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        "author": "Alex Rubinsteyn",
        "author_email": "alex.rubinsteyn@gmail.com",
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            "Development Status :: 3 - Alpha",
            "Environment :: Console",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: Apache Software License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Topic :: Scientific/Engineering :: Bio-Informatics"
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        "description": "[![Build Status](https://travis-ci.org/openvax/vaxrank.svg?branch=master)](https://travis-ci.org/openvax/vaxrank) [![Coverage Status](https://coveralls.io/repos/github/openvax/vaxrank/badge.svg?branch=master)](https://coveralls.io/github/openvax/vaxrank?branch=master)\n<a href=\"https://pypi.python.org/pypi/vaxrank/\">\n    <img src=\"https://img.shields.io/pypi/v/vaxrank.svg?maxAge=1000\" alt=\"PyPI\" />\n</a>\n\n# vaxrank\n\nSelection of mutated protein fragments for therapeutic personalized cancer vaccines.\n\n## Usage\n\n```sh\n\nvaxrank \\\n    --vcf test/data/b16.f10/b16.vcf \\\n    --bam test/data/b16.f10/b16.combined.bam \\\n    --vaccine-peptide-length 25 \\\n    --mhc-predictor netmhc \\\n    --mhc-alleles H2-Kb,H2-Db \\\n    --padding-around-mutation 5 \\\n    --output-ascii-report vaccine-peptides.txt \\\n    --output-pdf-report vaccine-peptides.pdf \\\n    --output-html-report vaccine-peptides.html\n```\n\n## Installation\n\nVaxrank can be installed using [pip](https://packaging.python.org/installing/#use-pip-for-installing):\n\n```\npip install vaxrank\n```\n\nNote: to generate PDF reports, you first need to install [wkhtmltopdf](http://wkhtmltopdf.org/), which you can do (on OS X) like so:\n\n```\nbrew install Caskroom/cask/wkhtmltopdf\n```\n\nVaxrank uses [PyEnsembl](https://github.com/openvax/pyensembl) for accessing information about the reference genome. You must install an Ensembl release corresponding to the reference genome associated with the mutations provided to Vaxrank.\n\nThe latest release for GRCh38 is Ensembl 93:\n```\npyensembl install --release 93 --species human\n```\n\nThe last release for GRCh37 is Ensembl 75:\n```\npyensembl install --release 75 --species human\n```\n\nIf your variants were called from alignments against hg19 then you can still use GRCh37 but should ignore mitochondrial variants.\n\n## Paper & Citation\n\nThere is a Vaxrank paper on biorxiv called [Vaxrank: A Computational Tool For Designing Personalized Cancer Vaccines](https://www.biorxiv.org/content/early/2017/05/27/142919) which can be cited as:\n\n    @article {Rubinsteyn142919,\n        author = {Rubinsteyn, Alex and Hodes, Isaac and Kodysh, Julia and Hammerbacher, Jeffrey},\n        title = {Vaxrank: A Computational Tool For Designing Personalized Cancer Vaccines},\n        year = {2017},\n        doi = {10.1101/142919},\n        publisher = {Cold Spring Harbor Laboratory},\n        abstract = {Therapeutic vaccines targeting mutant tumor antigens ({\\textquotedblleft}neoantigens{\\textquotedblright}) are an increasingly popular form of personalized cancer immunotherapy. Vaxrank is a computational tool for selecting neoantigen vaccine peptides from tumor mutations, tumor RNA data, and patient HLA type. Vaxrank is freely available at www.github.com/hammerlab/vaxrank under the Apache 2.0 open source license and can also be installed from the Python Package Index.},\n        URL = {https://www.biorxiv.org/content/early/2017/05/27/142919},\n        eprint = {https://www.biorxiv.org/content/early/2017/05/27/142919.full.pdf},\n        journal = {bioRxiv}\n    }\n\n\n# Development\n\nTo install Vaxrank for local development, you may do the below:\n\n```\ngit clone git@github.com:openvax/vaxrank.git\nconda create -q -n vaxrank-dev-env python=3.5.2 numpy scipy nose pandas pylint\nsource activate vaxrank-dev-env\npip install -r requirements.txt\npip install .\npyensembl install --release 87 --species human\npyensembl install --release 87 --species mouse\n```\n\nYou should run the linter and the test suite as you work on Vaxrank (and these will be run automatically by our continuous integration server up on a PR being made).\n\n```\n./lint.sh\nnosetests test\n```\n\nThe first run of the tests may take a while (8 minutes on a 2016 Macbook Pro) to create the FM index of the proteome, but subsequent tests should take only a few seconds.",
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