but rather plots that help us to interpret the results. -After running the `results()` function, a good way to start to have an idea about the results is to look at the MA plot. +From this point, we will examine plots that are generated after the differential expression analysis. These plots are not quality control (QC) plots, but rather plots that help us to interpret the results. +After running the `results()` function, a good way to start to have an idea about the results is to look at the MA plot. ![RNAseq](./img/MA_plot.png) @@ -48,30 +46,32 @@ ENSG00000156282 481.7624 1.095272 0.2969594 3.688289 To gain a comprehensive overview of the transcriptional profile, the volcano plot represents a highly informative tool. -![RNAseq](./img/volcanoplot.png) +![Volcano_plot](./img/volcanoplot.png) The treatment induced differential expression in five genes, with one downregulated and four upregulated. This plot visually represents the numerical results reported in the table above. After the identification of DE genes, it's informative to visualise the expression of specific genes of interest. The `plotCounts()` function applied directly on the `dds` object allows us to examine individual gene expression profiles without accessing the full `res` object. -![RNAseq](./img/plotCounts.png) +![Counts](./img/plotCounts.png) -In our example, post-treatment, we observe a significant increase in the expression of the _ENSG00000142192_ gene, highlighting its responsiveness to the experimental conditions. +In our example, post-treatment, we observe a significant increase in the expression of the *ENSG00000142192* gene, highlighting its responsiveness to the experimental conditions. Finally, we can create a heatmap using the normalised expression counts of DE genes. The resulting heatmap visualises how the expression of significant genes varies across samples. Each row represents a gene, and each column represents a sample. The color intensity in the heatmap reflects the normalised expression levels: red colors indicate higher expression, while blue colors indicate lower expression. -![RNAseq](./img/heatmap_DE_genes.png) +![Heatmap](./img/heatmap_de_genes.png) By examining the heatmap, we can visually identify the expression patterns of our five significant differentially expressed genes. This visualization allows us to identify how these genes respond to the treatment. The heatmap provides a clear and intuitive way to explore gene expression dynamics. + ## Over Representation Analysis (ORA) -Finally, we can attempt to assign biological significance to our differentially expressed genes through Enrichment Analysis (ORA). The ORA analysis identifies specific biological pathways, molecular functions and cellular processes, according to the Gene Ontology (GO) database, that are enriched with our differentially expressed genes. +Finally, we can attempt to assign biological significance to our differentially expressed genes through **Enrichment Analysis (ORA)**. The ORA analysis identifies specific biological pathways, molecular functions and cellular processes, according to the **Gene Ontology (GO)** database, that are enriched within our differentially expressed genes. -![RNAseq](./img/enrichment_plot.png) +![Enrichment](./img/enrichment_plot.png) The enrichment analysis reveals a possible involvement of cellular structures and processes, including "clathrin-coated pit", "dendritic spine", "neuron spine" and "endoplasmic reticulum lumen". These terms suggest a focus on cellular transport, structural integrity and protein processing, especially in neural contexts. This pattern points to pathways related to cellular organization and maintenance, possibly playing an important role in the biological condition under study. + ## Conclusions -In this tutorial, we have walked through the steps of performing differential expression analysis using DESeq2, from preparing the data to interpreting the results. We have seen how to identify differentially expressed genes, visualise the results and perform enrichment analysis to gain insights into the biological significance of the results. By following this tutorial, you should now be able to perform differential expression analysis using DESeq2 and interpret the results in the context of your own research question. +In this tutorial, we have walked through the steps of performing differential expression analysis using DESeq2, from preparing the data to interpreting the results. We have seen how to identify differentially expressed genes, visualise the results and perform enrichment analysis to gain insights into the biological significance of the results. By following this tutorial, you should now be able to perform differential expression analysis using DESeq2 and interpret the results in the context of your own research question. \ No newline at end of file diff --git a/docs/usage/DEanalysis/rnaseq.md b/docs/usage/DEanalysis/rnaseq.md index 79eed7edb..05ffcd439 100644 --- a/docs/usage/DEanalysis/rnaseq.md +++ b/docs/usage/DEanalysis/rnaseq.md @@ -1,70 +1,72 @@ ---- -order: 3 ---- - # The nf-core/rnaseq pipeline In order to carry out a RNA-Seq analysis we will use the nf-core pipeline [rnaseq](https://nf-co.re/rnaseq/3.14.0). + ## Overview The pipeline is organised following the diffent blocks: pre-processing, alignment (or lightweight alignment) and quantification, post-processing and final QC. -![RNAseq](./img/nf-core-rnaseq_metro_map_grey.png) +![Metromap](./img/nf-core-rnaseq_metro_map_grey.png) In each process, the users can choose among a range of different options. Importantly, the users can decide to follow one of the two different routes in the alignment and quantification step: - - alignment and quantification (stage 2); -- pseudoalignment and quantification (stage 3). +- pseudoalignment and quantification (stage 3). + ## Experimental Design -The number of reads and the number of biological replicates are two critical factors that researchers need to carefully consider during the design of a RNA-seq experiment. While it may seem intuitive that having a large number of reads is always desirable, an excessive number can lead to unnecessary costs and computational burdens, without providing significant improvements in quality of the data. Instead, it is often more beneficial to prioritise the number of biological replicates, as it allows to capture the natural biological variation of the data. Biological replicates involve collecting and sequencing RNA from distinct biological samples (e.g., different individuals, tissues, or time points), helping to detect genuine changes in gene expression. +The number of reads and the number of biological replicates are two critical factors that researchers need to carefully consider during the design of a RNA-seq experiment. While it may seem intuitive that having a large number of reads is always desirable, an excessive number can lead to unnecessary costs and computational burdens, without providing significant improvements in quality of the data. Instead, it is often more beneficial to prioritise the number of biological replicates, as it allows to capture the natural biological variation of the data. Biological replicates involve collecting and sequencing RNA from distinct biological samples (e.g., different individuals, tissues, or time points), helping to detect genuine changes in gene expression. !!! note This concept must not be confused with technical replicates that asses the technical variability of the sequencing platform by sequencing the same RNA sample multiple time. -To obtain optimal results, it is crucial to balance the number of biological replicates and sequencing depth. While deeper sequencing improves the detection of lowly expressed genes, it reaches a plateau, beyond which no additional benefits are gained. Statistical power calculations can inform experimental design by estimating the optimal number of reads and replicates required. For instance, this approach helps establish a suitable log2 fold change (log2FC) threshold for the DE analysis. By incorporating multiple biological replicates into the design and optimizing sequencing depth, researchers can enhance the statistical power of the analysis, reducing the number of false positive results and increasing the reliability of the findings. +To obtain optimal results, it is crucial to balance the number of biological replicates and sequencing depth. While deeper sequencing improves the detection of lowly expressed genes, it reaches a plateau, beyond which no additional benefits are gained. Statistical power calculations can inform experimental design by estimating the optimal number of reads and replicates required. For instance, this approach helps establish a suitable log2 fold change (log2FC) threshold for the DE analysis. By incorporating multiple biological replicates into the design and optimizing sequencing depth, researchers can enhance the statistical power of the analysis, reducing the number of false positive results and increasing the reliability of the findings. + ## Library design RNA-seq library design involves critical decisions, including the choice between paired-end and single-end sequencing. Paired-end sequencing provides valuable information on structural variations and transcript isoforms, improving mapping accuracy, especially for longer transcripts and repetitive regions. In contrast, single-end sequencing, where only one end of the fragment is sequenced, can be more cost-effective while still providing high-quality data for gene expression analysis. The decision between paired-end and single-end sequencing ultimately depends on the research question and experimental goals. Paired-end sequencing is preferred for novel transcript identification or isoform characterization, while single-end sequencing is sufficient for gene expression quantification. The type of RNA (e.g., mRNA or total RNA), read length, budget and computational resources can impact the choice. + ## Reference genome nf-core pipelines make use of the Illumina iGenomes collection as [reference genomes](https://nf-co.re/docs/usage/reference_genomes). Before starting the analysis, the users might want to check whether the genome they need is part of this collection. -They also might want to consider downloading the reference locally, when running on premises: this would be useful for multiple runs and to speed up the analysis. In this case the parameter `--igenomes_base` might be used to pass the root directory of the downloaded references. +They also might want to consider downloading the reference locally, when running on premises: this would be useful for multiple runs and to speed up the analysis. In this case the parameter `--igenomes_base` might be used to pass the root directory of the downloaded references. One might also need to use custom files: in this case the users might either provide specific parameters at command line (`--fasta` option followed by the genome of choiche), or create a config file adding a new section to the `genome` object. See [here](https://nf-co.re/docs/usage/reference_genomes#custom-genomes) for more details. We will follow this specific approach in this tutorial, since the data we will be using have been simulated on chromosome 21 of the Human GRCh38 reference, and we have prepared genome fasta and genome index containing only this chromosome locally. The two files are `/workspace/gitpod/training/data/refs/Homo_sapiens_assembly38_chr21.fa` and `/workspace/gitpod/training/data/refs/Homo_sapiens_assembly38_chr21.fa.fai`, respectively. + ## Reference annoation The reference annotation plays a crucial role in the RNA-seq analysis. Without a high-quality reference annotation, RNA-seq analysis would result in inaccurate or incomplete results. The reference annotation provides a precise guide for aligning sequencing reads to specific genomic regions, allowing to identify genes, transcripts and regulatory elements. This is particularly important for identifying novel transcripts and alternative splicing events. nf-core pipelines make use of the Illumina iGenomes collection also as [reference annotation](https://nf-co.re/docs/usage/reference_genomes). -The reference annotations are vastly out of date with respect to current annotations and miss certain features. So, the general recommendation is to download a newest annotation version compatible with the genome. A user can utilize the `--gtf` or the `--gff` options to specify the annottation files of choiche, or create a config file adding a new section to the `genome` object. +The reference annotations are vastly out of date with respect to current annotations and miss certain features. So, the general recommendation is to download a newest annotation version compatible with the genome. A user can utilize the `--gtf` or the `--gff` options to specify the annottation files of choiche, or create a config file adding a new section to the `genome` object. Similarly to the approach utilised for the genome, in this tutorial we will follow this approach. The annotation files include only the annotated transcripts on chromosome 21 of the Human GRCh38 reference genome and we have already prepared these files locally. The two files are `/workspace/gitpod/training/data/refs/gencode_v29_chr21.gff` and `/workspace/gitpod/training/data/refs/gencode_v29_transcripts_chr21.fa`, respectively. + ## Input files The input data should be provided in a CSV file, according to a format that is largely common for nf-core pipelines. The format is described in the [rnaseq usage page](https://nf-co.re/rnaseq/3.14.0/docs/usage). In the tutorial, the input file is `/workspace/gitpod/training/data/reads/rnaseq_samplesheet.csv`. + ## Running nf-core/rnaseq In the following sections we will: - - prepare our references; - set our computational resources in order to be able to run the pipeline on a gitpod VM; - edit the optional settings; - run the pipeline. + ## Reference and annotation files Following the considerations above, we will first of all edit the `nextflow.config` file in our working directory to add a new genome. @@ -86,6 +88,7 @@ genomes { To speed