paper edits

paper/paper.bib

@@ -388,6 +388,27 @@ @article{ondov16_mashfast
   file = {/home/kevin/work/bibliography/pdfs/ondov16_mash__genome_biology__mash.pdf}
 }
 
+@article{pedersen18_mosdepthquick,
+  title = {Mosdepth: Quick Coverage Calculation for Genomes and Exomes},
+  shorttitle = {Mosdepth},
+  author = {Pedersen, Brent S. and Quinlan, Aaron R.},
+  date = {2018-03-01},
+  journaltitle = {Bioinformatics (Oxford, England)},
+  shortjournal = {Bioinformatics},
+  volume = {34},
+  number = {5},
+  eprint = {29096012},
+  eprinttype = {pmid},
+  pages = {867--868},
+  issn = {1367-4811},
+  doi = {10.1093/bioinformatics/btx699},
+  abstract = {SUMMARY: Mosdepth is a new command-line tool for rapidly calculating genome-wide sequencing coverage. It measures depth from BAM or CRAM files at either each nucleotide position in a genome or for sets of genomic regions. Genomic regions may be specified as either a BED file to evaluate coverage across capture regions, or as a fixed-size window as required for copy-number calling. Mosdepth uses a simple algorithm that is computationally efficient and enables it to quickly produce coverage summaries. We demonstrate that mosdepth is faster than existing tools and provides flexibility in the types of coverage profiles produced. AVAILABILITY AND IMPLEMENTATION: mosdepth is available from https://github.com/brentp/mosdepth under the MIT license. CONTACT: bpederse@gmail.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.},
+  langid = {english},
+  pmcid = {PMC6030888},
+  keywords = {Algorithms,Exome Sequencing,{Genome, Human},Genomics,High-Throughput Nucleotide Sequencing,Humans,{Sequence Analysis, DNA},Software},
+  file = {/home/kevin/work/bibliography/pdfs/pedersen18_mosdepthquick__bioinformatics_(oxford,_england)__mosdepth.pdf;/home/kevin/work/bibliography/zotdir/storage/6NAYGY2D/Pedersen and Quinlan - 2018 - Mosdepth quick coverage calculation for genomes a.pdf}
+}
+
 @article{regalado20_combining,
   title = {Combining Whole-Genome Shotgun Sequencing and {{rRNA}} Gene Amplicon Analyses to Improve Detection of Microbe–Microbe Interaction Networks in Plant Leaves},
   author = {Regalado, Julian and Lundberg, Derek S. and Deusch, Oliver and Kersten, Sonja and Karasov, Talia and Poersch, Karin and Shirsekar, Gautam and Weigel, Detlef},

paper/paper.md

@@ -59,25 +59,25 @@ Across the entire pipeline, we operate on 'sample sets', named groups of one or
 
 ## Stage 1: Raw reads to per-sample reads
 
-Input data consists FASTQ files per **run** of each **library** corresponding to a **sample**. For each **run-lib** (one run of one library), we use `AdapterRemoval` [@schubert16_adapterremoval] to remove low quality or adaptor sequences, and to merge overlapping read pairs. We use `FastQC` to summarise sequence QC before and after `AdaptorRemoval`. 
+Input data consists of FASTQ files per **run** of each **library** corresponding to a **sample**. For each **runlib** (one run of one library), Acanthophis uses `AdapterRemoval` [@schubert16_adapterremoval] to remove low quality and adaptor sequences, and optionally to merge overlapping read pairs. It then uses `FastQC` to summarise sequence QC before and after `AdaptorRemoval`. 
 
 ## Stage 2: Alignment to reference(s)
 
-For read alignment to reference genomes we provide several configurable aligners, currently `BWA MEM` [@li13_aligningsequence], `NGM` [@sedlazeck13_nextgenmapfast], and `minimap2` [@li18_minimap2pairwise;@li21_newstrategies]. We then merge per-runlib BAMs to per-sample BAMs, and use `samtools markdup` [@li09_sequencealignment;@danecek21_twelveyears] to mark duplicate reads. Input reference genomes should be uncompressed, `samtools faidx`ed FASTA files. 
+To align reads to reference genomes, Acanthophis can use any of `BWA MEM` [@li13_aligningsequence], `NGM` [@sedlazeck13_nextgenmapfast], and `minimap2` [@li18_minimap2pairwise;@li21_newstrategies]. Then, Acanthophis merges per-runlib BAMs to per-sample BAMs, and uses `samtools markdup` [@li09_sequencealignment;@danecek21_twelveyears] to mark duplicate reads. Input reference genomes should be uncompressed, `samtools faidx`ed FASTA files. 
 
 ## Stage 3: Variant Calling
 
-We provide `bcftools mpileup` or `freebayes` to call raw variants, using priors and thresholds configurable for each sample set. We then normalise variants with `bcftools norm`, split multiallelic variants, filter each allele with per-sample set filters, and combine filter-passing alleles back into unique sites. Resulting variants are indexed and statistics calculated (bcftools stats). To parallelize variant calling: either a static list of non-overlapping genome windows is used (as supplied in a BED file), or mosdepth is used to break the genome into buckets with approximately equal amounts of data.
+Acanthophis uses `bcftools mpileup` and/or `freebayes` to call raw variants, using priors and thresholds configurable for each sample set. It then normalises variants with `bcftools norm`, splits multi-allelic variants, filters each allele with per-sample set filters, and combines filter-passing alleles back into unique sites, merges region-level VCFs, indexes, and calculates statistics on these final VCF files. Acanthophis provides two alternative approaches to parallelize variant calling: either a static list of non-overlapping genome windows (supplied in a BED file), or genome bins with approximately equal amounts of data, which are automatically generated using mosdepth [@pedersen18_mosdepthquick].
 
 ## Stage 4: Taxon profiling
 
-We use any of Kraken 2 [@wood19_improved], Bracken [@lu17_brackenestimating], Kaiju [@menzel16_fastsensitive], Centrifuge [@kim16_centrifugerapid], and Diamond [@buchfink15_fastsensitive] to create taxonomic profiles for each sample against any number of supplied databases. We then use taxpasta [@beber23_taxpastataxonomic] to combine multiple profiles into tables for easy downstream use.
+Acathophis uses any of Kraken 2 [@wood19_improved], Bracken [@lu17_brackenestimating], Kaiju [@menzel16_fastsensitive], Centrifuge [@kim16_centrifugerapid], and Diamond [@buchfink15_fastsensitive] to create taxonomic profiles for each sample against any number of supplied databases. We then use taxpasta [@beber23_taxpastataxonomic] to combine multiple profiles into tables for easy downstream use.
 
 ## Stage 5: *De novo* Estimates of Genetic Dissimilarity
 
-Acanthophis can use either `kWIP` [@murray17_kwipkmer] or Mash [@ondov16_mashfast] to estimate genetic distances between samples without alignment to a reference genome. These features first sketch reads into kmer sketches, and then calculate pairwise distances among samples.
+Acanthophis can use either `kWIP` [@murray17_kwipkmer] or Mash [@ondov16_mashfast] to estimate genetic distances between samples without alignment to a reference genome. These features first sketch reads into k-mer sketches, and then calculate pairwise distances among samples.
 
-## Stage 5: Reporting and Statistics
+## Stage 6: Reporting and Statistics
 
 Throughout all pipeline stages, various tools output summaries of their actions and/or outputs. We optionally combine these into unified reports by pipeline stage and sample set using MultiQC [@ewels16_multiqcsummarize].