[{"_1":2,"_169":-5,"_170":-5},"loaderData",{"_3":4,"_13":14,"_21":22,"_144":145},"root",{"_5":-5,"_6":-7,"_7":8,"_9":-7,"_10":11,"_12":-7},"currentUser","primaryEmail","siteVerification","6LeIxAcTAAAAAJcZVRqyHh71UMIEGNQ_MXjiZKhI","toast","enableDev",false,"incidentStatus","routes/_layout",{"_15":-5,"_16":17,"_18":17,"_19":11,"_20":11},"username","isAskGordonEnabled",true,"isUsingNewTemplate","showMinimalLanding","showHoneypotTrap","routes/_layout.r.$namespace.$reponame",{"_23":24,"_25":-5,"_26":11,"_27":11,"_28":29,"_30":31,"_32":-5,"_33":11,"_34":35,"_84":-5,"_85":86,"_135":-5,"_136":11,"_137":17,"_138":11,"_139":-5,"_140":11,"_141":-5,"_142":143},"canonicalUrl","https://hub.docker.com/r/dcroote/tracer/tags","mcpServer","canSeeDockerfile","canSeeAutobuilds","namespace","dcroote","reponame","tracer","cloudBuildSource","isAutobuildsAvailable","repo",{"_36":37,"_38":39,"_41":42,"_43":44,"_45":11,"_46":47,"_48":49,"_50":51,"_53":31,"_28":29,"_54":55,"_56":40,"_57":58,"_59":60,"_61":62,"_63":-5,"_64":58,"_65":66,"_67":11,"_68":29,"_69":70,"_75":17,"_76":77,"_81":29,"_82":83},"categories",[],"content_types",[40],"image","date_registered","2018-07-31T21:37:57.594159Z","description","Forked from teichlab/tracer. Used for testing CDR3 extraction from updated IgBLAST command","is_private","last_updated","2018-08-31T20:46:34.861262Z","last_modified","2024-10-16T13:48:34.145251Z","media_types",[52],"application/vnd.docker.container.image.v1+json","name","pull_count",1381,"repository_type","star_count",0,"status",1,"status_description","active","source","collaborator_count","full_description","# TraCeR\nTraCeR - reconstruction of T cell receptor sequences from single-cell RNA-seq data.\n\n## Contents ##\n1. [Introduction](#introduction)\n2. [Installation](#installation)\n3. [Setup](#setup)\n4. [Testing](#testing-tracer)\n5. [Usage](#using-tracer)\n\t- [*Assemble*](#assemble-tcr-reconstruction)\n - [*Summarise*](#summarise-summary-and-clonotype-networks)\n6. [Docker image](#docker-image)\n\n\n## Introduction\nThis 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. \n\nFor 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](http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.3800.html) or the [bioRxiv preprint](http://biorxiv.org/content/early/2015/08/28/025676) that preceded it.\n\nPlease email questions / problems to mjt.stubbington \\[at\\] gmail.com.\n\n_Also, please note that I (Mike) created and developed TraCeR while I was at EMBL-EBI and subsequently at the Sanger Institute. Now that I have left Sanger, I will continue to maintain the code when possible **as a personal project** independent of any employment that I have_\n\n## Installation\nTraCeR 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. \n\nTraCeR relies on several additional tools and Python modules that you should install.\n\nNote that TraCeR is compatible with both Python 2 and 3.\n\n### Pre-requisites\n\n#### Software \n1. [Bowtie2](http://bowtie-bio.sourceforge.net/bowtie2/index.shtml) - required for alignment of reads to synthetic TCR genomes.\n2. [Trinity](https://github.com/trinityrnaseq/trinityrnaseq/wiki) - 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](https://github.com/Teichlab/tracer#trinity-options).\n - Please note that Trinity requires a working installation of [Bowtie v1](http://bowtie-bio.sourceforge.net).\n3. [IgBLAST](http://www.ncbi.nlm.nih.gov/igblast/faq.html#standalone) - required for analysis of assembled contigs. (ftp://ftp.ncbi.nih.gov/blast/executables/igblast/release/).\n4. [makeblastdb](ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/ ) - **optional** but required if you want to use TraCeR's `build` mode to make your own references.\n5. Software for quantification of TCR expression:\n * [Kallisto](http://pachterlab.github.io/kallisto/), or alternatively\n * [Salmon](https://github.com/COMBINE-lab/salmon/releases).\n6. [Graphviz](http://www.graphviz.org) - Dot and Neato drawing programs required for visualisation of clonotype graphs. This is optional - see the [`--no_networks` option](#options-1) to [`summarise`](#summarise-summary-and-clonotype-networks).