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<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>OpenVax</title>
<meta name="description" content="Open-source software for personalized cancer vaccines: Vaxrank, Isovar, Varcode, mhctools, MHCflurry, PyEnsembl.">
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</head>
<body>
<header class="topbar">
<div class="wrap">
<a class="brand" href="#top" aria-label="OpenVax home">
<svg viewBox="0 0 32 16" aria-hidden="true"><path d="M4 8h24" stroke="currentColor" stroke-width="2"/><circle cx="4" cy="8" r="3.5" fill="currentColor"/><circle cx="28" cy="8" r="3.5" fill="currentColor"/><circle cx="16" cy="8" r="5" fill="#c2255c"/></svg>
OpenVax
</a>
<nav aria-label="Sections">
<a href="#software">Software</a>
<a class="optional" href="#tutorial">Tutorial</a>
<a href="#trials">Trials</a>
<a href="#papers">Papers</a>
<a class="optional" href="#people">People</a>
<a class="gh" href="https://github.com/openvax" aria-label="OpenVax on GitHub"><svg viewBox="0 0 16 16" aria-hidden="true"><path d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0016 8c0-4.42-3.58-8-8-8z"/></svg></a>
</nav>
</div>
</header>
<main id="top">
<section class="hero wrap">
<div>
<h1>Software for personalized cancer vaccines</h1>
<p class="lede">OpenVax is a collection of open-source Python libraries and command-line tools for finding the mutations in a patient's tumor that T cells could recognize, and choosing which of them to put in a vaccine. Vaccines designed with these tools have been given to patients in clinical trials.</p>
<div class="install"><span class="prompt">$</span>pip install vaxrank</div>
<p class="note">The libraries are maintained at the <a href="https://pirl.unc.edu/">Personalized Immunotherapy Research Lab (PIRL)</a> at UNC Chapel Hill.</p>
</div>
<figure class="peptide" aria-label="Example vaccine peptide spanning the KRAS G12D mutation">
<div class="seq" aria-hidden="true">
<span class="n" style="grid-column:1">1</span><span class="n" style="grid-column:12">12</span><span class="n" style="grid-column:25">25</span>
<span class="r">M</span><span class="r">T</span><span class="r">E</span><span class="r">Y</span><span class="r">K</span><span class="r">L</span><span class="r">V</span><span class="r lig">V</span><span class="r lig">V</span><span class="r lig">G</span><span class="r lig">A</span><span class="r lig mut">D</span><span class="r lig">G</span><span class="r lig">V</span><span class="r lig">G</span><span class="r lig">K</span><span class="r">S</span><span class="r">A</span><span class="r">L</span><span class="r">T</span><span class="r">I</span><span class="r">Q</span><span class="r">L</span><span class="r">I</span><span class="r">Q</span>
<span class="tag">HLA-A*11:01 epitope</span>
</div>
<figcaption>A 25-residue vaccine peptide from KRAS spanning the G12D mutation (<b>magenta</b>). The underlined 9-mer, VVGADGVGK, is presented to T cells by HLA-A*11:01. Vaxrank picks peptides like this for each patient.</figcaption>
</figure>
</section>
<section class="block" id="software">
<div class="wrap">
<h2>Software</h2>
<p class="prose">Each step below is a separate package that can be used on its own.</p>
<div class="pipe">
<ol class="steps">
<li class="io"><span class="node">Patient data</span><span class="cap">variants, tumor RNA, HLA type</span></li>
<li><a class="node" href="#varcode">Varcode</a><span class="cap">protein changes</span></li>
<li><a class="node" href="#isovar">Isovar</a><span class="cap">mutant sequence from RNA</span></li>