up the analysis we will include the `star_index` and the `salmon_index` in the config. These files have already been created locally. + ## Computing resources Based on the choices we made when starting up the gitpod environment, we recommend to use the following additional parameters. @@ -99,6 +102,7 @@ params { } ``` + ## Launching the pipeline Now we are ready to launch the pipeline, and we can use the following command line: @@ -123,4 +127,4 @@ nextflow run nf-core/rnaseq -r 3.12.0 \ ``` Notice that we will run the pipeline with STAR as aligner and Salmon in alignment-based mode to quantify gene expression. We will also run the pipeline with Salmon in mapping-based mode to perform a lightweight alignment and quantification. -The `skip` parameters were inserted to reduce the running time. +The `skip` parameters were inserted to reduce the running time. \ No newline at end of file diff --git a/docs/usage/DEanalysis/theory.md b/docs/usage/DEanalysis/theory.md index e05501be3..2a00d18b2 100644 --- a/docs/usage/DEanalysis/theory.md +++ b/docs/usage/DEanalysis/theory.md @@ -1,7 +1,3 @@ ---- -order: 2 ---- - # RNAseq Analysis Before we dive into the nf-core pipeline for analysing RNA-sequencing data, it's worth looking at some theoretical aspects of RNA-seq. @@ -18,7 +14,7 @@ In the scheme, we can identify three key phases in the workflow: data pre-proces Depending on the user's needs, the workflow can include additional downstream analyses such as functional enrichment analysis, co-expression analysis, and integration with other omics data. -## Pre-processing +## Pre-processing The pre-processing of sequencing reads from RNA-seq data is a critical step to ensure the quality and accuracy of downstream analysis. The raw reads obtained from the sequencer are stored as [FASTQ](https://en.wikipedia.org/wiki/FASTQ_format) files, which contain both the sequence data and quality scores. The initial processing step involves evaluating the quality of the reads and identifying potential issues such as adapter contamination, sequencing errors, or low-quality bases. The presence of adapters (short DNA sequences ligated to the ends of DNA fragments during library preparation) is detected through comparison with known adapter sequences or by using algorithms that identify adapter-like sequences, and removed in a process known as **read trimming**. Next, reads containing contaminants (genomic DNA and/or rRNA) and those with low-quality bases are filtered out. Finally, the quality of the filtered reads is checked again to ensure their suitability for downstream processing. @@ -29,7 +25,6 @@ In the RNA-seq workflow, the alignment step involves mapping sequencing reads to Errors, gaps, regions of poor sequence quality, insertions/deletions (INDELs), as well as duplicated and repetitive regions in the reference sequence, make this step challenging. Addressing these issues by choosing a high-quality reference and an appropriate aligner is essential for accurate results. A crucial component in the alignment step is the [annotation](https://www.ncbi.nlm.nih.gov/genbank/genomes_gff) file, which can be in General Feature Format (GFF) or Gene Transfer Format (GTF). These files contain key information about the location and structure of genes and transcripts, playing a crucial role in accurate mapping and gene expression quantification. Additionally, RNA-seq data often includes reads that span exon-exon junctions, and the annotation files provide information about splice junctions, allowing for the detection of different isoforms. The alignment and quantification steps can follow two different approaches depending on user preferences: - - traditional alignment and quantification; - lightweight alignment and quantification. @@ -39,7 +34,9 @@ Traditional alignment tools are time-consuming and require substantial computati ## Differential expression (DE) analysis with DESeq2 -Differential expression analysis is a statistical method for comparing gene expression levels between different experimental conditions, such as disease vs healthy (e.g., tumour tissue vs healthy tissue), treatment vs control (e.g., a sample treated with a specific stimulus, drug, or compound vs an untreated sample), and tissue vs tissue (e.g., brain vs heart). Differential expression analysis aims to assess, for each gene, whether the observed differences in expression between groups are statistically significant, accounting for the variation observed within groups (replicates). This part of the analysis is typically performed in R using various packages that have been developed, such as [DESeq2](https://bioconductor.org/packages/release/bioc/html/DESeq2.html), [edgeR](https://bioconductor.org/packages/release/bioc/html/edgeR.html), and [limma](https://bioconductor.org/packages/release/bioc/html/limma.html). This tutorial focuses on **DESeq2**, a popular R package known for its robustness. For more detailed information, see the [DESeq2 vignette](https://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html). A typical workflow is outlined in the flowchart below: +Differential expression analysis is a statistical method for comparing gene expression levels between different experimental conditions, such as disease vs healthy (e.g., tumour tissue vs healthy tissue), treatment vs control (e.g., a sample treated with a specific stimulus, drug, or compound vs an untreated sample), and tissue vs tissue (e.g., brain vs heart). Differential expression analysis aims to assess, for each gene, whether the observed differences in expression between groups are statistically significant, accounting for the variation observed within groups (replicates). This part of the analysis is typically performed in R using various packages that have been developed, such as [DESeq2](https://bioconductor.org/packages/release/bioc/html/DESeq2.html), [edgeR](https://bioconductor.org/packages/release/bioc/html/edgeR.html), and [limma](https://bioconductor.org/packages/release/bioc/html/limma.html). This tutorial focuses on **DESeq2**, a popular R package known for its robustness. For more detailed information, see the [DESeq2 vignette](https://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html). + +The analysis begins with the input data, which generally consist of a matrix obtained during the alignment and quantification step, summarising the expression levels of the different genes in each sample of the dataset. The rows of the matrix typically correspond to genes, and the columns represent the samples. Another essential prerequisite is a metadata table describing the samples. ### Quality Control @@ -51,11 +48,12 @@ Finally, it is often beneficial to filter out genes that are unlikely to show di !!! note - The `vst` or `rlog` transformations are applied to the normalised counts stored in the `dds` object, which is generated by running either the `DESeq()` or `estimateSizeFactors()` function. Since the estimation of size factors is an early step in the `DESeq()` function, the transformations are applied immediately afterwards. + The `vst` or `rlog` transformations are applied to the normalised counts stored in the `dds` object, which is generated by running either the `DESeq()` or `estimateSizeFactors()` function. Since the estimation of size factors is an early step in the `DESeq()` function, the transformations are generally applied immediately afterwards. + ### Design Formula -The design formula specifies the sources of variation that the statistical model needs to account for. It defines how the counts will be related to the experimental conditions, allowing the software to model the relationship between gene expression and the factors of interest, such as treatment, time points, or batch effects. It is important to specify the main factor of interest as well as additional covariates in the design formula. +The design formula specifies the sources of variation that the statistical model needs to account for. It defines how the counts will be related to the experimental conditions, allowing the software to model the relationship between gene expression and the factors of interest, such as treatment, time points or batch effects. It is important to specify the main factor of interest as well as additional covariates in the design formula. A well-defined design formula can account for potential confounding variables. For instance, if the experiment includes multiple conditions or batches, specifying these in the design helps to isolate the effect of the primary variable of interest on gene expression. An example is provided below: @@ -69,22 +67,24 @@ design = ~ condition design = ~ sex + developmental_stage + condition ``` -In R, the tilde (`~`) is used in formula syntax to specify relationships between variables in statistical models. Here, it indicates that gene counts (dependent variable) will be modelled as a function of the variables specified (predictors). - !!! note - The results will not be affected by the order of variables, but the common practice is to specify the main source of variation in the last position of the design formula. + In R, the tilde (`~`) is used in formula syntax to specify relationships between variables in statistical models. Here, it indicates that gene counts (dependent variable) will be modelled as a function of the specified variables (predictors). + + +The results will not be affected by the order of variables but the common practice is to specify the main source of variation in the last position of the design formula. + ### Differential Expression Analysis RNA-seq data typically contain a large number of genes with low expression counts, indicating that many genes are expressed at very low levels across samples. At the same time, RNA-seq data exhibit a skewed distribution with a long right tail due to the absence of an upper limit for gene expression levels. This means that while most genes have low to moderate expression levels, a small number are expressed at high levels. Accurate statistical modelling must therefore account for this distribution to avoid misleading conclusions. -![RNAseq](./img/count_distribution.png) -The analysis begins with the input data, which generally consist of a matrix obtained during the alignment and quantification step, summarising the expression levels of the different genes in each sample of the dataset. The rows of the matrix typically correspond to genes, and the columns represent the samples. Another essential prerequisite is a metadata table describing the samples. +![RNAseq](./img/count_distribution.png) The core of the differential expression analysis is the `DESeq()` function, a wrapper that streamlines several key steps into a single command. The different functions are listed below: + ![RNAseq](./img/DESeq_function.png) !!! note @@ -93,7 +93,7 @@ The core of the differential expression analysis is the `DESeq()` function, a wr The different steps are explained in detail below: -1. **Normalisation**: since DESeq2 compares counts between sample groups for the same gene, it does not need to adjust for gene length. However, it is essential to account for variations in sequencing depth and RNA composition among samples. To normalise the data, DESeq2 utilises size factors, which correct for these variations in library sizes and RNA composition. +1) **Normalisation**: since DESeq2 compares counts between sample groups for the same gene, it does not need to adjust for gene length. However, it is essential to account for variations in sequencing depth and RNA composition among samples. To normalise the data, DESeq2 utilises size factors, which correct for these variations in library sizes and RNA composition. The size factors are calculated using the **median ratio** method: @@ -102,39 +102,38 @@ The size factors are calculated using the **median ratio** method: - **Determine size factors**: For each sample, take the median of these ratios (column-wise) to derive the size factors; - **Normalise counts**: Divide each raw count by the corresponding sample size factor to generate normalised counts. -The median ratio method assumes that not all genes are differentially expressed, so large outlier genes will not negatively influence the size factors. This approach is robust to imbalances in up-/down-regulation and can handle large numbers of differentially expressed genes. - !!! note While normalised counts are useful for downstream visualisation of results, they should not be used as input for DESeq2. Instead, DESeq2 requires count data in the form of a matrix of integer values. -2. **Estimate dispersion and gene-wise dispersion**: the dispersion is a measure of how much the variance deviates from the mean. The estimation of dispersion is essential to model the variance of the count data. Importantly, RNA-seq data are characterised by **overdispersion**, where the variance in gene expression levels often exceeds the mean (variance > mean). +2) **Estimate dispersion and gene-wise dispersion**: the dispersion is a measure of how much the variance deviates from the mean. The estimation of dispersion is essential to model the variance of the count data. Importantly, RNA-seq data are characterised by **overdispersion**, where the variance in gene expression levels often exceeds the mean (variance > mean). + ![RNAseq](./img/overdispersion.png) -DESeq2 addresses this issue by employing the **negative binomial distribution**, which generalises the Poisson distribution by introducing an additional dispersion parameter. This parameter quantifies the extra variability present in RNA-seq data, providing a more realistic representation than the Poisson distribution, which assumes equal mean and variance. DESeq2 starts by estimating the **common dispersion**, a single estimate of dispersion applicable to all genes in the dataset. This estimate provides a baseline for variability across all genes in the dataset. Next, DESeq2 estimates **gene-wise dispersion**, a separate estimate of dispersion for each individual gene, taking into account that different genes may exhibit varying levels of expression variability due to biological differences. The dispersion