\n\n##### Installing IgBlast \nDownloading the executable files from `ftp://ftp.ncbi.nih.gov/blast/executables/igblast/release/` 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 `optional_file` directory (ftp://ftp.ncbi.nih.gov/blast/executables/igblast/release/optional_file/) and put them into the same directory as the igblast executable. This is also described in the igblast README file.\n\nYou should also ensure to set the `$IGDATA` environment variable to point to the location of the IgBlast executable. For example run `export IGDATA=//igblast/1.4.0/bin`.\n\n## Setup \nTo set up the python dependencies, use the requirements file:\n\n pip install -r requirements.txt\n\nIt is **highly** recommended that numpy and biopython are first installed through your system's package manager or conda.\n\nNote: Seaborn depends on the module statsmodels, which if updated through other packages may cause problems in Seaborn. If such issues arise, try to uninstall statsmodels and install again:\n \n conda uninstall statsmodels --yes\n conda install -c taugspurger statsmodels=0.8.0 \n\nThe tracer module is then installed using:\n\n python setup.py install\n\nThis will add the binary 'tracer' to your local bin folder, which can then be run from anywhere.\n\n_Note that installing TraCeR using this method requires you to specify the location of the originally downloaded files in your config file ([see below](#tracer-directory))._\n\nIf you would like to contribute to TraCeR, you can set up a development version with\n\n python setup.py develop\n\nWhich will make TraCeR accessible in your python environment, and incorporate local updates to the code.\n\nOnce the prerequisites above are installed and working you're ready to tell TraCeR where to find them.\n\nTraCeR uses a configuration file to point it to the locations of files that it needs and a couple of other options.\nAn example configuration file is included in the repository - `tracer.conf`.\n\nTraCeR looks for the configuration file, in descending order of priority, from the following sources:\n1. The `-c` option used at run time for any of the TraCeR modes\n2. The environmental variable `TRACER_CONF`, which can be set to point to a default location\n3. The default global location of `~/.tracerrc`\n4. If all else fails, the provided example `tracer.conf` in the directory is used\n\n**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 `//tracer/resources/Mmus/igblast_dbs`.\n\n### External tool locations \nTracer will look in your system's `PATH` for external tools. You can override this behaviour by editing your `~/.tracerrc`.\nEdit `~/.tracerrc` (or a copy) so that the paths within the `[tool_locations]` section point to the executables for all of the required tools.\n\n\t[tool_locations]\n\t#paths to tools used by TraCeR for alignment, quantitation, etc\n\tbowtie2_path = /path/to/bowtie2\n\tbowtie2-build_path = /path/to/bowtie2-build\n\tigblast_path = /path/to/igblastn\n\tmakeblastdb_path = /path/to/makeblastdb\n\tkallisto_path = /path/to/kallisto\n\tsalmon_path = /path/to/salmon\n\ttrinity_path = /path/to/trinity\n\tdot_path = /path/to/dot\n\tneato_path = /path/to/neato\n\t\t\n\t\t\n#### Trinity options \n##### Jellyfish memory \n\t[trinity_options]\n\t#line below specifies maximum memory for Trinity Jellyfish component. Set it appropriately for your environment.\n\tmax_jellyfish_memory = 1G\n\nTrinity needs to know the maximum memory available to it for the Jellyfish component. Specify this here.\n\n\n#### Trinity version \n #uncomment the line below to explicitly specify Trinity version. Options are '1' or '2'\n #trinity_version = 2\n\nTraCeR will automatically detect the version of Trinity you have installed. You can also explicitly specify it here if you wish.\n\n#### Trinity with short reads (<50 bases)\n\nIf you're running Trinity with read lengths that are shorter than 50 bases, you'll be restricted to using the Inchworm component of Trinity, which does draft contig assembly via greedy kmer extension. In your 'tracer.conf', uncomment the 'inchworm_only=True' to activate short read mode, and uncomment the 'trinity_kmer_length' as well. You can change the trinity_kmer_length value as needed; 17 is the shortest value, and the default length in Trinity is 25. Trinity v2 should be used in this mode.