<li><span class="node"><a href="#mhctools">mhctools</a><a href="#mhcflurry">MHCflurry</a></span><span class="cap">MHC presentation</span></li>
<li><a class="node" href="#vaxrank">Vaxrank</a><span class="cap">ranked vaccine peptides</span></li>
<li class="io"><span class="node">Vaccine</span><span class="cap">peptides or mRNA</span></li>
</ol>
</div>
<h3>Vaccine design</h3>
<div class="pkgs">
<article class="pkg" id="vaxrank">
<h4><a href="https://github.com/openvax/vaxrank">Vaxrank</a></h4>
<p>Chooses the mutant peptides for a personalized vaccine. Scores each candidate by predicted MHC presentation and by how much tumor RNA supports it, then outputs ranked peptides, mRNA constructs or reports for clinical review. Also accepts results from <a href="https://github.com/pirl-unc/lens">LENS</a> or <a href="https://github.com/griffithlab/pVACtools">pVACseq</a>.</p>
<code class="pip">pip install vaxrank</code>
<p class="links"><a href="https://openvax.github.io/vaxrank/">Documentation</a><a href="https://github.com/openvax/vaxrank">Source</a><a href="https://doi.org/10.1101/142919">Preprint</a></p>
</article>
<article class="pkg" id="isovar">
<h4><a href="https://github.com/openvax/isovar">Isovar</a></h4>
<p>Determines the mutant protein sequence the tumor expresses by assembling the RNA reads that cover each mutation, so nearby germline or somatic variants and splicing changes come from the reads rather than the reference genome.</p>
<code class="pip">pip install isovar</code>
<p class="links"><a href="https://github.com/openvax/isovar">Source</a></p>
</article>
<article class="pkg" id="varcode">
<h4><a href="https://github.com/openvax/varcode">Varcode</a></h4>
<p>Predicts what DNA variants do to genes, transcripts and proteins: substitutions, frameshifts, stop codons, splice and structural variants. Reads VCF and MAF files and returns the mutant protein where it can be determined.</p>
<code class="pip">pip install varcode</code>
<p class="links"><a href="https://openvax.github.io/varcode/">Documentation</a><a href="https://github.com/openvax/varcode">Source</a></p>
</article>
<article class="pkg" id="mhctools">
<h4><a href="https://github.com/openvax/mhctools">mhctools</a></h4>
<p>One Python interface to many MHC binding, presentation, processing and immunogenicity predictors, including NetMHCpan, MHCflurry, MixMHCpred and BigMHC. Switching or comparing predictors is a one-line change.</p>
<code class="pip">pip install mhctools</code>
<p class="links"><a href="https://openvax.github.io/mhctools/">Documentation</a><a href="https://github.com/openvax/mhctools">Source</a></p>
</article>
<article class="pkg" id="mhcflurry">
<h4><a href="https://github.com/openvax/mhcflurry">MHCflurry</a></h4>
<p>Predicts which peptides MHC class I molecules present, with pretrained models for binding affinity, antigen processing and presentation. Runs from the command line or Python and can be retrained on new data.</p>
<code class="pip">pip install mhcflurry</code>
<p class="links"><a href="https://openvax.github.io/mhcflurry/">Documentation</a><a href="https://github.com/openvax/mhcflurry">Source</a><a href="https://doi.org/10.1016/j.cels.2018.05.014">Paper (2018)</a><a href="https://doi.org/10.1016/j.cels.2020.06.010">MHCflurry 2.0 paper (2020)</a><a href="https://doi.org/10.1007/978-1-0716-0327-7_8">Book chapter (2020)</a></p>
</article>
<article class="pkg" id="topiary">
<h4><a href="https://github.com/openvax/topiary">Topiary</a></h4>
<p>Predicts, filters and ranks MHC-presented peptides from any source, including tumor mutations and viral proteins. Built on mhctools.</p>