parameter (α) is related to the mean (μ) and variance of the data, as described by the equation: +DESeq2 addresses this issue by employing the **negative binomial distribution**, which generalises the Poisson distribution by introducing an additional dispersion parameter. This parameter quantifies the extra variability present in RNA-seq data, providing a more realistic representation than the Poisson distribution, which assumes equal mean and variance. DESeq2 starts by estimating the **common dispersion**, a single estimate of dispersion applicable to all genes in the dataset. This estimate provides a baseline for variability across all genes in the dataset. Next, DESeq2 estimates **gene-wise dispersion**, a separate estimate of dispersion for each individual gene, taking into account that different genes may exhibit varying levels of expression variability due to biological differences. +The dispersion parameter (α) is related to the mean (μ) and variance of the data, as described by the equation: `Var = μ + α ⋅ μ²` A key feature of DESeq2's dispersion estimates is their negative correlation with the mean and positive correlation with variance. Genes with low expression have higher dispersion values, while genes with high expression tend to have lower dispersion. Additionally, genes sharing similar mean expression levels can exhibit different dispersion estimates based on their variance. To improve the accuracy of dispersion estimates, DESeq2 assumes that genes with similar expression profiles share similar dispersion patterns and leverages this information to refine the estimates. -- **Mean-dispersion relationship**: this process, known as dispersion fitting, models the relationship between the mean expression level of a gene and its dispersion. In this process, DESeq2 identifies a trend in the dispersion estimates across genes. The fitted curve, typically a smooth curve, describes how dispersion changes as a function of the mean expression level. +3) **Mean-dispersion relationship**: this process, known as dispersion fitting, models the relationship between the mean expression level of a gene and its dispersion. In this process, DESeq2 identifies a trend in the dispersion estimates across genes. The fitted curve, typically a smooth curve, describes how dispersion changes as a function of the mean expression level. -- **Final dispersion estimates**: DESeq2 refines the gene-wise dispersion by shrinking it towards the fitted curve. The "shrinkage" helps control for overfitting, particularly in genes with low counts or few replicates, and makes the dispersion estimates more reliable. However, genes with exceptionally high dispersion values are not shrunk because they likely deviate from the modelling assumptions, exhibiting elevated variability due to biological or technical factors. Shrinking these values could lead to false positives. +4) **Final dispersion estimates**: DESeq2 refines the gene-wise dispersion by shrinking it towards the fitted curve. The "shrinkage" helps control for overfitting, particularly in genes with low counts or few replicates, and makes the dispersion estimates more reliable. However, genes with exceptionally high dispersion values are not shrunk because they likely deviate from the modelling assumptions, exhibiting elevated variability due to biological or technical factors. Shrinking these values could lead to false positives. -3. **Fitting model and testing**: the initial step in hypothesis testing involves defining a **null hypothesis** for each gene. Here, the null hypothesis states that there is no difference in expression between the two sample groups (LFC == 0). Next, DESeq applies a statistical test to assess whether the null hypothesis is true. - DESeq2 fits a generalised linear model (GLM) to the normalised counts using the calculated size factors and final dispersion estimates. A GLM is a flexible extension of linear regression that models the relationship between a response variable (normalised counts) and predictors (e.g., condition). - A GLM is used because it provides flexibility in modelling RNA-seq data, which has unique characteristics, such as discrete counts and overdispersion (variance exceeding the mean). By using a **negative binomial distribution**, DESeq2's GLM can handle the additional variability in gene expression counts that often occurs in RNA-seq data. - Once the model is fit, coefficients are estimated for each sample group along with their standard errors. These coefficients represent the estimated log2 fold changes between groups and serve as the basis for hypothesis testing, using either a **Wald test** or a **Likelihood Ratio Test (LRT)**, depending on the experimental design: +5) **Fitting model and testing**: the initial step in hypothesis testing involves defining a **null hypothesis** for each gene. The null hypothesis states that there is no difference in expression between the tested sample groups (LFC == 0). Next, DESeq applies a statistical test to assess whether the null hypothesis is true. +DESeq2 fits a **generalised linear model (GLM)** to the normalised counts using the calculated size factors and final dispersion estimates. A GLM is a flexible extension of linear regression that models the relationship between a response variable (normalised counts) and predictors (e.g., condition). +By using a **negative binomial distribution**, DESeq2's GLM can handle the additional variability in gene expression counts that often occurs in RNA-seq data.Once the model is fit, coefficients are estimated for each sample group along with their standard errors. These coefficients represent the estimated log2 fold changes between groups and serve as the basis for hypothesis testing, using either a **Wald test** or a **Likelihood Ratio Test (LRT)**, depending on the experimental design: - **Wald Test**: The Wald test is ideal for simpler experimental designs, such as comparing two conditions (e.g., treated vs untreated). It tests whether the estimated effect size (log2 fold change) of a predictor variable (like treatment) is significantly different from zero. This test provides direct estimates of fold changes with associated p-values, making it computationally efficient for straightforward comparisons. - + - **Likelihood Ratio Test (LRT)**: The LRT is more suitable for complex experimental designs involving multiple variables (e.g., time points, treatments and batches). It compares the fit of two nested models: one with the factor of interest (e.g., treatment) and one without it to assess whether including that factor significantly improves model fit. This approach allows DESeq2 to account for confounding variables and to isolate the effect of specific variables on gene expression, offering flexibility for multi-factor analyses. -When performing multiple tests, such as in the case of RNA-seq data where thousands of genes are tested, the risk of false positives increases. To account for this, DESeq2 employs multiple test correction methods (the Benjamini-Hochberg procedure is the default) to adjust the p-values and control the false discovery rate (FDR). The FDR is the expected proportion of false positives among the identified significant results. +When performing multiple tests, such as in the case of RNA-seq data where thousands of genes are tested, the risk of false positives increases. To account for this, DESeq2 employs multiple test correction methods (the Benjamini-Hochberg procedure is the default) to adjust the p-values and control the false discovery rate (FDR). !!! note - For example, by setting the FDR cutoff to < 0.05, 5% of genes identified as differentially expressed are expected to be false positives. For instance, if 400 genes are identified as differentially expressed with an FDR cutoff of 0.05, you would expect 20 of them to be false positives. + The FDR is the expected proportion of false positives among the identified significant results. For example, by setting the FDR cutoff to < 0.05, 5% of genes identified as differentially expressed are expected to be false positives. For instance, if 400 genes are identified as differentially expressed with an FDR cutoff of 0.05, you would expect 20 of them to be false positives. -After identifying DE genes using DESeq2, it is essential to interpret the biological significance of these genes through functional analysis. This involves examining the roles of the differentially expressed genes in various biological processes, molecular functions, and pathways, providing insights into the underlying mechanisms driving the observed changes in gene expression. This interpretation can help in discovering pathways involved in disease or identifying potential therapeutic targets. Different tools are available to carry out these functional analyses, such as [Gene Ontology](https://geneontology.org), [Reactome](https://reactome.org/), [KEGG](https://www.genome.jp/kegg), [clusterProfiler](https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html), [g:Profiler](https://biit.cs.ut.ee/gprofiler), and [WikiPathways](https://www.wikipathways.org). +After identifying DE genes using DESeq2, it is essential to interpret the biological significance of these genes through functional analysis. This involves examining the roles of the differentially expressed genes in various biological processes, molecular functions and pathways, providing insights into the underlying mechanisms driving the observed changes in gene expression. This interpretation can help in discovering pathways involved in disease or identifying potential therapeutic targets. Different tools are available to carry out these functional analyses, such as [Gene Ontology](https://geneontology.org), [Reactome](https://reactome.org/), [KEGG](https://www.genome.jp/kegg), [clusterProfiler](https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html), [g:Profiler](https://biit.cs.ut.ee/gprofiler) and [WikiPathways](https://www.wikipathways.org). From e8d80612e89c9289853d3171f6ec182a1956263b Mon Sep 17 00:00:00 2001 From: LorenzoS96 <47034913+LorenzoS96@users.noreply.github.com> Date: Thu, 31 Oct 2024 13:45:20 +0100 Subject: [PATCH 009/107] prettier check --- docs/usage/DEanalysis/de_rstudio.md | 104 ++++++++++++------------ docs/usage/DEanalysis/index.md | 7 +- docs/usage/DEanalysis/interpretation.md | 18 ++-- docs/usage/DEanalysis/rnaseq.md | 24 ++---- docs/usage/DEanalysis/theory.md | 32 +++----- 5 files changed, 81 insertions(+), 104 deletions(-) diff --git a/docs/usage/DEanalysis/de_rstudio.md b/docs/usage/DEanalysis/de_rstudio.md index 78be7c10b..89a258c3a 100644 --- a/docs/usage/DEanalysis/de_rstudio.md +++ b/docs/usage/DEanalysis/de_rstudio.md @@ -2,7 +2,6 @@ In this section of the tutorial, we will guide you through the practical steps necessary to set up the RStudio environment, load the required libraries and data, and execute the DESeq2 analysis. By the end of this section, you will have a fully functional DESeq2 analysis pipeline set up in RStudio, ready to uncover the differentially expressed genes in your dataset. - ## Launching the RStudio environment Once the nf-core/rnaseq pipeline is terminated, the resulting data are stored in the folder `results_star_salmon`. Now, we can analyse the results by running DESeq2 on RStudio. First of all, we need to launch it: @@ -28,18 +27,17 @@ This command will keep the gitpod session active for exactly 2 hours, providing Now come back to our **RStudio session**. - ## Differential Expression Analysis As in all analysis, firstly we need to create a new project: -1) Go to the **File** menu and select **New Project**; +1. Go to the **File** menu and select **New Project**; -2) Select **New Directory**, **New Project**, name the project as shown below and click on **Create Project**; +2. Select **New Directory**, **New Project**, name the project as shown below and click on **Create Project**; ![R_project](./img/project_R.png) -3) The new project will be automatically opened in RStudio +3. The new project will be automatically opened in RStudio We can check whether we are in the correct working directory with `getwd()` The path `/workspace/gitpod/training/DE_analysis/` should appear on your console. To store our results in an organized way, we will create a folder named **de_results** using the **New Folder** button in the bottom right panel. We will save all our resulting tables and plots in this folder. Next, go to the **File menu**, select **New File** and then **R Script** to create a script editor in which we will save all commands required for the analysis. In the editor type: @@ -90,7 +88,8 @@ load("/workspace/gitpod/training/results_star_salmon/star_salmon/deseq2_qc/deseq In this tutorial we will analyse the `dds` object generated by running the alignment with **STAR** and the quantification with **Salmon**. Alternatively, a user could choose to analyse the the `dds` object generated by running only **Salmon** for both lightweight alignment and quantification. The file is stored in `/workspace/gitpod/training/results_star_salmon/salmon/deseq2_qc/deseq2.dds.RData`. -In DESEq2, the `dds` object is a central data structure that contains the following components: +In DESEq2, the `dds` object is a central data structure that contains the following components: + - `countData`: a matrix of raw count data, where each row represents a gene and each column represents a sample; - `colData`: a data frame containing information about the samples, such as the experimental design, treatment and other relevant metadata; - `design`: a formula specifying the experimental design utilised to estimate the dispersion and the log2foldchange. @@ -107,7 +106,7 @@ colData(dds) # to check the sample info design(dds) # to check the design formula ``` -The `colData` and the `design` are the ones created within the nfcore/rnaseq pipeline and must be reorganised prior to the analysis. With the following commands we will create our metadata starting from the info stored in the `dds`. We will rename the column of the `colData`, we will ensure that the rownames of the metadata are present in the same order as the column names and finally we