\n\n\n\n##### HPC configuration \n #uncomment the line below if you've got a configuration file for Trinity to use a computing grid #\n trinity_grid_conf = /path/to/trinity/grid.conf\n\nTrinity 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.\n \n\n#### IgBLAST options \n##### Receptor type \n igblast_seqtype = TCR\n\nType of sequence to be analysed. Since TraCeR currently only works with TCR sequences, there's no need to change this. \n\n#### Base transcriptomes for Kallisto/Salmon \n\t[base_transcriptomes]\n\tMmus = /path/to/kallisto/transcriptome_for_Mmus\n\tHsap = /path/to/kallisto/transcriptome_for_Hsap\n\nLocation 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).\n\n#### Base indices for Kallisto/Salmon\n\t[salmon_base_indices]\n\tMmus = /path/to/salmon/index_for_Mmus\n\tHsap = /path/to/salmon/index_for_Hsap\n\t[kallisto_base_indices]\n\tMmus = /path/to/kallisto/index_for_Mmus\n\tHsap = /path/to/kallisto/index_for_Hsap\n\nLocation of Kallisto/Salmon indices built (exclusively) from corresponding `[base_transcriptomes]`. These indices are only needed when option `--small_index` is used in *Assemble* mode (see below). \n\n#### Salmon options\n\t[salmon_options]\n\tlibType = A\n\tkmerLen = 31\n\n* 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.\n* 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. \n\nSee salmon [documentation](http://salmon.readthedocs.io/en/latest/salmon.html) for more details.\n\n### TraCeR directory\n\n [tracer_location]\n #Path to where TraCeR was originally downloaded\n tracer_path = /path/to/tracer\n\nLocation of the cloned TraCeR repository containing tracerlib, test_data, resources etc. Eg. `/user/software/tracer`.\nNeeded for localisation of resources and test_data if running TraCeR with the tracer binary. Note: this is the path to the **directory** and not the executable.\n\n## Testing TraCeR \nTraCeR 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:\n\n tracer test -p -c \n \n**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.\n\nYou can also pass the following two options to change the Graphviz output format or to prevent attempts to draw network graphs\n\n`-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. \n`--no_networks` : Don't try to draw clonotype network graphs. This is useful if you don't have a working installation of Graphviz.\n \nRunning `test` will peform the [`assemble`](#assemble-tcr-reconstruction) step using the small test dataset.\n It will then perform [`summarise`](#summarise-summary-and-clonotype-networks) using the assemblies that are generated along with pre-calculated output for two other cells (in `test_data/results`), dumping the data in the `output` directory as specified.\n\nCompare 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.\n\n\n## Using TraCeR \nTracer has three modes: *assemble*, *summarise* and *build*. \n\n*Assemble* takes fastq files of paired-end RNA-seq reads from a single-cell and reconstructs TCR sequences.\n\n*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. \n\n*Build* creates new combinatorial recombinomes for species other than the inbuilt Human and Mouse.\n\n\n### *Assemble*: TCR reconstruction \n\n#### Usage \n\n tracer assemble [options] [] \n\n\n##### Main arguments\n* `` : fastq file containing #1 mates from paired-end sequencing or all reads from single-end sequencing. \n* `` : fastq file containing #2 mates from paired-end sequencing. Do not use if your data are from single-end sequencing. \n* `` : name of the cell. This is arbitrary text that will be used for all subsequent references to the cell in filenames/labels etc. \n* `` : directory for output. Will be created if it doesn't exist. Cell-specific output will go into `//`. This path should be the same for every cell that you want to summarise together.\n\n\n##### Options \n\n* `-p/--ncores ` : number of processor cores available. This is passed to Bowtie2, Trinity, and Kallisto or Salmon. Default=1.\n* `-c/--config_file ` : config file to use. Default = `~/.tracerrc`\n* `--resource_dir`: the directory containing the resources required for alignment. By default this is the `resources` directory in this repository, but can be pointed to a user-built set of resources.