<code class="pip">pip install topiary</code>
<p class="links"><a href="https://openvax.github.io/topiary/">Documentation</a><a href="https://github.com/openvax/topiary">Source</a></p>
</article>
<article class="pkg wide" id="pipeline">
<h4><a href="https://github.com/openvax/neoantigen-vaccine-pipeline">neoantigen-vaccine-pipeline</a></h4>
<p>The end-to-end workflow used in the Mount Sinai trials: aligns tumor and normal sequencing reads, calls somatic variants and runs Vaxrank. Packaged with Snakemake and Docker.</p>
<p class="links"><a href="https://github.com/openvax/neoantigen-vaccine-pipeline">Source</a><a href="https://doi.org/10.3389/fimmu.2017.01807">Paper (2018)</a><a href="https://doi.org/10.1007/978-1-0716-0327-7_10">Book chapter (2020)</a></p>
</article>
</div>
<h3>Genome reference</h3>
<div class="pkgs small">
<article class="pkg" id="pyensembl">
<h4><a href="https://github.com/openvax/pyensembl">PyEnsembl</a></h4>
<p>Local access to Ensembl genome annotation (genes, transcripts, exons and their sequences) for human and many other species.</p>
<code class="pip">pip install pyensembl</code>
</article>
<article class="pkg" id="gtfparse">
<h4><a href="https://github.com/openvax/gtfparse">gtfparse</a></h4>
<p>Fast parsing of GTF gene annotation files into data frames.</p>
<code class="pip">pip install gtfparse</code>
</article>
<article class="pkg" id="datacache">
<h4><a href="https://github.com/openvax/datacache">datacache</a></h4>
<p>Downloads, checks and caches the reference data that PyEnsembl and other packages use.</p>
<code class="pip">pip install datacache</code>
</article>
</div>
<h3>Smaller utilities</h3>
<p class="utils">
<a href="https://github.com/openvax/varlens">varlens</a> (command-line tools for variants and reads),
<a href="https://github.com/openvax/pepdata">pepdata</a> (IEDB data and amino acid properties),
<a href="https://github.com/pirl-unc/mhcgnomes">mhcgnomes</a> (parses MHC allele names),
<a href="https://github.com/openvax/sercol">sercol</a> and
<a href="https://github.com/openvax/serializable">serializable</a> (shared base classes).
</p>
</div>
</section>
<section class="block" id="tutorial">
<div class="wrap">
<h2>Tutorial</h2>
<div class="prose">
<p>The <a href="https://github.com/openvax/neoantigen-vaccine-pipeline">neoantigen-vaccine-pipeline</a> runs everything from raw sequencing reads to ranked vaccine peptides inside one Docker image. You need tumor and normal whole-exome sequencing and tumor RNA-seq as gzipped FASTQ files, the patient's MHC class I alleles, and a machine with at least 16 cores. No cluster is required.</p>
</div>
<ol class="howto">
<li>
<h4>Install</h4>
<pre><code>docker pull openvax/neoantigen-vaccine-pipeline:latest
docker run openvax/neoantigen-vaccine-pipeline:latest -h</code></pre>
</li>
<li>
<h4>Get a reference genome</h4>
<p>Processed b37decoy, GRCh38 and mm10 references are available in <a href="https://console.cloud.google.com/storage/browser/reference-genomes">Google Cloud</a>. For GRCh38:</p>
<pre><code>gsutil -m cp gs://reference-genomes/grch38.tar.gz /path/to/reference-genome/
cd /path/to/reference-genome && tar -zxvf grch38.tar.gz
chmod -R a+w grch38</code></pre>
</li>
<li>
<h4>Set up three directories</h4>
<p>They are mounted as Docker volumes: <code>/inputs</code> holds the FASTQ files and a config file, <code>/outputs</code> receives results, and <code>/reference-genome</code> holds the reference data. All three must be world-writable, because the pipeline runs as an unprivileged user.</p>
</li>
<li>
<h4>Write a config</h4>
