TraCeR

TraCeR - reconstruction of T cell receptor sequences from single-cell RNA-seq data.

IMPORTANT: Python dependencies have changed since the last release. Use the requirements file (detailed here) to update them if necessary.

Contents

1. Introduction
2. Installation
3. Setup
4. Testing
5. Usage
   • Assemble
   • Summarise

Introduction

This tool reconstructs the sequences of rearranged and expressed T cell receptor genes from single-cell RNA-seq data. It then uses the TCR sequences to identify cells that have the same receptor sequences and so derive from the same original clonally-expanded cell.

For more information on TraCeR, its validation and how it can be applied to investigate T cell populations during infection, see our paper in Nature Methods or the bioRxiv preprint that preceded it.

Please email questions / problems to [email protected]

Installation

TraCeR is written in Python and so can just be downloaded, made executable (with chmod u+x tracer) and run or run with python tracer. Download the latest version and accompanying files from www.github.com/teichlab/tracer.

TraCeR relies on several additional tools and Python modules that you should install.

Note that TraCeR is compatible with both Python 2 and 3.

Pre-requisites

Software

1. Bowtie2 - required for alignment of reads to synthetic TCR genomes.
2. Trinity - required for assembly of reads into TCR contigs. TraCeR now works with both version 1 and version 2 of Trinity. It should automatically detect the version that is installed or you can specify it in the config file.
   • Please note that Trinity requires a working installation of Bowtie v1.
3. IgBLAST - required for analysis of assembled contigs. (ftp://ftp.ncbi.nih.gov/blast/executables/igblast/release/).
4. makeblastdb - optional but required if you want to use TraCeR's build mode to make your own references.
5. Software for quantification of TCR expression:
   • Kallisto, or alternatively
   • [Salmon] (https://github.com/COMBINE-lab/salmon/releases).
6. Graphviz - Dot and Neato drawing programs required for visualisation of clonotype graphs. This is optional - see the --no_networks option to summarise.

Installing IgBlast

Downloading the executable files from ftp://ftp.ncbi.nih.gov/blast/executables/igblast/release/<version_number> is not sufficient for a working IgBlast installation. You must also download the internal_data directory (ftp://ftp.ncbi.nih.gov/blast/executables/igblast/release/internal_data) and put it into the same directory as the igblast executable. This is also described in the igblast README file.

You should also ensure to set the $IGDATA environment variable to point to the location of the IgBlast executable. For example run export IGDATA=/<path_to_igblast>/igblast/1.4.0/bin.

Python modules

1. Matplotlib
2. Seaborn
3. Biopython
4. Prettytable
5. Levenshtein
6. Networkx
7. It seems that v1.11 of Networkx behaves differently when writing dot files for use with Graphviz. If you have this (or later versions) you also need to install PyDotPlus.
8. Future for compatibility with Python 2.

Setup

To set up the python dependencies, use the requirements file:

pip install -r requirements.txt

It is highly recommended that numpy and biopython are first installed through your system's package manager or conda.

The tracer module is then installed using:

python setup.py install

This will add the binary 'tracer' to your local bin folder, which can then be run from anywhere.

If you would like to contribute to TraCeR, you can set up a development version with

python setup.py develop

Which will make TraCeR accessible in your python environment, and incorporate local updates to the code.

Once the prerequisites above are installed and working you're ready to tell TraCeR where to find them.

TraCeR uses a configuration file to point it to the locations of files that it needs and a couple of other options. An example configuration file is included in the repository - tracer.conf. By default, this is ~/.tracerrc. If tracer fails to find this file, it will use the tracer.conf in the repository. The -c option to the various tracer modules allows you to specify any other file to act as the configuration file.

Important: If you specify relative paths in the config file these will be used as relative to the main installation directory. For example, resources/Mmus/igblast_dbs will resolve to /<wherever you installed tracer>/tracer/resources/Mmus/igblast_dbs.

External tool locations

Tracer will look in your system's PATH for external tools. You can override this behaviour by editing your ~/.tracerrc. Edit ~/.tracerrc (or a copy) so that the paths within the [tool_locations] section point to the executables for all of the required tools.