\n* `-s/--species` : Species from which the T cells were derived. Options are `Mmus` or `Hsap` for mouse or human data. If you have defined new species using the [`build`](#build-build-combinatorial-recombinomes-for-a-given-species) mode, you should specify the same name here. Default = `Mmus`.\n* `--receptor_name` : If, for some reason, you've used a different receptor name when using [`build`](#build-build-combinatorial-recombinomes-for-a-given-species) then you should also specify it here. Default = `TCR`\n* `--loci` : The specific loci that you wish to assemble. TraCeR knows about alpha (`A`), beta (`B`), gamma (`G`) and delta (`D`) for mouse and human TCRs. Other locus names can be specified if you use [`build`](#build-build-combinatorial-recombinomes-for-a-given-species). By default, TraCeR will attempt to assemble alpha and beta sequences. To change this, pass a space-delimited list of locus names. For example, to only look for gamma/delta sequences use `--loci G D`. **Important** The way that this argument is parsed means that you **must** follow it with another command line option or the argument parser will get confused. For example: `--loci G D --p 1`. We know this [isn't ideal](https://github.com/Teichlab/tracer/issues/62).\n* `-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.\n* `-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`.\n* `-q/--quant_method` : Method used for expression quantification. Options are `-q salmon` and `-q kallisto` (default).\n* `--small_index` : Use this option to speed up expression quantification. The location of an index that is built (exclusively) from the corresponding `base_transcriptome` must be specified in the configuration file (under `[salmon_base_indices]` or `[kallisto_base_indices]`). Since this index does not contain any TCR sequences, it has to be built only once for each species and can then be used for all cells. When option `--small-index` is used, reads are first quantified with this `base_index` (this should lead to non-zero TPM numbers for all non-TCR transcripts present in the cell). After selecting all transcripts with non-zero TPM numbers, cell-specific TCR sequences (as constructed by Bowtie-Trinity-IgBlast) are appended to that list. Then a new Kallisto/Salmon index is built from the combined set (since this set contains only a subset of the `base_transcriptome`, this is fast). Finally, reads are quantified with this small index. \n* `--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.\n* `--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.\n* `--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.\n* `--invariant_sequences`: Custom invariant sequence file. Use the default example in 'resources/Mmus/invariant_seqs.csv'\n\n#### Output \n\nFor each cell, an `//` directory will be created. This will contain the following subdirectories.\n\n1. `//aligned_reads` \n This contains the output from Bowtie2 with the sequences of the reads that aligned to the synthetic genomes.\n\n2. `//Trinity_output` \n Contains fasta files for each locus where contigs could be assembled. Also two text files that log successful and unsuccessful assemblies.\n\n3. `//IgBLAST_output` \n Files with the output from IgBLAST for the contigs from each locus. \n\n4. `//unfiltered_TCR_seqs` \n Files describing the TCR sequences that were assembled prior to filtering by expression if necessary.\n - `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.\n - `_TCRseqs.fa` : fasta file containing full-length, reconstructed TCR sequences.\n - `.pkl` : Python [pickle](https://docs.python.org/2/library/pickle.html) file containing the internal representation of the cell and its recombinants as used by TraCeR. This is used in the summarisation steps.\n\n5. `//expression_quantification` \n Contains Kallisto/Salmon output with expression quantification of the entire transcriptome *including* the reconstructed TCRs. When option `--small_index` is used, this directory contains only the output of the quantification with the small index (built from reconstructed TCRs and only a subset of the base transcriptome; see above).\n\n6. `//filtered_TCR_seqs` \n 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**.