<p>A YAML file in <code>/inputs</code> lists the sample's files, up to six MHC class I alleles (each must be supported by NetMHCpan) and tool settings. Paths are relative to the mounted directories. Start from the <a href="https://github.com/openvax/neoantigen-vaccine-pipeline/blob/master/test/idh1_config_grch38.yaml">example config</a>. Paired-end reads use <code>r1</code> and <code>r2</code> entries with <code>type: paired-end</code>; data from several lanes goes in separate fragments with distinct <code>fragment_id</code> values.</p>
</li>
<li>
<h4>Run the test case</h4>
<p>The test data is a small set of reads covering the IDH1 R132H mutation, with the tumor DNA reads reused as RNA.</p>
<pre><code>cd /path/to/inputs
URL=https://github.com/openvax/neoantigen-vaccine-pipeline/raw/master
wget $URL/test/idh1_config_grch38.yaml
wget $URL/datagen/idh1_r132h_normal.fastq.gz
wget $URL/datagen/idh1_r132h_tumor.fastq.gz
docker run -it \
-v /path/to/inputs:/inputs \
-v /path/to/outputs:/outputs \
-v /path/to/reference-genome:/reference-genome \
openvax/neoantigen-vaccine-pipeline:latest \
--configfile=/inputs/idh1_config_grch38.yaml</code></pre>
<p>The first run takes a few extra minutes while files are downloaded and cached. Results include ranked variants and vaccine peptides as text and PDF reports; the test should report a single IDH1 R132H variant.</p>
</li>
</ol>
<div class="prose">
<p>To call somatic variants only, leave the tumor RNA and HLA alleles out of the config and the pipeline writes Mutect and Strelka VCFs instead (<a href="https://github.com/openvax/neoantigen-vaccine-pipeline/blob/master/test/idh1_config_dna_only.yaml">example config</a>). Intermediate outputs and other options are described in the <a href="https://github.com/openvax/neoantigen-vaccine-pipeline#readme">pipeline README</a>.</p>
<p class="muted">Adapted from a 2020 post by Julia Kodysh.</p>
</div>
</div>
</section>
<section class="block" id="trials">
<div class="wrap">
<h2>Clinical trials</h2>
<p class="prose">Four trials at Mount Sinai have tested <b>PGV-001</b>, a personalized vaccine of up to 10 synthetic long peptides per patient, chosen by the OpenVax pipeline and given with the adjuvant <a href="https://www.oncovir.com/science">poly-ICLC</a>.</p>
<h3>Mount Sinai</h3>
<div class="trials">
<article class="trial">
<div>
<h4>Solid tumors and myeloma</h4>
<p class="meta"><a class="nct" href="https://clinicaltrials.gov/study/NCT02721043">NCT02721043</a> <span class="status done">Completed</span><br>PI: <a href="https://profiles.mountsinai.org/nina-bhardwaj">Nina Bhardwaj</a></p>
</div>
<div class="trial-body">
<p>Adjuvant PGV-001 after standard treatment of head and neck, lung, breast or bladder cancer, or multiple myeloma. The vaccine was safe and induced T-cell responses in every vaccinated patient.</p>
<ul class="pubs">
<li><span class="kind">Paper</span><a href="https://doi.org/10.1158/2159-8290.CD-24-0934">Saxena et al., <i>Cancer Discovery</i> 2025</a></li>
<li><span class="kind">Abstracts</span><a href="https://doi.org/10.1158/1538-7445.AM2023-CT270">AACR 2023</a>, <a href="https://doi.org/10.1158/1538-7445.AM2021-LB048">AACR 2021</a>, <a href="https://doi.org/10.1136/jitc-2020-SITC2020.0289">SITC 2020</a>, <a href="https://doi.org/10.1158/1538-7445.AM2020-CT173">AACR 2020</a>, <a href="https://doi.org/10.1200/JCO.2019.37.15_suppl.e14307">ASCO 2019</a>, <a href="https://doi.org/10.1186/s40425-018-0422-y">SITC 2018</a>, <a href="https://doi.org/10.1158/2326-6074.CRICIMTEATIAACR18-A005">CRI 2018</a>, <a href="https://higherlogicdownload.s3.amazonaws.com/SITCANCER/3bcb5ebf-803a-42fe-83b6-0773bc4eb962/UploadedImages/Annual%20Meeting%202017/SITC_2017_Abstract_Book.pdf">SITC 2017</a>, <a href="https://doi.org/10.1200/JCO.2017.35.15_suppl.TPS3114">ASCO 2017</a></li>