will update the `colData` of the `dds` object with our newly created metadata. +The `colData` and the `design` are the ones created within the nfcore/rnaseq pipeline and must be reorganised prior to the analysis. With the following commands we will create our metadata starting from the info stored in the `dds`. We will rename the column of the `colData`, we will ensure that the rownames of the metadata are present in the same order as the column names and finally we will update the `colData` of the `dds` object with our newly created metadata. ```r #### Creation of metadata starting from the dds colData #### @@ -148,7 +147,7 @@ Now that everything is setted up, we can proceed to generate a new DESeq2 object dds_new <- DESeqDataSet(dds, design = ~ condition) -# dds inspection +# dds inspection head(counts(dds_new)) # to check the raw counts @@ -168,15 +167,15 @@ Before running the different steps of the analysis, a good practice consists in # Select a minimal number of samples = 3 -smallestGroupSize <- 3 +smallestGroupSize <- 3 # Select genes with a sum counts of at least 10 in 3 samples -keep <- rowSums(counts(dds_new) >= 10) >= smallestGroupSize +keep <- rowSums(counts(dds_new) >= 10) >= smallestGroupSize # Keep only the genes that pass the threshold -dds_filtered <- dds_new[keep,] +dds_filtered <- dds_new[keep,] ``` Now, it is time to run the differential expression analysis with the `DESeq()` function: @@ -189,7 +188,8 @@ dds_final <- DESeq(dds_filtered) The `DESeq()` function is a high-level wrapper that simplifies the process of differential expression analysis by combining multiple steps into a single function call. -This makes the workflow more user-friendly and ensures that all necessary preprocessing and statistical steps are executed in the correct order. The key functions that **DESeq2** calls include: +This makes the workflow more user-friendly and ensures that all necessary preprocessing and statistical steps are executed in the correct order. The key functions that **DESeq2** calls include: + - **estimateSizeFactors**: to normalise the count data; - **estimateDispersion**: to estimate the dispersion; - **nbinomWaldTest**: to perform differential expression test. @@ -217,11 +217,12 @@ rld <- rlog(dds_final, blind = TRUE) ``` The `rlog` and the `vst` transformations have an argument, **blind** that can be set to: + - **TRUE** (default): useful for QC analysis because it re-estimates the dispersion, allowing for comparison of samples in an unbiased manner with respect to experimental conditions; - **FALSE**: the function utilizes the already estimated dispersion, generally applied when differences in counts are expected to be due to the experimental design. Next, we perform Principal Component Analysis (PCA) to explore the data. DESeq2 provides a built-in function, `plotPCA()`, which uses [ggplot2](https://ggplot2.tidyverse.org) for visualisation, taking the `rld` (or the `vst`) object as input. -Since the **treatment** is the principal condition of interest in our metadata, we will use the `condition` information from our metadata to plot the PCA: +Since the **treatment** is the principal condition of interest in our metadata, we will use the `condition` information from our metadata to plot the PCA: ```r #### Plot PCA #### @@ -241,7 +242,7 @@ sampleDists <- dist(t(assay(rld))) # Calculate pairwise distances between sampl # Convert distances to a matrix -sampleDistMatrix <- as.matrix(sampleDists) +sampleDistMatrix <- as.matrix(sampleDists) # Set the row and column names of the distance matrix @@ -255,11 +256,11 @@ colors <- colorRampPalette(rev(brewer.pal(9, "Greens")))(255) # function from RC # Create the heatmap -pheatmap(sampleDistMatrix, - clustering_distance_rows = sampleDists, - clustering_distance_cols = sampleDists, - col = colors, - fontsize_col = 8, +pheatmap(sampleDistMatrix, + clustering_distance_rows = sampleDists, + clustering_distance_cols = sampleDists, + col = colors, + fontsize_col = 8, fontsize_row = 8) ``` @@ -286,10 +287,10 @@ normalised_counts$gene <- rownames(counts(dds_final)) # Relocate the gene column to the first position -normalised_counts <- normalised_counts %>% +normalised_counts <- normalised_counts %>% relocate(gene, .before = control_rep1) -# Save normalised counts +# Save normalised counts write.csv(normalised_counts, file = "de_results/normalised_counts.csv") ``` @@ -303,7 +304,7 @@ The `results()` function in DESeq2 is used to extract the results of the DE anal - **pvalue**: the p-value from the Wald test indicates the probability of observing the measured difference in gene expression (log2 fold change) by chance, assuming no true difference exists (null hypothesis). A low p-value suggests that the observed expression change between samples is unlikely due to random chance, so we can reject the null hypothesis; - **padj**: the adjusted p-value, which takes into account multiple testing corrections, using the Benjamini-Hochberg method to control the false discovery rate; -By default, the `results()` function returns the results for all genes in the analysis with an adjusted p-value below a specific FDR cutoff, set by default to 0.1. This threshold can be modified with the parameter `alpha`. The `results()` function can also be customised to filter the results based on certain criteria (log2 fold change or padj) or to set a specific contrast. A contrast is a specific comparison between two or more levels of a factor, such as the comparison between the treatment and control groups. The order of the contrast names determines the direction of the fold change that is reported in the results. Specifically, the first level of the contrast is the condition of interest and the second level is the reference level. +By default, the `results()` function returns the results for all genes in the analysis with an adjusted p-value below a specific FDR cutoff, set by default to 0.1. This threshold can be modified with the parameter `alpha`. The `results()` function can also be customised to filter the results based on certain criteria (log2 fold change or padj) or to set a specific contrast. A contrast is a specific comparison between two or more levels of a factor, such as the comparison between the treatment and control groups. The order of the contrast names determines the direction of the fold change that is reported in the results. Specifically, the first level of the contrast is the condition of interest and the second level is the reference level. Notice that in this tutorial the contrast is already correctly specified. ```r @@ -313,15 +314,15 @@ res <- results(dds_final) # Visualise the results -head(res) +head(res) -# Summarise the results showing the number of tested genes (genes with non-zero total read count), the genes up- and down-regulated at the selected threshold (alpha) and the number of genes excluded by the multiple testing due to a low mean count +# Summarise the results showing the number of tested genes (genes with non-zero total read count), the genes up- and down-regulated at the selected threshold (alpha) and the number of genes excluded by the multiple testing due to a low mean count summary(res) # DESeq2 function to extract the name of the contrast -resultsNames(dds_final) +resultsNames(dds_final) # res <- results(dds, contrast = c("design_formula", "condition_of_interest", "reference_condition")) # Command to set the contrast, if necessary @@ -336,37 +337,37 @@ res_viz$gene <- rownames(res) # Convert the results to a tibble for easier manipulation and relocate the gene column to the first position -res_viz <- as_tibble(res_viz) %>% +res_viz <- as_tibble(res_viz) %>% relocate(gene, .before = baseMean) -# Save the results table +# Save the results table write.csv(res_viz, file = "de_results/de_result_table.csv") ``` -In the *Experimental Design* section, we emphasised the importance of estimating the log2 fold change threshold using a statistical power calculation, rather than selecting it arbitrarily. This approach ensures that the chosen threshold is statistically appropriate and tailored to the specifics of the experiment. However, since we are working with simulated data for demonstration purposes, we will use a padj threshold of 0.05 and consider genes with a log2 fold change greater than 1 or lower than -1 as differentially expressed. +In the _Experimental Design_ section, we emphasised the importance of estimating the log2 fold change threshold using a statistical power calculation, rather than selecting it arbitrarily. This approach ensures that the chosen threshold is statistically appropriate and tailored to the specifics of the experiment. However, since we are working with simulated data for demonstration purposes, we will use a padj threshold of 0.05 and consider genes with a log2 fold change greater than 1 or lower than -1 as differentially expressed. ```r #### Extract significant DE genes from the results #### # Filter the results to include only significantly differentially expressed genes with an adjusted p-value (padj) less than 0.05 and a log2foldchange greater than 1 or less than -1 -resSig <- subset(res_viz, padj < 0.05 & abs(log2FoldChange) > 1) +resSig <- subset(res_viz, padj < 0.05 & abs(log2FoldChange) > 1) # Convert the results to a tibble for easier manipulation and relocate the 'Gene' column to be the first column -resSig <- as_tibble(resSig) %>% - relocate(gene, .before = baseMean) +resSig <- as_tibble(resSig) %>% + relocate(gene, .before = baseMean) # Order the significant genes by their adjusted p-value (padj) in ascending order -resSig <- resSig[order(resSig$padj),] +resSig <- resSig[order(resSig$padj),] # Display the final results table of significant genes -resSig +resSig -# Save the significant DE genes +# Save the significant DE genes write.csv(resSig, file = "de_results/sig_de_genes.csv") ``` @@ -380,7 +381,7 @@ Now that we have obtained the results of the differential expression analysis, i plotMA(res, ylim = c(-2,2)) ``` - + - **counts plot**: it plots the normalised counts for a single gene across the different conditions in your experiment. It’s particularly useful for visualising the expression levels of specific genes of interest and comparing them across sample groups. ```r @@ -399,14 +400,14 @@ Notiche that this plot is based on the normalised counts. significant_genes <- resSig[, 1] -# Extract normalised counts for significant genes from the normalised counts matrix and convert the "gene" column to row names +# Extract normalised counts for significant genes from the normalised counts matrix and convert the "gene" column to row names -significant_counts <- inner_join(normalised_counts, significant_genes, by = "gene") %>% +significant_counts <- inner_join(normalised_counts, significant_genes, by = "gene") %>% column_to_rownames("gene") # Create the heatmap using pheatmap -pheatmap(significant_counts, +pheatmap(significant_counts, cluster_rows = TRUE, fontsize = 8, scale = "row", @@ -422,49 +423,48 @@ pheatmap(significant_counts, # Convert the results to a tibble and add a column indicating differential expression status -res_tb <- as_tibble(res) %>% +res_tb <- as_tibble(res) %>% mutate(diffexpressed = case_when( - log2FoldChange > 1 & padj < 0.05 ~ 'upregulated', + log2FoldChange > 1 & padj < 0.05 ~ 'upregulated', log2FoldChange < -1 & padj < 0.05 ~ 'downregulated', TRUE ~ 'not_de')) # Add a new column with gene names -res_tb$gene <- rownames(res) +res_tb$gene <- rownames(res) # Relocate the 'gene' column to be the first column -res_tb <- res_tb %>% +res_tb <- res_tb %>% relocate(gene, .before = baseMean) # Order the table by adjusted p-value (padj) and add a new column for gene labels -res_tb <- res_tb %>% arrange(padj) %>% +res_tb <- res_tb %>% arrange(padj) %>% mutate(genelabels = "") - + # Label the top 5 most significant genes res_tb$genelabels[1:5] <- res_tb$gene[1:5] # Create a volcano plot using ggplot2 -ggplot(data = res_tb, aes(x = log2FoldChange, y = -log10(padj), col = diffexpressed)) + - geom_point(size = 0.6) + +ggplot(data = res_tb, aes(x = log2FoldChange, y = -log10(padj), col = diffexpressed)) + + geom_point(size = 0.6) + geom_text_repel(aes(label = genelabels), size = 2.5, max.overlaps = Inf) + - ggtitle("DE genes treatment versus control") + + ggtitle("DE genes treatment versus control") + geom_vline(xintercept = c(-1, 1), col = "black", linetype = 'dashed', linewidth = 0.2) + geom_hline(yintercept = -log10(0.05), col = "black", linetype = 'dashed', linewidth = 0.2) + theme(plot.title = element_text(size = rel(1.25), hjust = 0.5), axis.title = element_text(size = rel(1))) + - scale_color_manual(values = c("upregulated" = "red", - "downregulated" = "blue", + scale_color_manual(values = c("upregulated" = "red", + "downregulated" = "blue", "not_de" = "grey")) + labs(color = 'DE genes') + xlim(-3,5) ``` - ## Functional analysis The output of the differential expression analysis is a list of significant DE genes. To uncover the underlying biological mechanisms, various downstream analyses can be performed, such as functional enrichment analysis (identify overrepresented biological processes, molecular functions, cellular components or pathways) and network analysis (group genes based on similar expression patterns to identify novel interactions). To facilitate the interpretation of the resulting list of DE genes, a range of freely available web- and R-based tools can be employed. @@ -530,7 +530,7 @@ go_enrich <- enrichGO( # Create a bar plot of the top enriched GO terms barplot <- barplot( - go_enrich, + go_enrich, title = "Enrichment analysis barplot", font.size = 8 ) @@ -546,4 +546,4 @@ dotplot <- dotplot( # Combine the bar plot and dot plot into a single plot grid plot_grid(barplot, dotplot, nrow = 2) -``` \ No newline at end of file +``` diff --git a/docs/usage/DEanalysis/index.md b/docs/usage/DEanalysis/index.md index 8bab2769b..6c68014c0 100644 --- a/docs/usage/DEanalysis/index.md +++ b/docs/usage/DEanalysis/index.md @@ -20,7 +20,6 @@ By the end of this workshop, you will be able to: Let's get started! - ## Running with Gitpod In order to run this using GitPod, please make sure: @@ -30,21 +29,17 @@ In order