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Mutant peptide ranking for personalized cancer vaccines

Project description

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Selection of mutated protein fragments for therapeutic personalized cancer vaccines.


vaxrank \
    --vcf test/data/b16.f10/b16.vcf \
    --bam test/data/b16.f10/b16.combined.bam \
    --vaccine-peptide-length 25 \
    --mhc-predictor netmhc \
    --mhc-alleles H2-Kb,H2-Db \
    --padding-around-mutation 5 \
    --output-ascii-report vaccine-peptides.txt \
    --output-pdf-report vaccine-peptides.pdf \
    --output-html-report vaccine-peptides.html


Vaxrank can be installed using pip:

pip install vaxrank

Note: to generate PDF reports, you first need to install wkhtmltopdf, which you can do (on OS X) like so:

brew install Caskroom/cask/wkhtmltopdf

Vaxrank uses 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.

The latest release for GRCh38 is Ensembl 93:

pyensembl install --release 93 --species human

The last release for GRCh37 is Ensembl 75:

pyensembl install --release 75 --species human

If your variants were called from alignments against hg19 then you can still use GRCh37 but should ignore mitochondrial variants.

Paper & Citation

There is a Vaxrank paper on biorxiv called Vaxrank: A Computational Tool For Designing Personalized Cancer Vaccines which can be cited as:

@article {Rubinsteyn142919,
    author = {Rubinsteyn, Alex and Hodes, Isaac and Kodysh, Julia and Hammerbacher, Jeffrey},
    title = {Vaxrank: A Computational Tool For Designing Personalized Cancer Vaccines},
    year = {2017},
    doi = {10.1101/142919},
    publisher = {Cold Spring Harbor Laboratory},
    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 under the Apache 2.0 open source license and can also be installed from the Python Package Index.},
    URL = {},
    eprint = {},
    journal = {bioRxiv}


To install Vaxrank for local development, you may do the below:

git clone
conda create -q -n vaxrank-dev-env python=3.5.2 numpy scipy nose pandas pylint
source activate vaxrank-dev-env
pip install -r requirements.txt
pip install .
pyensembl install --release 87 --species human
pyensembl install --release 87 --species mouse

You 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).

nosetests test

The 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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