[tool_locations]
#paths to tools used by TraCeR for alignment, quantitation, etc
bowtie2_path = /path/to/bowtie2
bowtie2-build_path = /path/to/bowtie2-build
igblast_path = /path/to/igblastn
makeblastdb_path = /path/to/makeblastdb
kallisto_path = /path/to/kallisto
salmon_path = /path/to/salmon
trinity_path = /path/to/trinity
dot_path = /path/to/dot
neato_path = /path/to/neato

Resource locations and necessary files

The tools used by TraCeR need a variety of additional files to work properly and to allow extraction of TCR-derived reads and expression quantification etc. The locations of these files are specified in the other sections of the configuration file and are detailed below.

Currently, organism-specific files (TCR gene sequences, synthetic genome indices, igblast_indices) for mouse and human are distributed with the source-code in the resources directory. There will soon be a build module that constructs all of the necessary synthetic genomes and indices from any collection of V and J gene sequences.

Bowtie synthetic genomes path

[bowtie2_options]
synthetic_genome_index_path = resources/Mmus/synthetic_genomes

This path specifies the directory that contains Bowtie2 indices constructed from all possible combinations of V and J segments for each locus.

Trinity options

Jellyfish memory

[trinity_options]
#line below specifies maximum memory for Trinity Jellyfish component. Set it appropriately for your environment.
max_jellyfish_memory = 1G

Trinity needs to know the maximum memory available to it for the Jellyfish component. Specify this here.

Trinity version

#uncomment the line below to explicitly specify Trinity version. Options are '1' or '2'
#trinity_version = 2

TraCeR will automatically detect the version of Trinity you have installed. You can also explicitly specify it here if you wish.

HPC configuration

#uncomment the line below if you've got a configuration file for Trinity to use a computing grid #
trinity_grid_conf = /path/to/trinity/grid.conf

Trinity can parallelise contig assembly by submitting jobs across a compute cluster. If you're running in such an environment you can specify an optional trinity config file here. See the Trinity documentation for more information.

IgBLAST options

Databases path

[IgBlast_options]
igblast_index_location = resources/Mmus/igblast_dbs

VDJ sequences

This path specifies the directory that contains IgBLAST database files for V, D and J genes. These files are named imgt_tcr_db_<SEGMENT>.fa.

imgt_seq_location = resources/Mmus/imgt_sequences

Path to fasta files with sequences for each V, D or J gene. Files are names TR<LOCUS><SEGMENT>.fa.

Receptor type

igblast_seqtype = TCR

Type of sequence to be analysed. Since TraCeR currently only works with TCR sequences, there's no need to change this.

Base transcriptomes for Kallisto/Salmon

[base_transcriptomes]
Mmus = /path/to/kallisto/transcriptome_for_Mmus
Hsap = /path/to/kallisto/transcriptome_for_Hsap

Location of the transcriptome fasta file to which the specific TCR sequences will be appended from each cell. Can be downloaded from http://bio.math.berkeley.edu/kallisto/transcriptomes/ and many other places. This must be a plain-text fasta file so decompress it if necessary (files from the Kallisto link are gzipped).

Salmon options

[salmon_options]
libType = A
kmerLen = 31

• Description of the type of sequencing library from which the reads come (containing, e.g., the relative orientation of paired end reads). As of version 0.7.0, Salmon also has the ability to automatically infer (i.e. guess) the library type based on how the first few thousand reads map to the transcriptome. Set libType = A for automatic detection.
• Salmon builds the quasi-mapping-based index, using an auxiliary k-mer hash over k-mers of length kmerLen. While quasi-mapping will make used of arbitrarily long matches between the query and reference, the k size selected here will act as the minimum acceptable length for a valid match. The value for kmerLen must be odd; its default and maximum value is 31.

See salmon documentation for more details.

Testing TraCeR

TraCeR comes with a small dataset in test_data/ (containing only TCRA or TCRB reads for a single cell) that you can use to test your installation and config file and confirm that all the prerequisites are working. Run it as:

tracer test -p <ncores> -c <config_file>

Note: The data used in the test are derived from mouse T cells so make sure that the config file points to the appropriate mouse resource files.