to run this using GitPod, please make sure: Now you're all set and can use the following button to launch the service: - [![Open in GitPod](https://img.shields.io/badge/Gitpod-%20Open%20in%20Gitpod-908a85?logo=gitpod)](https://gitpod.io/#https://github.com/lescai-teaching/rnaseq-tutorial) - - ## Additional documentation - You can find detailed documentation on **Nextflow** [here](https://www.nextflow.io/docs/latest/) - You can find additional training on [these pages](https://training.nextflow.io) - ## Credits & Copyright This training material has been written and completed during the [nf-core](https://nf-co.re) Hackathon in Barcellona, 2024, by Lorenzo Sola, Francesco Lescai and Mariangela Santorsola, with special thanks to Victoria Cepeda for her contributions to the tutorial's revision. The tutorial is aimed at anyone who is interested in using nf-core pipelines for their studies or research activities. The Docker image and Gitpod environment used in this repository have been created by [Seqera](https://seqera.io) but have been made open-source ([CC BY-NC-ND](https://creativecommons.org/licenses/by-nc-nd/4.0/)) for the community. -All examples and descriptions are licensed under the [Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License](http://creativecommons.org/licenses/by-nc-nd/4.0/). \ No newline at end of file +All examples and descriptions are licensed under the [Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License](http://creativecommons.org/licenses/by-nc-nd/4.0/). diff --git a/docs/usage/DEanalysis/interpretation.md b/docs/usage/DEanalysis/interpretation.md index 7ed680ec2..fb0f38a25 100644 --- a/docs/usage/DEanalysis/interpretation.md +++ b/docs/usage/DEanalysis/interpretation.md @@ -6,10 +6,9 @@ Once DE genes have been identified, the next crucial step is to interpret the re The results illustrated in this section might show slight variations compared to your runs due to randomness in the STAR algorithm. This randomness arises from using variable seed values and parallel processing, leading to minor differences in results between runs on the same data. These small discrepancies are not biologically significant and may affect counts and subsequent plots (such as PCA and count plots). However, the overall patterns and main findings should remain consistent. While exact reproducibility is ideal, minor variations are acceptable in practice, as long as they do not impact the main conclusions of the study. - ## Quality control plots -The first plot we will examine is the Principal Component Analysis (PCA) plot. Since we're working with simulated data, our metadata is relatively simple, consisting of just three variables: sample, condition and replica. In a typical RNA-seq experiment, however, metadata can be complex and encompass a wide range of variables that could contribute to sample variation, such as sex, age and developmental stage. +The first plot we will examine is the Principal Component Analysis (PCA) plot. Since we're working with simulated data, our metadata is relatively simple, consisting of just three variables: sample, condition and replica. In a typical RNA-seq experiment, however, metadata can be complex and encompass a wide range of variables that could contribute to sample variation, such as sex, age and developmental stage. ![PCA](./img/PCA.png) @@ -19,15 +18,14 @@ Next, we will examine the hierarchical clustering plot to explore the relationsh ![Cluster](./img/hierarchical_clustering.png) -Remember that to create this plot, we utilized the `dist()` function, so in the legend on the right, a value of 0 corresponds to high correlation, while a value of 5 corresponds to very low correlation. Similar to PCA, we can see that samples tend to cluster together according to `condition`, indeed we can observe a high degree of correlation between the three control samples and between the three treated samples. +Remember that to create this plot, we utilized the `dist()` function, so in the legend on the right, a value of 0 corresponds to high correlation, while a value of 5 corresponds to very low correlation. Similar to PCA, we can see that samples tend to cluster together according to `condition`, indeed we can observe a high degree of correlation between the three control samples and between the three treated samples. Overall, the integration of these plots suggests that we are working with high-quality data and we can confidently proceed to the differential expression analysis. - ## Differential expression results -From this point, we will examine plots that are generated after the differential expression analysis. These plots are not quality control (QC) plots, but rather plots that help us to interpret the results. -After running the `results()` function, a good way to start to have an idea about the results is to look at the MA plot. +From this point, we will examine plots that are generated after the differential expression analysis. These plots are not quality control (QC) plots, but rather plots that help us to interpret the results. +After running the `results()` function, a good way to start to have an idea about the results is to look at the MA plot. ![RNAseq](./img/MA_plot.png) @@ -54,7 +52,7 @@ After the identification of DE genes, it's informative to visualise the expressi ![Counts](./img/plotCounts.png) -In our example, post-treatment, we observe a significant increase in the expression of the *ENSG00000142192* gene, highlighting its responsiveness to the experimental conditions. +In our example, post-treatment, we observe a significant increase in the expression of the _ENSG00000142192_ gene, highlighting its responsiveness to the experimental conditions. Finally, we can create a heatmap using the normalised expression counts of DE genes. The resulting heatmap visualises how the expression of significant genes varies across samples. Each row represents a gene, and each column represents a sample. The color intensity in the heatmap reflects the normalised expression levels: red colors indicate higher expression, while blue colors indicate lower expression. @@ -62,16 +60,14 @@ Finally, we can create a heatmap using the normalised expression counts of DE ge By examining the heatmap, we can visually identify the expression patterns of our five significant differentially expressed genes. This visualization allows us to identify how these genes respond to the treatment. The heatmap provides a clear and intuitive way to explore gene expression dynamics. - ## Over Representation Analysis (ORA) -Finally, we can attempt to assign biological significance to our differentially expressed genes through **Enrichment Analysis (ORA)**. The ORA analysis identifies specific biological pathways, molecular functions and cellular processes, according to the **Gene Ontology (GO)** database, that are enriched within our differentially expressed genes. +Finally, we can attempt to assign biological significance to our differentially expressed genes through **Enrichment Analysis (ORA)**. The ORA analysis identifies specific biological pathways, molecular functions and cellular processes, according to the **Gene Ontology (GO)** database, that are enriched within our differentially expressed genes. ![Enrichment](./img/enrichment_plot.png) The enrichment analysis reveals a possible involvement of cellular structures and processes, including "clathrin-coated pit", "dendritic spine", "neuron spine" and "endoplasmic reticulum lumen". These terms suggest a focus on cellular transport, structural integrity and protein processing, especially in neural contexts. This pattern points to pathways related to cellular organization and maintenance, possibly playing an important role in the biological condition under study. - ## Conclusions -In this tutorial, we have walked through the steps of performing differential expression analysis using DESeq2, from preparing the data to interpreting the results. We have seen how to identify differentially expressed genes, visualise the results and perform enrichment analysis to gain insights into the biological significance of the results. By following this tutorial, you should now be able to perform differential expression analysis using DESeq2 and interpret the results in the context of your own research question. \ No newline at end of file +In this tutorial, we have walked through the steps of performing differential expression analysis using DESeq2, from preparing the data to interpreting the results. We have seen how to identify differentially expressed genes, visualise the results and perform enrichment analysis to gain insights into the biological significance of the results. By following this tutorial, you should now be able to perform differential expression analysis using DESeq2 and interpret the results in the context of your own research question. diff --git a/docs/usage/DEanalysis/rnaseq.md b/docs/usage/DEanalysis/rnaseq.md index 05ffcd439..a801520a1 100644 --- a/docs/usage/DEanalysis/rnaseq.md +++ b/docs/usage/DEanalysis/rnaseq.md @@ -2,7 +2,6 @@ In order to carry out a RNA-Seq analysis we will use the nf-core pipeline [rnaseq](https://nf-co.re/rnaseq/3.14.0). - ## Overview The pipeline is organised following the diffent blocks: pre-processing, alignment (or lightweight alignment) and quantification, post-processing and final QC. @@ -10,63 +9,58 @@ The pipeline is organised following the diffent blocks: pre-processing, alignmen ![Metromap](./img/nf-core-rnaseq_metro_map_grey.png) In each process, the users can choose among a range of different options. Importantly, the users can decide to follow one of the two different routes in the alignment and quantification step: -- alignment and quantification (stage 2); -- pseudoalignment and quantification (stage 3). +- alignment and quantification (stage 2); +- pseudoalignment and quantification (stage 3). ## Experimental Design -The number of reads and the number of biological replicates are two critical factors that researchers need to carefully consider during the design of a RNA-seq experiment. While it may seem intuitive that having a large number of reads is always desirable, an excessive number can lead to unnecessary costs and computational burdens, without providing significant improvements in quality of the data. Instead, it is often more beneficial to prioritise the number of biological replicates, as it allows to capture the natural biological variation of the data. Biological replicates involve collecting and sequencing RNA from distinct biological samples (e.g., different individuals, tissues, or time points), helping to detect genuine changes in gene expression. +The number of reads and the number of biological replicates are two critical factors that researchers need to carefully consider during the design of a RNA-seq experiment. While it may seem intuitive that having a large number of reads is always desirable, an excessive number can lead to unnecessary costs and computational burdens, without providing significant improvements in quality of the data. Instead, it is often more beneficial to prioritise the number of biological replicates, as it allows to capture the natural biological variation of the data. Biological replicates involve collecting and sequencing RNA from distinct biological samples (e.g., different individuals, tissues, or time points), helping to detect genuine changes in gene expression. !!! note This concept must not be confused with technical replicates that asses the technical variability of the sequencing platform by sequencing the same RNA sample multiple time. -To obtain optimal results, it is crucial to balance the number of biological replicates and sequencing depth. While deeper sequencing improves the detection of lowly expressed genes, it reaches a plateau, beyond which no additional benefits are gained. Statistical power calculations can inform experimental design by estimating the optimal number of reads and replicates required. For instance, this approach helps establish a suitable log2 fold change (log2FC) threshold for the DE analysis. By incorporating multiple biological replicates into the design and optimizing sequencing depth, researchers can enhance the statistical power of the analysis, reducing the number of false positive results and increasing the reliability of the findings. - +To obtain optimal results, it is crucial to balance the number of biological replicates and sequencing depth. While deeper sequencing improves the detection of lowly expressed genes, it reaches a plateau, beyond which no additional benefits are gained. Statistical power calculations can inform experimental design by estimating the optimal number of reads and replicates required. For instance, this approach helps establish a suitable log2 fold change (log2FC) threshold for the DE analysis. By incorporating multiple biological replicates into the design and optimizing sequencing depth, researchers can enhance the statistical power of the analysis, reducing the number of false positive results and increasing the reliability of the findings. ## Library design RNA-seq library design involves critical decisions, including the choice between paired-end and single-end sequencing. Paired-end sequencing provides valuable information on structural variations and transcript isoforms, improving mapping accuracy, especially for longer transcripts and repetitive regions. In contrast, single-end sequencing, where only one end of the fragment is sequenced, can be more cost-effective while still providing high-quality data for gene expression analysis. The decision between paired-end and single-end sequencing ultimately depends on the research question and experimental goals. Paired-end sequencing is preferred for novel transcript identification or isoform characterization, while single-end sequencing is sufficient for gene expression quantification. The type of RNA (e.g., mRNA or total RNA), read length, budget and computational resources can impact the choice. - ## Reference genome nf-core pipelines make use of the Illumina iGenomes collection as [reference genomes](https://nf-co.re/docs/usage/reference_genomes). Before