</ul>
</div>
</article>
<article class="trial">
<div>
<h4>Glioblastoma</h4>
<p class="meta"><a class="nct" href="https://clinicaltrials.gov/study/NCT03223103">NCT03223103</a> <span class="status active">Active, not recruiting</span><br>PI: <a href="https://doctors.montefioreeinstein.org/providers/1427029461/adilia-hormigo">Adilia Hormigo</a></p>
</div>
<div class="trial-body">
<p>PGV-001 with <a href="https://www.novocure.com/ttfields">tumor treating fields</a>, added to standard treatment (surgery, radiation and temozolomide) for newly diagnosed glioblastoma. Enrollment is complete; follow-up continues.</p>
<ul class="pubs">
<li><span class="kind">Abstracts</span><a href="https://doi.org/10.1093/neuonc/noae165.0343">SNO 2024</a>, <a href="https://doi.org/10.1136/jitc-2021-SITC2021.334">SITC 2021</a>, <a href="https://doi.org/10.1093/neuonc/noaa215.151">SNO 2020 (interim)</a>, <a href="https://doi.org/10.1093/neuonc/noaa215.301">SNO 2020 (neoantigen selection)</a>, <a href="https://doi.org/10.1158/1538-7445.AM2019-CT062">AACR 2019</a>, <a href="https://doi.org/10.1093/neuonc/noy148.026">SNO 2018</a></li>
</ul>
</div>
</article>
<article class="trial">
<div>
<h4>Urothelial cancer</h4>
<p class="meta"><a class="nct" href="https://clinicaltrials.gov/study/NCT03359239">NCT03359239</a> <span class="status done">Completed</span><br>PI: <a href="https://profiles.mountsinai.org/matthew-galsky">Matthew Galsky</a></p>
</div>
<div class="trial-body">
<p>PGV-001 with the checkpoint inhibitor atezolizumab for advanced urothelial cancer. The combination was safe and induced T-cell responses in all patients.</p>
<ul class="pubs">
<li><span class="kind">Paper</span><a href="https://doi.org/10.1038/s43018-025-00966-7">Saxena et al., <i>Nature Cancer</i> 2025</a></li>
<li><span class="kind">Abstracts</span><a href="https://doi.org/10.1158/1538-7445.AM2024-LB113">AACR 2024</a>, <a href="https://doi.org/10.1200/JCO.2024.42.4_suppl.597">ASCO GU 2024</a></li>
</ul>
</div>
</article>
<article class="trial">
<div>
<h4>Prostate cancer</h4>
<p class="meta"><a class="nct" href="https://clinicaltrials.gov/study/NCT05010200">NCT05010200</a> <span class="status active">Active, not recruiting</span><br>PI: <a href="https://profiles.mountsinai.org/ashutosh-tewari">Ash Tewari</a></p>
</div>
<div class="trial-body">
<p>Adjuvant PGV-001, alone or with the immune stimulant CDX-301 (FLT3 ligand). Enrollment is complete (27 patients); results are pending.</p>
</div>
</article>
</div>
<h3>Elsewhere</h3>
<div class="trials">
<article class="trial">
<div>
<h4>Liver cancer</h4>
<p class="meta"><a class="nct" href="https://clinicaltrials.gov/study/NCT04912765">NCT04912765</a> <span class="status open">Recruiting</span><br>PI: <a href="https://www.nccs.com.sg/doctor/medical-oncology/han-shuting">Shuting Han</a>, <a href="https://www.nccs.com.sg/">National Cancer Centre Singapore</a></p>
</div>
<div class="trial-body">
<p>A dendritic cell vaccine loaded with each patient's neoantigens, given with nivolumab after surgery for hepatocellular carcinoma or colorectal cancer metastatic to the liver. Neoantigens are selected with the OpenVax pipeline.</p>
<ul class="pubs">
<li><span class="kind">Paper</span><a href="https://doi.org/10.1126/sciadv.adz1156">Teo et al., <i>Science Advances</i> 2026</a></li>
</ul>
</div>
</article>
</div>
</div>
</section>