You can also pass the following two options to change the Graphviz output format or to prevent attempts to draw network graphs

-g/--graph_format : Output format for the clonotype networks. This is passed directly to Graphviz and so must be one of the options detailed at http://www.graphviz.org/doc/info/output.html.
--no_networks : Don't try to draw clonotype network graphs. This is useful if you don't have a working installation of Graphviz.

Running test will peform the assemble step using the small test dataset. It will then perform summarise using the assemblies that are generated along with pre-calculated output for two other cells (in test_data/results).

Compare the output in test_data/results/filtered_TCR_summary with the expected results in test_data/expected_summary. There should be three cells, each with one productive alpha, one productive beta, one non-productive alpha and one non-productive beta. Cells 1 and 2 should be in a clonotype.

Using TraCeR

Tracer has three modes: assemble, summarise and build.

Assemble takes fastq files of paired-end RNA-seq reads from a single-cell and reconstructs TCR sequences.

Summarise takes a set of directories containing output from the assemble phase (each directory represents a single cell) and summarises TCR recovery rates as well as generating clonotype networks.

Build creates new combinatorial recombinomes for species other than the inbuilt Human and Mouse.

Assemble: TCR reconstruction

Usage

tracer assemble [options] <file_1> [<file_2>] <cell_name> <output_directory>

Main arguments

• <file_1> : fastq file containing #1 mates from paired-end sequencing or all reads from single-end sequencing.
• <file_2> : fastq file containing #2 mates from paired-end sequencing. Do not use if your data are from single-end sequencing.
• <cell_name> : name of the cell. This is arbitrary text that will be used for all subsequent references to the cell in filenames/labels etc.
• <output_directory> : directory for output. Will be created if it doesn't exist. Cell-specific output will go into /<output_directory>/<cell_name>. This path should be the same for every cell that you want to summarise together.

Options

• -p/--ncores <int> : number of processor cores available. This is passed to Bowtie2, Trinity, and Kallisto or Salmon. Default=1.
• -c/--config_file <conf_file> : config file to use. Default = ~/.tracerrc
• -s/--species : Species from which the T cells were derived. Options are Mmus or Hsap for mouse or human data. This is only important for determination of iNKT cells in the summarise step because it defines the V segments that are indicative of iNKT cells. Default = Mmus.
• -r/--resume_with_existing_files : if this is set, TraCeR will look for existing output files and not re-run steps that already appear to have been completed. This saves time if TraCeR died partway through a step and you want to resume where it left off.
• -m/--seq_method : method by which to generate sequences for assessment of recombinant productivity. By default (-m imgt), TraCeR replaces all but the junctional sequence of each detected recombinant with the reference sequence from IMGT prior to assessing productivity of the sequence. This makes the assumption that sequence changes outside the junctional region are due to PCR/sequencing errors rather than being genuine polymorphisms. This is likely to be true for well-characterised mouse sequences but may be less so for human and other outbred populations. To determine productivity from only the assembled contig sequence for each recombinant use -m assembly.
• -q/--quant_method : Method used for expression quantification. Options are -q salmon and -q kallisto (default).
• --single_end : use this option if your data are single-end reads. If this option is set you must specify fragment length and fragment sd as below.
• --fragment_length : Estimated average fragment length in the sequencing library. Used for Kallisto quantification. Required for single-end data. Can also be set for paired-end data if you don't want Kallisto to estimate it directly.
• --fragment_sd : Estimated standard deviation of average fragment length in the sequencing library. Used for Kallisto quantification. Required for single-end data. Can also be set for paired-end data if you don't want Kallisto to estimate it directly.
• --invariant_sequences: Custom invariant sequence file. Use the default example in 'resources/Mmus/invariant_seqs.csv'

Output

For each cell, an /<output_directory>/<cell_name> directory will be created. This will contain the following subdirectories.

1. <output_directory>/<cell_name>/aligned_reads
   This contains the output from Bowtie2 with the sequences of the reads that aligned to the synthetic genomes.

2. <output_directory>/<cell_name>/Trinity_output
   Contains fasta files for each locus where contigs could be assembled. Also two text files that log successful and unsuccessful assemblies.