starting the analysis, the users might want to check whether the genome they need is part of this collection. -They also might want to consider downloading the reference locally, when running on premises: this would be useful for multiple runs and to speed up the analysis. In this case the parameter `--igenomes_base` might be used to pass the root directory of the downloaded references. +They also might want to consider downloading the reference locally, when running on premises: this would be useful for multiple runs and to speed up the analysis. In this case the parameter `--igenomes_base` might be used to pass the root directory of the downloaded references. One might also need to use custom files: in this case the users might either provide specific parameters at command line (`--fasta` option followed by the genome of choiche), or create a config file adding a new section to the `genome` object. See [here](https://nf-co.re/docs/usage/reference_genomes#custom-genomes) for more details. We will follow this specific approach in this tutorial, since the data we will be using have been simulated on chromosome 21 of the Human GRCh38 reference, and we have prepared genome fasta and genome index containing only this chromosome locally. The two files are `/workspace/gitpod/training/data/refs/Homo_sapiens_assembly38_chr21.fa` and `/workspace/gitpod/training/data/refs/Homo_sapiens_assembly38_chr21.fa.fai`, respectively. - ## Reference annoation The reference annotation plays a crucial role in the RNA-seq analysis. Without a high-quality reference annotation, RNA-seq analysis would result in inaccurate or incomplete results. The reference annotation provides a precise guide for aligning sequencing reads to specific genomic regions, allowing to identify genes, transcripts and regulatory elements. This is particularly important for identifying novel transcripts and alternative splicing events. nf-core pipelines make use of the Illumina iGenomes collection also as [reference annotation](https://nf-co.re/docs/usage/reference_genomes). -The reference annotations are vastly out of date with respect to current annotations and miss certain features. So, the general recommendation is to download a newest annotation version compatible with the genome. A user can utilize the `--gtf` or the `--gff` options to specify the annottation files of choiche, or create a config file adding a new section to the `genome` object. +The reference annotations are vastly out of date with respect to current annotations and miss certain features. So, the general recommendation is to download a newest annotation version compatible with the genome. A user can utilize the `--gtf` or the `--gff` options to specify the annottation files of choiche, or create a config file adding a new section to the `genome` object. Similarly to the approach utilised for the genome, in this tutorial we will follow this approach. The annotation files include only the annotated transcripts on chromosome 21 of the Human GRCh38 reference genome and we have already prepared these files locally. The two files are `/workspace/gitpod/training/data/refs/gencode_v29_chr21.gff` and `/workspace/gitpod/training/data/refs/gencode_v29_transcripts_chr21.fa`, respectively. - ## Input files The input data should be provided in a CSV file, according to a format that is largely common for nf-core pipelines. The format is described in the [rnaseq usage page](https://nf-co.re/rnaseq/3.14.0/docs/usage). In the tutorial, the input file is `/workspace/gitpod/training/data/reads/rnaseq_samplesheet.csv`. - ## Running nf-core/rnaseq In the following sections we will: + - prepare our references; - set our computational resources in order to be able to run the pipeline on a gitpod VM; - edit the optional settings; - run the pipeline. - ## Reference and annotation files Following the considerations above, we will first of all edit the `nextflow.config` file in our working directory to add a new genome. @@ -88,7 +82,6 @@ genomes { To speed up the analysis we will include the `star_index` and the `salmon_index` in the config. These files have already been created locally. - ## Computing resources Based on the choices we made when starting up the gitpod environment, we recommend to use the following additional parameters. @@ -102,7 +95,6 @@ params { } ``` - ## Launching the pipeline Now we are ready to launch the pipeline, and we can use the following command line: @@ -127,4 +119,4 @@ nextflow run nf-core/rnaseq -r 3.12.0 \ ``` Notice that we will run the pipeline with STAR as aligner and Salmon in alignment-based mode to quantify gene expression. We will also run the pipeline with Salmon in mapping-based mode to perform a lightweight alignment and quantification. -The `skip` parameters were inserted to reduce the running time. \ No newline at end of file +The `skip` parameters were inserted to reduce the running time. diff --git a/docs/usage/DEanalysis/theory.md b/docs/usage/DEanalysis/theory.md index 2a00d18b2..b829ec3c4 100644 --- a/docs/usage/DEanalysis/theory.md +++ b/docs/usage/DEanalysis/theory.md @@ -14,7 +14,7 @@ In the scheme, we can identify three key phases in the workflow: data pre-proces Depending on the user's needs, the workflow can include additional downstream analyses such as functional enrichment analysis, co-expression analysis, and integration with other omics data. -## Pre-processing +## Pre-processing The pre-processing of sequencing reads from RNA-seq data is a critical step to ensure the quality and accuracy of downstream analysis. The raw reads obtained from the sequencer are stored as [FASTQ](https://en.wikipedia.org/wiki/FASTQ_format) files, which contain both the sequence data and quality scores. The initial processing step involves evaluating the quality of the reads and identifying potential issues such as adapter contamination, sequencing errors, or low-quality bases. The presence of adapters (short DNA sequences ligated to the ends of DNA fragments during library preparation) is detected through comparison with known adapter sequences or by using algorithms that identify adapter-like sequences, and removed in a process known as **read trimming**. Next, reads containing contaminants (genomic DNA and/or rRNA) and those with low-quality bases are filtered out. Finally, the quality of the filtered reads is checked again to ensure their suitability for downstream processing. @@ -25,6 +25,7 @@ In the RNA-seq workflow, the alignment step involves mapping sequencing reads to Errors, gaps, regions of poor sequence quality, insertions/deletions (INDELs), as well as duplicated and repetitive regions in the reference sequence, make this step challenging. Addressing these issues by choosing a high-quality reference and an appropriate aligner is essential for accurate results. A crucial component in the alignment step is the [annotation](https://www.ncbi.nlm.nih.gov/genbank/genomes_gff) file, which can be in General Feature Format (GFF) or Gene Transfer Format (GTF). These files contain key information about the location and structure of genes and transcripts, playing a crucial role in accurate mapping and gene expression quantification. Additionally, RNA-seq data often includes reads that span exon-exon junctions, and the annotation files provide information about splice junctions, allowing for the detection of different isoforms. The alignment and quantification steps can follow two different approaches depending on user preferences: + - traditional alignment and quantification; - lightweight alignment and quantification. @@ -48,8 +49,7 @@ Finally, it is often beneficial to filter out genes that are unlikely to show di !!! note - The `vst` or `rlog` transformations are applied to the normalised counts stored in the `dds` object, which is generated by running either the `DESeq()` or `estimateSizeFactors()` function. Since the estimation of size factors is an early step in the `DESeq()` function, the transformations are generally applied immediately afterwards. - + The `vst` or `rlog` transformations are applied to the normalised counts stored in the `dds` object, which is generated by running either the `DESeq()` or `estimateSizeFactors()` function. Since the estimation of size factors is an early step in the `DESeq()` function, the transformations are generally applied immediately afterwards. ### Design Formula @@ -71,20 +71,16 @@ design = ~ sex + developmental_stage + condition In R, the tilde (`~`) is used in formula syntax to specify relationships between variables in statistical models. Here, it indicates that gene counts (dependent variable) will be modelled as a function of the specified variables (predictors). - The results will not be affected by the order of variables but the common practice is to specify the main source of variation in the last position of the design formula. - ### Differential Expression Analysis RNA-seq data typically contain a large number of genes with low expression counts, indicating that many genes are expressed at very low levels across samples. At the same time, RNA-seq data exhibit a skewed distribution with a long right tail due to the absence of an upper limit for gene expression levels. This means that while most genes have low to moderate expression levels, a small number are expressed at high levels. Accurate statistical modelling must therefore account for this distribution to avoid misleading conclusions. - ![RNAseq](./img/count_distribution.png) The core of the differential expression analysis is the `DESeq()` function, a wrapper that streamlines several key steps into a single command. The different functions are listed below: - ![RNAseq](./img/DESeq_function.png) !!! note @@ -93,7 +89,7 @@ The core of the differential expression analysis is the `DESeq()` function, a wr The different steps are explained in detail below: -1) **Normalisation**: since DESeq2 compares counts between sample groups for the same gene, it does not need to adjust for gene length. However, it is essential to account for variations in sequencing depth and RNA composition among samples. To normalise the data, DESeq2 utilises size factors, which correct for these variations in library sizes and RNA composition. +1. **Normalisation**: since DESeq2 compares counts between sample groups for the same gene, it does not need to adjust for gene length. However, it is essential to account for variations in sequencing depth and RNA composition among samples. To normalise the data, DESeq2 utilises size factors, which correct for these variations in library sizes and RNA composition. The size factors are calculated using the **median ratio** method: @@ -106,34 +102,32 @@ The size factors are calculated using the **median ratio** method: While normalised counts are useful for downstream visualisation of results, they should not be used as input for DESeq2. Instead, DESeq2 requires count data in the form of a matrix of integer values. -2) **Estimate dispersion and gene-wise dispersion**: the dispersion is a measure of how much the variance deviates from the mean. The estimation of dispersion is essential to model the variance of the count data. Importantly, RNA-seq data are characterised by **overdispersion**, where the variance in gene expression levels often exceeds the mean (variance > mean). - +2. **Estimate dispersion and gene-wise dispersion**: the dispersion is a measure of how much the variance deviates from the mean. The estimation of dispersion is essential to model the variance of the count data. Importantly, RNA-seq data are characterised by **overdispersion**, where the variance in gene expression levels often exceeds the mean (variance > mean). ![RNAseq](./img/overdispersion.png) -DESeq2 addresses this issue by employing the **negative binomial distribution**, which generalises the Poisson distribution by introducing an additional dispersion parameter. This parameter quantifies the extra variability present in RNA-seq data, providing a more realistic representation than the Poisson distribution, which assumes equal mean and variance. DESeq2 starts by estimating the **common dispersion**, a single estimate of dispersion applicable to all genes in the dataset. This estimate provides a baseline for variability across all genes in the dataset. Next, DESeq2 estimates **gene-wise dispersion**, a separate estimate of dispersion for each individual gene, taking into account that different genes may exhibit varying levels of expression variability due to biological differences. +DESeq2 addresses this issue by employing the **negative binomial distribution**, which generalises the Poisson distribution by introducing an additional dispersion parameter. This parameter quantifies the extra variability present in RNA-seq data, providing a more realistic representation than the Poisson distribution, which assumes equal mean and variance. DESeq2 starts by estimating the **common dispersion**, a single estimate of dispersion applicable to all genes in the dataset. This estimate provides a baseline for variability across all genes in the dataset. Next, DESeq2 estimates **gene-wise dispersion**, a separate estimate of dispersion for each individual gene, taking into account that different genes may exhibit varying levels of expression variability due to biological differences. The dispersion parameter (α) is related to the mean (μ) and variance of the data, as described by the equation: `Var = μ + α ⋅ μ²` A key feature of DESeq2's dispersion estimates is their negative correlation with the mean and positive correlation with variance. Genes with low expression have higher dispersion values, while genes with high expression tend to have lower dispersion. Additionally, genes sharing similar mean expression levels can exhibit different dispersion estimates based on their variance. To improve the accuracy of dispersion estimates, DESeq2 assumes that genes with similar expression profiles share similar dispersion patterns and leverages this information to refine the estimates. -3) **Mean-dispersion relationship**: this process, known as dispersion fitting, models the relationship