<section class="block" id="papers">
<div class="wrap">
<h2>Papers</h2>
<h3 style="margin-top:0">Software and methods</h3>
<ul class="refs">
<li><span class="title"><a href="https://doi.org/10.1101/054775">Predicting peptide-MHC binding affinities with imputed training data</a></span><br><span class="src">Rubinsteyn, O'Donnell, Damaraju & Hammerbacher. <i>bioRxiv</i>, 2016.</span></li>
<li><span class="title"><a href="https://doi.org/10.1101/142919">Vaxrank: a computational tool for designing personalized cancer vaccines</a></span><br><span class="src">Rubinsteyn, Hodes, Kodysh & Hammerbacher. <i>bioRxiv</i>, 2017.</span></li>
<li><span class="title"><a href="https://doi.org/10.3389/fimmu.2017.01807">Computational pipeline for the PGV-001 neoantigen vaccine trial</a></span><br><span class="src">Rubinsteyn et al. <i>Frontiers in Immunology</i>, 2018. <a href="https://doi.org/10.1101/174516">Preprint</a></span></li>
<li><span class="title"><a href="https://doi.org/10.1016/j.cels.2018.05.014">MHCflurry: open-source class I MHC binding affinity prediction</a></span><br><span class="src">O'Donnell et al. <i>Cell Systems</i>, 2018. <a href="https://doi.org/10.1101/174243">Preprint</a></span></li>
<li><span class="title"><a href="https://doi.org/10.1007/978-1-0716-0327-7_10">OpenVax: an open-source computational pipeline for cancer neoantigen prediction</a></span><br><span class="src">Kodysh & Rubinsteyn. <i>Methods in Molecular Biology</i>, 2020.</span></li>
<li><span class="title"><a href="https://doi.org/10.1007/978-1-0716-0327-7_8">High-throughput MHC I ligand prediction using MHCflurry</a></span><br><span class="src">O'Donnell & Rubinsteyn. <i>Methods in Molecular Biology</i>, 2020.</span></li>
<li><span class="title"><a href="https://doi.org/10.1016/j.cels.2020.06.010">MHCflurry 2.0: improved pan-allele prediction of MHC class I-presented peptides by incorporating antigen processing</a></span><br><span class="src">O'Donnell, Rubinsteyn & Laserson. <i>Cell Systems</i>, 2020. <a href="https://doi.org/10.1101/2020.03.28.013714">Preprint</a></span></li>
</ul>
<h3>Clinical results</h3>
<ul class="refs">
<li><span class="title"><a href="https://doi.org/10.1158/2159-8290.CD-24-0934">PGV001, a multi-peptide personalized neoantigen vaccine platform: phase I study in patients with solid and hematologic malignancies in the adjuvant setting</a></span><br><span class="src">Saxena, Marron, Kodysh et al. <i>Cancer Discovery</i>, 2025.</span></li>
<li><span class="title"><a href="https://doi.org/10.1038/s43018-025-00966-7">Atezolizumab plus personalized neoantigen vaccination in urothelial cancer: a phase 1 trial</a></span><br><span class="src">Saxena, Anker, Kodysh et al. <i>Nature Cancer</i>, 2025.</span></li>
<li><span class="title"><a href="https://doi.org/10.1126/sciadv.adz1156">Healthy donor T cell receptors expand functional neoantigen recognition beyond patient vaccination</a></span><br><span class="src">Teo et al. <i>Science Advances</i>, 2026.</span></li>
</ul>
</div>
</section>
<section class="block" id="history">
<div class="wrap">
<h2>History</h2>
<div class="prose">
<p>OpenVax started in <a href="https://web.archive.org/web/20170604013230/http://www.hammerlab.org/">Hammer Lab</a>, Jeff Hammerbacher's lab at <a href="https://icahn.mssm.edu/">Mount Sinai</a>, where the first of these libraries were written. The group then built the OpenVax pipeline to select vaccine targets for <a href="https://profiles.mountsinai.org/nina-bhardwaj">Nina Bhardwaj</a>'s <a href="https://labs.icahn.mssm.edu/saxenalab/">Vaccine and Cell Therapy Lab</a>, and for several years was its own small research group within Mount Sinai.</p>