3. <output_directory>/<cell_name>/IgBLAST_output
   Files with the output from IgBLAST for the contigs from each locus.

4. <output_directory>/<cell_name>/unfiltered_TCR_seqs
   Files describing the TCR sequences that were assembled prior to filtering by expression if necessary.

   • unfiltered_TCRs.txt : text file containing TCR details. Begins with count of productive/total rearrangements detected for each locus. Then details of each detected recombinant.
   • <cell_name>_TCRseqs.fa : fasta file containing full-length, reconstructed TCR sequences.
   • <cell_name>.pkl : Python pickle file containing the internal representation of the cell and its recombinants as used by TraCeR. This is used in the summarisation steps.

5. <output_directory>/<cell_name>/expression_quantification
   Contains Kallisto/Salmon output with expression quantification of the entire transcriptome including the reconstructed TCRs.

6. <output_directory>/<cell_name>/filtered_TCR_seqs
   Contains the same files as the unfiltered directory above but these recombinants have been filtered so that only the two most highly expressed from each locus are retained. This resolves biologically implausible situtations where more than two recombinants are detected for a locus. This directory contains the final output with high-confidence TCR assignments.

Summarise: Summary and clonotype networks

Usage

tracer summarise [options] <input_dir>

Main argument

• <input_dir> : directory containing subdirectories of each cell you want to summarise.

Options

• -c/--config_file <conf_file> : config file to use. Default = ~/.tracerrc
• -u/--use_unfiltered : Set this flag to use unfiltered recombinants for summary and networks rather than the recombinants filtered by expression level.
• -i/--keep_inkt : TraCeR attempts to identify iNKT cells by their characteristic TCRA gene segments (TRAV11–TRAJ18). By default, these are removed before creation of clonotype networks. Setting this option retains the iNKT cells in all stages.
• -g/--graph_format : Output format for the clonotype networks. This is passed directly to Graphviz and so must be one of the options detailed at http://www.graphviz.org/doc/info/output.html.
• --no_networks : Don't try to draw clonotype network graphs. This is useful if you don't have a working installation of Graphviz.

Output

Output is written to <input_dir>/filtered_TCR_summary or <input_dir>/unfiltered_TCR_summary depending on whether the --use_unfiltered option was set.

The following output files are generated:

1. TCR_summary.txt Summary statistics describing successful TCR reconstruction rates and the numbers of cells with 0, 1, 2 or more recombinants for each locus.
2. recombinants.txt List of TCR identifiers, lengths and productivities for each cell.
3. reconstructed_lengths_TCR[A|B].pdf and reconstructed_lengths_TCR[A|B].txt Distribution plots (and text files with underlying data) showing the lengths of the VDJ regions from assembled TCR contigs. Longer contigs give higher-confidence segment assignments. Text files are only generated if at least one TCR is found for a locus. Plots are only generated if at least two TCRs are found for a locus.
4. clonotype_sizes.pdf and clonotype_sizes.txt Distribution of clonotype sizes as bar graph and text file.
5. clonotype_network_[with|without]_identifiers.<graph_format> graphical representation of clonotype networks either with full recombinant identifiers or just lines indicating presence/absence of recombinants.
6. clonotype_network_[with|without]_identifiers.dot files describing the clonotype networks in the Graphviz DOT language

Build: Build Combinatorial Recombinomes for a Given Species

Usage

tracer build <species> <receptor_name> <locus_name> <N_padding> <colour> <V_seqs> <J_seqs> <C_seqs> <D_seqs>

Main Arguments

• <species> : Species (e.g. Mmus).
• <receptor_name> : Name of receptor (e.g. TCR).
• <locus_name> : Name of locus (e.g. A)
• <N_padding> : Number of ambiguous N nucleotides between V and J
• <colour> : Colour for the productive recombinants (optional). Specify as HTML (e.g. E41A1C) or use "random"
• <V_seqs> : Fasta file containing V gene sequences
• <J_seqs> : Fasta file containing J gene sequences
• <C_seqs> : Fasta file containing single constant region sequence
• <D_seqs> : Fasta file containing D gene sequences (optional)

Options

• -f/--force_overwrite : Force overwrite of existing resources