\n\n\n### *Summarise*: Summary and clonotype networks \n\n#### Usage \n tracer summarise [options] \n\n##### Main argument \n* `` : directory containing subdirectories of each cell you want to summarise. \n* ``: the directory containing the resources required for alignment.\n By default this is the `resources` directory in this repository, but can be pointed to a user-built set of resources.\n\n##### Options \n* `-c/--config_file ` : config file to use. Default = `~/.tracerrc`\n* `-u/--use_unfiltered` : Set this flag to use unfiltered recombinants for summary and networks rather than the recombinants filtered by expression level. \n* `--resource_dir`: the directory containing the resources required for alignment. By default this is the `resources` directory in this repository, but can be pointed to a user-built set of resources.\n* `-s/--species` : Species from which the T cells were derived. Options are `Mmus` or `Hsap` for mouse or human data. If you have defined new species using the [`build`](#build-build-combinatorial-recombinomes-for-a-given-species) mode, you should specify the same name here. Default = `Mmus`.\n* `--receptor_name` : If, for some reason, you've used a different receptor name when using [`build`](#build-build-combinatorial-recombinomes-for-a-given-species) then you should also specify it here. Default = `TCR`\n* `--loci` : The specific loci that you wish to summarise. TraCeR knows about alpha (`A`), beta (`B`), gamma (`G`) and delta (`D`) for mouse and human TCRs. Other locus names can be specified if you use [`build`](#build-build-combinatorial-recombinomes-for-a-given-species). By default, TraCeR will attempt to summarise alpha and beta sequences. To change this, pass a space-delimited list of locus names. For example, to only look for gamma/delta sequences use `--loci G D`. **Important** The way that this argument is parsed means that you **must** follow it with another command line option or the argument parser will get confused. For example: `--loci G D --p 1`. We know this [isn't ideal](https://github.com/Teichlab/tracer/issues/62). Default = `A B`\n* `-i/--keep_invariant` : TraCeR attempts to identify invariant TCR cells by their characteristic TCRA gene segments. By default, these are removed before creation of clonotype networks. Setting this option retains the invariant cells in all stages. \n* `-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. \n* `--no_networks` : Don't try to draw clonotype network graphs. This is useful if you don't have a working installation of Graphviz.\n\n#### Output \nOutput is written to `/filtered_TCR_summary` or `/unfiltered_TCR_summary` depending on whether the `--use_unfiltered` option was set.\n\nThe following output files are generated:\n\n1. `TCR_summary.txt`\n Summary statistics describing successful TCR reconstruction rates and the numbers of cells with 0, 1, 2 or more recombinants for each locus.\n2. `recombinants.txt`\n List of TCR identifiers, lengths, productivities, and CDR3 sequences (nucleotide and amino acid) for each cell. **Note:** It's possible for non-productive rearrangements to still have a detectable CDR3 if a frameshift introduces a stop-codon after this region. In these cases, the CDR3 nucleotides and amino acids are still reported but are shown in lower-case.\n3. `reconstructed_lengths_TCR[A|B].pdf` and `reconstructed_lengths_TCR[A|B].txt`\n 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. \n4. `clonotype_sizes.pdf` and `clonotype_sizes.txt`\n Distribution of clonotype sizes as bar graph and text file.\n5. `clonotype_network_[with|without]_identifiers.`\n graphical representation of clonotype networks either with full recombinant identifiers or just lines indicating presence/absence of recombinants.\n6. `clonotype_network_[with|without]_identifiers.dot`\n files describing the clonotype networks in the [Graphviz DOT language](http://www.graphviz.org/doc/info/lang.html)\n \n### *Build*: Build Combinatorial Recombinomes for a Given Species\n\n#### Usage\n tracer build \n\n#### Main Arguments\n* `` : Species (e.g. Mmus).\n* `` : Name of receptor (e.g. TCR).\n* `` : Name of locus (e.g. A)\n* `` : Number of ambiguous N nucleotides between V and J\n* `` : Colour for the productive recombinants (optional). 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dcroote/tracer

Tags for dcroote/tracer