HELLO! TCRDIST2 HAS BEEN DEPRECIATED SEE TCRDIST3 and TCRREGEX

tcrdist2

tcrdist2 provides flexible distance measures for comparing T cell receptors

tcrdist2 is a python API-enabled toolkit for analyzing T-cell receptor repertoires. Some of the functionality and code is adapted from the original tcr-dist package which was released with the publication of Dash et al. Nature (2017) doi:10.1038/nature22383. This package contains a new API for accessing the features of tcr-dist, as well as many new features that expand the T cell receptor analysis pipeline.

The original code for replicating analysis performed in the manuscript can be found here.

HELLO! TCRDIST2 HAS BEEN DEPRECIATED SEE TCRDIST3 and TCRREGEX

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Documentation

Documentation, installation instructions, information about dependencies, and examples can be found at tcrdist2.readthedocs.io

Installation

The development version of tcrdist2 compatible with Python 3.6 or later. It can be cloned or installed directly.

  pip install git+https://github.com/kmayerb/tcrdist2.git@API2

To test out code used in the documented examples. Create a venv. Install tcrdist2 with all dependencies.

python3 -m venv ./tenv
source tenv/bin/activate
pip install git+https://github.com/kmayerb/tcrdist2.git

Quickly test installation

python -c "import tcrdist"

Development Files

If you wish, use the following set of commands to install testing files and legacy blast (replace the download_from argument to 'dropbox_osx' or 'dropbox_linux' based on your operating system).

python -c "import tcrdist as td; td.install_test_files.install_test_files()"
python -c "import tcrdist as td; td.setup_db.install_all_next_gen()"
python -c "import tcrdist as td; td.setup_blast.install_blast_to_externals(download_from = 'dropbox_osx')"

Citing

Quantifiable predictive features define epitope-specific T cell receptor repertoires

Pradyot Dash, Andrew J. Fiore-Gartland, Tomer Hertz, George C. Wang, Shalini Sharma, Aisha Souquette, Jeremy Chase Crawford, E. Bridie Clemens, Thi H. O. Nguyen, Katherine Kedzierska, Nicole L. La Gruta, Philip Bradley & Paul G. Thomas

Nature (2017).

OLGA: fast computation of generation probabilities of B- and T-cell receptor amino acid sequences and motifs

Zachary Sethna, Yuval Elhanati, Curtis G Callan, Aleksandra M Walczak, Thierry Mora

Bioinformatics (2019)

(tcrdist2 incorporates OLGA for generation probability estimates)

Parasail: SIMD C library for global, semi-global, and local pairwise sequence alignments

Jeff Daily

BMC Bioinformatics (2016)

(tcrdist2 depends on Parasail for fast sequence alignment)