between the mean expression level of a gene and its dispersion. In this process, DESeq2 identifies a trend in the dispersion estimates across genes. The fitted curve, typically a smooth curve, describes how dispersion changes as a function of the mean expression level. +3. **Mean-dispersion relationship**: this process, known as dispersion fitting, models the relationship between the mean expression level of a gene and its dispersion. In this process, DESeq2 identifies a trend in the dispersion estimates across genes. The fitted curve, typically a smooth curve, describes how dispersion changes as a function of the mean expression level. -4) **Final dispersion estimates**: DESeq2 refines the gene-wise dispersion by shrinking it towards the fitted curve. The "shrinkage" helps control for overfitting, particularly in genes with low counts or few replicates, and makes the dispersion estimates more reliable. However, genes with exceptionally high dispersion values are not shrunk because they likely deviate from the modelling assumptions, exhibiting elevated variability due to biological or technical factors. Shrinking these values could lead to false positives. +4. **Final dispersion estimates**: DESeq2 refines the gene-wise dispersion by shrinking it towards the fitted curve. The "shrinkage" helps control for overfitting, particularly in genes with low counts or few replicates, and makes the dispersion estimates more reliable. However, genes with exceptionally high dispersion values are not shrunk because they likely deviate from the modelling assumptions, exhibiting elevated variability due to biological or technical factors. Shrinking these values could lead to false positives. -5) **Fitting model and testing**: the initial step in hypothesis testing involves defining a **null hypothesis** for each gene. The null hypothesis states that there is no difference in expression between the tested sample groups (LFC == 0). Next, DESeq applies a statistical test to assess whether the null hypothesis is true. -DESeq2 fits a **generalised linear model (GLM)** to the normalised counts using the calculated size factors and final dispersion estimates. A GLM is a flexible extension of linear regression that models the relationship between a response variable (normalised counts) and predictors (e.g., condition). -By using a **negative binomial distribution**, DESeq2's GLM can handle the additional variability in gene expression counts that often occurs in RNA-seq data.Once the model is fit, coefficients are estimated for each sample group along with their standard errors. These coefficients represent the estimated log2 fold changes between groups and serve as the basis for hypothesis testing, using either a **Wald test** or a **Likelihood Ratio Test (LRT)**, depending on the experimental design: +5. **Fitting model and testing**: the initial step in hypothesis testing involves defining a **null hypothesis** for each gene. The null hypothesis states that there is no difference in expression between the tested sample groups (LFC == 0). Next, DESeq applies a statistical test to assess whether the null hypothesis is true. + DESeq2 fits a **generalised linear model (GLM)** to the normalised counts using the calculated size factors and final dispersion estimates. A GLM is a flexible extension of linear regression that models the relationship between a response variable (normalised counts) and predictors (e.g., condition). + By using a **negative binomial distribution**, DESeq2's GLM can handle the additional variability in gene expression counts that often occurs in RNA-seq data.Once the model is fit, coefficients are estimated for each sample group along with their standard errors. These coefficients represent the estimated log2 fold changes between groups and serve as the basis for hypothesis testing, using either a **Wald test** or a **Likelihood Ratio Test (LRT)**, depending on the experimental design: - **Wald Test**: The Wald test is ideal for simpler experimental designs, such as comparing two conditions (e.g., treated vs untreated). It tests whether the estimated effect size (log2 fold change) of a predictor variable (like treatment) is significantly different from zero. This test provides direct estimates of fold changes with associated p-values, making it computationally efficient for straightforward comparisons. - - **Likelihood Ratio Test (LRT)**: The LRT is more suitable for complex experimental designs involving multiple variables (e.g., time points, treatments and batches). It compares the fit of two nested models: one with the factor of interest (e.g., treatment) and one without it to assess whether including that factor significantly improves model fit. This approach allows DESeq2 to account for confounding variables and to isolate the effect of specific variables on gene expression, offering flexibility for multi-factor analyses. -When performing multiple tests, such as in the case of RNA-seq data where thousands of genes are tested, the risk of false positives increases. To account for this, DESeq2 employs multiple test correction methods (the Benjamini-Hochberg procedure is the default) to adjust the p-values and control the false discovery rate (FDR). +When performing multiple tests, such as in the case of RNA-seq data where thousands of genes are tested, the risk of false positives increases. To account for this, DESeq2 employs multiple test correction methods (the Benjamini-Hochberg procedure is the default) to adjust the p-values and control the false discovery rate (FDR). !!! note - The FDR is the expected proportion of false positives among the identified significant results. For example, by setting the FDR cutoff to < 0.05, 5% of genes identified as differentially expressed are expected to be false positives. For instance, if 400 genes are identified as differentially expressed with an FDR cutoff of 0.05, you would expect 20 of them to be false positives. + The FDR is the expected proportion of false positives among the identified significant results. For example, by setting the FDR cutoff to < 0.05, 5% of genes identified as differentially expressed are expected to be false positives. For instance, if 400 genes are identified as differentially expressed with an FDR cutoff of 0.05, you would expect 20 of them to be false positives. After identifying DE genes using DESeq2, it is essential to interpret the biological significance of these genes through functional analysis. This involves examining the roles of the differentially expressed genes in various biological processes, molecular functions and pathways, providing insights into the underlying mechanisms driving the observed changes in gene expression. This interpretation can help in discovering pathways involved in disease or identifying potential therapeutic targets. Different tools are available to carry out these functional analyses, such as [Gene Ontology](https://geneontology.org), [Reactome](https://reactome.org/), [KEGG](https://www.genome.jp/kegg), [clusterProfiler](https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html), [g:Profiler](https://biit.cs.ut.ee/gprofiler) and [WikiPathways](https://www.wikipathways.org). From e5d9d4b3e01c2f56b7267e875c13f00ca1489448 Mon Sep 17 00:00:00 2001 From: Siddhartha Bagaria <1929612+siddharthab@users.noreply.github.com> Date: Tue, 5 Nov 2024 14:58:07 -0800 Subject: [PATCH 010/107] actually update umicollapse --- modules.json | 4 ++-- modules/nf-core/umicollapse/environment.yml | 2 +- modules/nf-core/umicollapse/main.nf | 6 ++--- modules/nf-core/umicollapse/meta.yml | 3 ++- .../umicollapse/tests/main.nf.test.snap | 24 +++++++++---------- .../tests/main.nf.test.snap | 4 ++-- 6 files changed, 22 insertions(+), 21 deletions(-) diff --git a/modules.json b/modules.json index f51a6431a..93b28f2ab 100644 --- a/modules.json +++ b/modules.json @@ -269,7 +269,7 @@ }, "umicollapse": { "branch": "master", - "git_sha": "666652151335353eef2fcd58880bcef5bc2928e1", + "git_sha": "78991a2306e7631c006498a372e87f135a4212db", "installed_by": ["bam_dedup_stats_samtools_umicollapse"] }, "umitools/dedup": { @@ -298,7 +298,7 @@ "nf-core": { "bam_dedup_stats_samtools_umicollapse": { "branch": "master", - "git_sha": "763d4b5c05ffda3ac1ac969dc67f7458cfb2eb1d", + "git_sha": "78991a2306e7631c006498a372e87f135a4212db", "installed_by": ["subworkflows"] }, "bam_dedup_stats_samtools_umitools": { diff --git a/modules/nf-core/umicollapse/environment.yml b/modules/nf-core/umicollapse/environment.yml index 3847980dd..066f55eef 100644 --- a/modules/nf-core/umicollapse/environment.yml +++ b/modules/nf-core/umicollapse/environment.yml @@ -2,4 +2,4 @@ channels: - conda-forge - bioconda dependencies: - - bioconda::umicollapse=1.0.0 + - bioconda::umicollapse=1.1.0 diff --git a/modules/nf-core/umicollapse/main.nf b/modules/nf-core/umicollapse/main.nf index dae290e6e..aec10e851 100644 --- a/modules/nf-core/umicollapse/main.nf +++ b/modules/nf-core/umicollapse/main.nf @@ -5,8 +5,8 @@ process UMICOLLAPSE { conda "${moduleDir}/environment.yml" container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container ? - 'https://depot.galaxyproject.org/singularity/umicollapse:1.0.0--hdfd78af_1' : - 'biocontainers/umicollapse:1.0.0--hdfd78af_1' }" + 'https://depot.galaxyproject.org/singularity/umicollapse:1.1.0--hdfd78af_0' : + 'biocontainers/umicollapse:1.1.0--hdfd78af_0' }" input: tuple val(meta), path(input), path(bai) @@ -24,7 +24,7 @@ process UMICOLLAPSE { script: def args = task.ext.args ?: '' def prefix = task.ext.prefix ?: "${meta.id}" - def VERSION = '1.0.0-1' // WARN: Version information not provided by tool on CLI. Please update this string when bumping container versions. + def VERSION = '1.1.0-0' // WARN: Version information not provided by tool on CLI. Please update this string when bumping container versions. // Memory allocation: We need to make sure that both heap and stack size is sufficiently large for // umicollapse. We set the stack size to 5% of the available memory, the heap size to 90% // which leaves 5% for stuff happening outside of java without the scheduler killing the process. diff --git a/modules/nf-core/umicollapse/meta.yml b/modules/nf-core/umicollapse/meta.yml index 8b366c244..7c4ebc5ec 100644 --- a/modules/nf-core/umicollapse/meta.yml +++ b/modules/nf-core/umicollapse/meta.yml @@ -11,7 +11,7 @@ tools: (UMIs)/Random Molecular Tags (RMTs)." homepage: "https://github.com/Daniel-Liu-c0deb0t/UMICollapse" documentation: "https://github.com/Daniel-Liu-c0deb0t/UMICollapse" - tool_dev_url: "https://github.com/Daniel-Liu-c0deb0t/UMICollapse" + tool_dev_url: "https://github.com/siddharthab/UMICollapse" doi: "10.7717/peerj.8275" licence: ["MIT"] identifier: "" @@ -78,4 +78,5 @@ maintainers: - "@CharlotteAnne" - "@chris-cheshire" - "@apeltzer" + - "@siddharthab" - "@MatthiasZepper" diff --git a/modules/nf-core/umicollapse/tests/main.nf.test.snap b/modules/nf-core/umicollapse/tests/main.nf.test.snap index 3f393eac1..bf6d5f30c 100644 --- a/modules/nf-core/umicollapse/tests/main.nf.test.snap +++ b/modules/nf-core/umicollapse/tests/main.nf.test.snap @@ -11,14 +11,14 @@ ] ], [ - "versions.yml:md5,c1e0275d81b1c97a9344d216f9154996" + "versions.yml:md5,03fdbcb1ba9bd40325ca42859d39deb1" ] ], "meta": { - "nf-test": "0.8.4", - "nextflow": "23.10.1" + "nf-test": "0.9.0", + "nextflow": "24.04.4" }, - "timestamp": "2024-05-20T08:47:11.402203361" + "timestamp": "2024-10-22T10:43:04.890267074" }, "umicollapse fastq tests": { "content": [ @@ -32,14 +32,14 @@ ] ], [ - "versions.yml:md5,c1e0275d81b1c97a9344d216f9154996" + "versions.yml:md5,03fdbcb1ba9bd40325ca42859d39deb1" ] ], "meta": { - "nf-test": "0.8.4", - "nextflow": "23.10.1" + "nf-test": "0.9.0", + "nextflow": "24.04.4" }, - "timestamp": "2024-01-30T10:45:56.053352008" + "timestamp": "2024-10-22T10:43:45.691571914" }, "umicollapse stub tests": { "content": [ @@ -112,13 +112,13 @@ ] ], [ - "versions.yml:md5,c1e0275d81b1c97a9344d216f9154996" + "versions.yml:md5,03fdbcb1ba9bd40325ca42859d39deb1" ] ], "meta": { - "nf-test": "0.8.4", - "nextflow": "23.10.1" + "nf-test": "0.9.0", + "nextflow": "24.04.4" }, - "timestamp": "2024-05-20T08:47:30.028323337" + "timestamp": "2024-10-22T10:43:33.250587075" } } \ No newline at end of file diff --git a/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/main.nf.test.snap b/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/main.nf.test.snap index ccb965763..f2b1fb20d 100644 --- a/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/main.nf.test.snap +++ b/subworkflows/nf-core/bam_dedup_stats_samtools_umicollapse/tests/main.nf.test.snap @@ -49,7 +49,7 @@ [ "versions.yml:md5,20605eb79c410c0ed179ba660d82f75b", "versions.yml:md5,23617661d2c899996bee2b05db027e25", - "versions.yml:md5,268e43f34038d4c6146ed050630f95b4", + "versions.yml:md5,657bce03545b4c57f9c5fc4314bf85f7", "versions.yml:md5,e02a62a393a833778e16542eeed0d148", "versions.yml:md5,ef00762e264b99ac45713dc0dedf4060" ] @@ -58,7 +58,7 @@ "nf-test": "0.9.0", "nextflow": "24.04.4" }, - "timestamp": "2024-09-16T08:04:02.126366857" + "timestamp": "2024-10-22T10:44:38.266860983" }, "test_bam_dedup_stats_samtools_umicollapse_idxstats": { "content": [ From 05513ee52422f5014fccc42c45f475c7b8e315b5 Mon Sep 17 00:00:00 2001 From: Siddhartha Bagaria Date: Wed, 6 Nov 2024 13:25:39 -0800 Subject: [PATCH 011/107] Apply suggestions from code review Co-authored-by: Jonathan Manning --- workflows/rnaseq/main.nf | 50 ++++++++++++++++++++-------------------- 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/workflows/rnaseq/main.nf b/workflows/rnaseq/main.nf index 8c3b99ba3..1b7309f7d 100755 --- a/workflows/rnaseq/main.nf +++ b/workflows/rnaseq/main.nf @@ -222,28 +222,28 @@ workflow RNASEQ { BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_GENOME ( ch_genome_bam.join(ch_genome_bam_index, by: [0]) ) - umi_dedup_genome = BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_GENOME - ch_multiqc_files = ch_multiqc_files.mix(umi_dedup_genome.out.dedup_stats.collect{it[1]}.ifEmpty([])) + UMI_DEDUP_GENOME = BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_GENOME + ch_multiqc_files = ch_multiqc_files.mix(UMI_DEDUP_GENOME.out.dedup_stats.collect{it[1]}.ifEmpty([])) } else