<p>The group has since wound down. Mount Sinai still uses the software to design vaccines, and development continues at <a href="https://pirl.unc.edu/">PIRL</a> at UNC Chapel Hill.</p>
</div>
<h2 id="people" style="margin-top:3.5rem">People</h2>
<div class="people">
<div class="person"><h4><a href="https://unclineberger.org/directory/alex-rubinsteyn/">Alex Rubinsteyn</a></h4><p>Wrote most of the OpenVax libraries and still maintains them. Faculty in <a href="https://www.med.unc.edu/genetics/">Genetics</a> and <a href="https://www.med.unc.edu/compmed/">Computational Medicine</a> at UNC Chapel Hill and a member of the <a href="https://unclineberger.org/">UNC Lineberger Comprehensive Cancer Center</a>. Co-leads the <a href="https://pirl.unc.edu/">Personalized Immunotherapy Research Lab</a>.</p></div>
<div class="person"><h4><a href="https://github.com/julia326">Julia Kodysh</a></h4><p>Built the <a href="https://github.com/openvax/neoantigen-vaccine-pipeline">vaccine pipeline</a> used in the Mount Sinai trials and still works on it. Works at <a href="https://www.freenome.com/">Freenome</a>.</p></div>
<div class="person"><h4><a href="https://timodonnell.github.io/">Tim O'Donnell</a></h4><p>Created MHCflurry and still contributes to it. Now at <a href="https://openathena.ai/">Open Athena</a>, a nonprofit that helps academic labs build scientific AI models.</p></div>
<div class="person"><h4><a href="https://profiles.mountsinai.org/mesude-bicak">Mesude Bicak</a></h4><p>Directs the Bioinformatics Program at the <a href="https://icahn.mssm.edu/research/tisch">Mount Sinai Tisch Cancer Center</a> and uses OpenVax to design vaccines there.</p></div>
</div>
<h3>Earlier members</h3>
<div class="people">
<div class="person"><h4><a href="https://en.wikipedia.org/wiki/Jeff_Hammerbacher">Jeff Hammerbacher</a></h4><p>Founded Hammer Lab, where OpenVax began. Now founder and CEO of <a href="https://openathena.ai/">Open Athena</a>.</p></div>
<div class="person"><h4><a href="https://sequencesoftware.io/">Tavi Nathanson</a></h4><p>Worked on early versions of PyEnsembl, Varcode, mhctools and Topiary. Now runs Sequence Software and <a href="https://purplecomputer.org/">Purple Computer</a>.</p></div>
<div class="person"><h4><a href="https://isaachodes.io/">Isaac Hodes</a></h4><p>Worked on the first version of the PGV trial pipeline. Now at <a href="https://openathena.ai/">Open Athena</a>.</p></div>
</div>
<p class="muted" style="margin-top:1.5rem">Other contributors include Arun Ahuja, Arman Aksoy, Seb Mondet and Walid Ahmad.</p>
</div>
</section>
</main>
<footer>
<div class="wrap cols">
<div>
<h5>OpenVax</h5>
<p>Open-source software for personalized cancer vaccines, Apache 2.0 licensed. For questions or bug reports, open an issue on the relevant <a href="https://github.com/openvax">GitHub repository</a>.</p>
</div>
<div>
<h5>Code</h5>
<ul>
<li><a href="https://github.com/openvax">OpenVax on GitHub</a></li>
<li><a href="https://github.com/pirl-unc">PIRL on GitHub</a></li>
<li><a href="https://github.com/hammerlab">Hammer Lab on GitHub</a></li>
</ul>
</div>
<div>
<h5>Labs</h5>
<ul>
<li><a href="https://pirl.unc.edu/">PIRL, UNC Chapel Hill</a></li>
<li><a href="https://labs.icahn.mssm.edu/saxenalab/">Vaccine and Cell Therapy Lab, Mount Sinai</a></li>
<li><a href="https://web.archive.org/web/20170604013230/http://www.hammerlab.org/">Hammer Lab (archived site)</a></li>
</ul>
</div>
</div>
</footer>
</body>
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