if (params.umi_dedup_tool == "umitools") { BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME ( ch_genome_bam.join(ch_genome_bam_index, by: [0]), params.umitools_dedup_stats ) - umi_dedup_genome = BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME - ch_multiqc_files = ch_multiqc_files.mix(umi_dedup_genome.out.deduplog.collect{it[1]}) + UMI_DEDUP_GENOME = BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME + ch_multiqc_files = ch_multiqc_files.mix(UMI_DEDUP_GENOME.out.deduplog.collect{it[1]}) } else { error("Unknown umi_dedup_tool '${params.umi_dedup_tool}'") } - ch_genome_bam = umi_dedup_genome.out.bam - ch_genome_bam_index = umi_dedup_genome.out.bai - ch_multiqc_files = ch_multiqc_files.mix(umi_dedup_genome.out.stats.collect{it[1]}) - ch_multiqc_files = ch_multiqc_files.mix(umi_dedup_genome.out.flagstat.collect{it[1]}) - ch_multiqc_files = ch_multiqc_files.mix(umi_dedup_genome.out.idxstats.collect{it[1]}) + ch_genome_bam = UMI_DEDUP_GENOME.out.bam + ch_genome_bam_index = UMI_DEDUP_GENOME.out.bai + ch_multiqc_files = ch_multiqc_files.mix(UMI_DEDUP_GENOME.out.stats.collect{it[1]}) + ch_multiqc_files = ch_multiqc_files.mix(UMI_DEDUP_GENOME.out.flagstat.collect{it[1]}) + ch_multiqc_files = ch_multiqc_files.mix(UMI_DEDUP_GENOME.out.idxstats.collect{it[1]}) if (params.bam_csi_index) { - ch_genome_bam_index = umi_dedup_genome.out.csi + ch_genome_bam_index = UMI_DEDUP_GENOME.out.csi } - ch_versions = ch_versions.mix(umi_dedup_genome.out.versions) + ch_versions = ch_versions.mix(UMI_DEDUP_GENOME.out.versions) // Co-ordinate sort, index and run stats on transcriptome BAM BAM_SORT_STATS_SAMTOOLS ( @@ -258,20 +258,20 @@ workflow RNASEQ { BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_TRANSCRIPTOME ( ch_transcriptome_sorted_bam.join(ch_transcriptome_sorted_bai, by: [0]) ) - umi_dedup_transcriptome = BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_TRANSCRIPTOME + UMI_DEDUP_TRANSCRIPTOME = BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_TRANSCRIPTOME } else if (params.umi_dedup_tool == "umitools") { BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_TRANSCRIPTOME ( ch_transcriptome_sorted_bam.join(ch_transcriptome_sorted_bai, by: [0]), params.umitools_dedup_stats ) - umi_dedup_transcriptome = BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_TRANSCRIPTOME + UMI_DEDUP_TRANSCRIPTOME = BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_TRANSCRIPTOME } else { error("Unknown umi_dedup_tool '${params.umi_dedup_tool}'") } // Name sort BAM before passing to Salmon SAMTOOLS_SORT ( - umi_dedup_transcriptome.out.bam, + UMI_DEDUP_TRANSCRIPTOME.out.bam, ch_fasta.map { [ [:], it ] } ) @@ -398,27 +398,27 @@ workflow RNASEQ { BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_GENOME ( ch_genome_bam.join(ch_genome_bam_index, by: [0]), ) - umi_dedup_genome = BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_GENOME - ch_multiqc_files = ch_multiqc_files.mix(umi_dedup_genome.out.dedup_stats.collect{it[1]}.ifEmpty([])) + UMI_DEDUP_GENOME = BAM_DEDUP_STATS_SAMTOOLS_UMICOLLAPSE_GENOME + ch_multiqc_files = ch_multiqc_files.mix(UMI_DEDUP_GENOME.out.dedup_stats.collect{it[1]}.ifEmpty([])) } else if (params.umi_dedup_tool == "umitools") { BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME ( ch_genome_bam.join(ch_genome_bam_index, by: [0]), params.umitools_dedup_stats ) - umi_dedup_genome = BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME - ch_multiqc_files = ch_multiqc_files.mix(umi_dedup_genome.out.deduplog.collect{it[1]}) + UMI_DEDUP_GENOME = BAM_DEDUP_STATS_SAMTOOLS_UMITOOLS_GENOME + ch_multiqc_files = ch_multiqc_files.mix(UMI_DEDUP_GENOME.out.deduplog.collect{it[1]}) } else { error("Unknown umi_dedup_tool '${params.umi_dedup_tool}'") } - ch_genome_bam = umi_dedup_genome.out.bam - ch_genome_bam_index = umi_dedup_genome.out.bai - ch_multiqc_files = ch_multiqc_files.mix(umi_dedup_genome.out.stats.collect{it[1]}) - ch_multiqc_files = ch_multiqc_files.mix(umi_dedup_genome.out.flagstat.collect{it[1]}) - ch_multiqc_files = ch_multiqc_files.mix(umi_dedup_genome.out.idxstats.collect{it[1]}) + ch_genome_bam = UMI_DEDUP_GENOME.out.bam + ch_genome_bam_index = UMI_DEDUP_GENOME.out.bai + ch_multiqc_files = ch_multiqc_files.mix(UMI_DEDUP_GENOME.out.stats.collect{it[1]}) + ch_multiqc_files = ch_multiqc_files.mix(UMI_DEDUP_GENOME.out.flagstat.collect{it[1]}) + ch_multiqc_files = ch_multiqc_files.mix(UMI_DEDUP_GENOME.out.idxstats.collect{it[1]}) if (params.bam_csi_index) { - ch_genome_bam_index = umi_dedup_genome.out.csi + ch_genome_bam_index = UMI_DEDUP_GENOME.out.csi } - ch_versions = ch_versions.mix(umi_dedup_genome.out.versions) + ch_versions = ch_versions.mix(UMI_DEDUP_GENOME.out.versions) } } From aa35d37ebaaeb3d86cf85ec7662d6f210321de45 Mon Sep 17 00:00:00 2001 From: Lorenzo Sola <47034913+LorenzoS96@users.noreply.github.com> Date: Thu, 7 Nov 2024 09:08:11 +0100 Subject: [PATCH 012/107] Update docs/usage/DEanalysis/index.md MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Matthias Hörtenhuber --- docs/usage/DEanalysis/index.md | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/usage/DEanalysis/index.md b/docs/usage/DEanalysis/index.md index 6c68014c0..e1e7a329c 100644 --- a/docs/usage/DEanalysis/index.md +++ b/docs/usage/DEanalysis/index.md @@ -1,5 +1,4 @@ --- -enableToc: false title: "nf-core/rnaseq tutorial" --- From b1ec267010751598691bad5bbab3904164212906 Mon Sep 17 00:00:00 2001 From: Lorenzo Sola <47034913+LorenzoS96@users.noreply.github.com> Date: Thu, 7 Nov 2024 09:08:28 +0100 Subject: [PATCH 013/107] Update docs/usage/DEanalysis/interpretation.md MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Matthias Hörtenhuber --- docs/usage/DEanalysis/interpretation.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/usage/DEanalysis/interpretation.md b/docs/usage/DEanalysis/interpretation.md index fb0f38a25..2257fcf59 100644 --- a/docs/usage/DEanalysis/interpretation.md +++ b/docs/usage/DEanalysis/interpretation.md @@ -33,7 +33,7 @@ By default, genes are coloured in blue if the padj is less than 0.1 and the log2 After filtering our genes of interest according to our threshold, let's have a look to our significatnt genes: -```bash +```tsv gene baseMean log2FoldChange lfcSE stat pvalue padj ENSG00000205726 121645.5908 2.894480 0.1515387 19.100600 2.496005e-81 5.840651e-79 ENSG00000142192 51065.3192 3.025489 0.1891258 15.997230 1.335883e-57 1.562983e-55 From 659c86b351e30d840ae53d5924ab3e9b7122f615 Mon Sep 17 00:00:00 2001 From: Lorenzo Sola <47034913+LorenzoS96@users.noreply.github.com> Date: Thu, 7 Nov 2024 09:08:39 +0100 Subject: [PATCH 014/107] Update docs/usage/DEanalysis/rnaseq.md MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Matthias Hörtenhuber --- docs/usage/DEanalysis/rnaseq.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/usage/DEanalysis/rnaseq.md b/docs/usage/DEanalysis/rnaseq.md index a801520a1..f10373da3 100644 --- a/docs/usage/DEanalysis/rnaseq.md +++ b/docs/usage/DEanalysis/rnaseq.md @@ -17,7 +17,7 @@ In each process, the users can choose among a range of different options. Import The number of reads and the number of biological replicates are two critical factors that researchers need to carefully consider during the design of a RNA-seq experiment. While it may seem intuitive that having a large number of reads is always desirable, an excessive number can lead to unnecessary costs and computational burdens, without providing significant improvements in quality of the data. Instead, it is often more beneficial to prioritise the number of biological replicates, as it allows to capture the natural biological variation of the data. Biological replicates involve collecting and sequencing RNA from distinct biological samples (e.g., different individuals, tissues, or time points), helping to detect genuine changes in gene expression. -!!! note +::: note This concept must not be confused with technical replicates that asses the technical variability of the sequencing platform by sequencing the same RNA sample multiple time. From f2b34d0673d03dc2b9da3c85d90e1fad98133824 Mon Sep 17 00:00:00 2001 From: Lorenzo Sola <47034913+LorenzoS96@users.noreply.github.com> Date: Thu, 7 Nov 2024 09:08:55 +0100 Subject: [PATCH 015/107] Update docs/usage/DEanalysis/rnaseq.md MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Matthias Hörtenhuber --- docs/usage/DEanalysis/rnaseq.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/usage/DEanalysis/rnaseq.md b/docs/usage/DEanalysis/rnaseq.md index f10373da3..2c410b4bd 100644 --- a/docs/usage/DEanalysis/rnaseq.md +++ b/docs/usage/DEanalysis/rnaseq.md @@ -19,7 +19,8 @@ The number of reads and the number of biological replicates are two critical fac ::: note - This concept must not be confused with technical replicates that asses the technical variability of the sequencing platform by sequencing the same RNA sample multiple time. +This concept must not be confused with technical replicates that asses the technical variability of the sequencing platform by sequencing the same RNA sample multiple time. +::: To obtain optimal results, it is crucial to balance the number of biological replicates and sequencing depth. While deeper sequencing improves the detection of lowly expressed genes, it reaches a plateau, beyond which no additional benefits are gained. Statistical power calculations can inform experimental design by estimating the optimal number of reads and replicates required. For instance, this approach helps establish a suitable log2 fold change (log2FC) threshold for the DE analysis. By incorporating multiple biological replicates into the design and optimizing sequencing depth, researchers can enhance the statistical power of the analysis, reducing the number of false positive results and increasing the reliability of the findings. From c8c7ef84ef84c888c460798aa7a5eebbbea31c90 Mon Sep 17 00:00:00 2001 From: Lorenzo Sola <47034913+LorenzoS96@users.noreply.github.com> Date: Thu, 7 Nov 2024 09:10:17 +0100 Subject: [PATCH 016/107] Update docs/usage/DEanalysis/rnaseq.md MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Matthias Hörtenhuber --- docs/usage/DEanalysis/rnaseq.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/usage/DEanalysis/rnaseq.md b/docs/usage/DEanalysis/rnaseq.md index 2c410b4bd..094a60273 100644 --- a/docs/usage/DEanalysis/rnaseq.md +++ b/docs/usage/DEanalysis/rnaseq.md @@ -67,7 +67,7 @@ In the following sections we will: Following the considerations above, we will first of all edit the `nextflow.config` file in our working directory to add a new genome. It is sufficient to add the following code to the `parameters` directive in the config. -```groovy +```groovy title="nextflow.config" igenomes_base = '/workspace/gitpod/training/data/refs/' genomes { 'GRCh38chr21' { From ae16c2ccaf9b533434dabc77ed7c5ba5e6b1547d Mon Sep 17 00:00:00 2001 From: LorenzoS96 <47034913+LorenzoS96@users.noreply.github.com> Date: Thu, 7 Nov 2024 09:15:53 +0100 Subject: [PATCH 017/107] update index.md --- docs/usage/DEanalysis/index.md | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/docs/usage/DEanalysis/index.md b/docs/usage/DEanalysis/index.md index e1e7a329c..68e35bb99 100644 --- a/docs/usage/DEanalysis/index.md +++ b/docs/usage/DEanalysis/index.md @@ -1,12 +1,13 @@ --- -title: "nf-core/rnaseq tutorial" +order: 1 --- -## Welcome + +# Welcome These pages are a tutorial workshop for the [Nextflow](https://www.nextflow.io) pipeline [nf-core/rnaseq](https://nf-co.re/rnaseq). -In this workshop, we will recap the application of next generation sequencing to identify differentially expressed genes. You will learn how to use the rnaseq pipeline out this data-intensive workflow efficiently. We will cover topics such as configuration of the pipeline, code execution and data interpretation. +In this workshop, we will recap the application of next generation sequencing to identify differentially expressed genes. You will learn how to use the rnaseq pipeline to carry out this data-intensive workflow efficiently. We will cover topics such as configuration of the pipeline, code execution and data interpretation. Please note that this is not an introductory workshop, and we will assume some basic familiarity with Nextflow. @@ -19,6 +20,7 @@ By the end of this workshop, you will be able to: Let's get started! + ## Running with Gitpod In order to run this using GitPod, please make sure: @@ -28,16 +30,20 @@ In order to run this using GitPod, please make sure: Now you're all set and can use the following button to launch the service: + [![Open in GitPod](https://img.shields.io/badge/Gitpod-%20Open%20in%20Gitpod-908a85?logo=gitpod)](https://gitpod.io/#https://github.com/lescai-teaching/rnaseq-tutorial) + + ## Additional documentation - You can find detailed documentation on **Nextflow** [here](https://www.nextflow.io/docs/latest/) - You can find additional training on [these pages](https://training.nextflow.io) + ## Credits & Copyright -This training material has been written and completed during the [nf-core](https://nf-co.re) Hackathon in Barcellona, 2024, by Lorenzo Sola, Francesco Lescai and Mariangela Santorsola, with special thanks to Victoria Cepeda for her contributions to the tutorial's revision. The tutorial is aimed at anyone who is interested in using nf-core pipelines for their studies or research activities. +This training material has been written and completed by [Lorenzo Sola](https://github.com/LorenzoS96), [Francesco Lescai](https://github.com/lescai), and [Mariangela Santorsola](https://github.com/msantorsola) during the [nf-core](https://nf-co.re) Hackathon in Barcellona, 2024. Thank you to [Victoria Cepeda](https://github.com/vcepeda) for her contributions to the tutorial's revision. The tutorial is aimed at anyone who is interested in using nf-core pipelines for their studies or research activities. The Docker image and Gitpod environment used in this repository have been created by [Seqera](https://seqera.io) but have been made open-source ([CC BY-NC-ND](https://creativecommons.org/licenses/by-nc-nd/4.0/)) for the community. 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