pVACtools is a cancer immunotherapy tools suite consisting of the following tools:

A cancer immunotherapy pipeline for identifying and prioritizing neoantigens from a VCF file.
A cancer immunotherapy pipeline for identifying and prioritizing neoantigens from a FASTA file.
A tool for detecting neoantigens resulting from gene fusions.
A tool designed to aid specifically in the construction of DNA-based cancer vaccines.
A browser-based user interface that assists users in launching, managing, reviewing, and visualizing the results of pVACtools processes.
pVACtools immunotherapy workflow

New in release 1.5.7

This is a hotfix release. It fixes the following issues:

  • The pvacbind run command would previously allow fasta input files with duplicated headers. However, it would silently skip subsequent entries with duplicated headers even if the fasta sequence was novel. With this release pVACbind will now error out if a duplicate fasta header is encounterd.

New in version 1.5

This version adds the following features:

  • This version introduces a new tool, pVACbind, which can be used to run our immunotherapy pipeline with a peptides FASTA file as input. This new tool is similar to pVACseq but certain options and filters are removed:

    • All input sequences are interpreted in isolation so corresponding wildtype sequence and score information are not assigned. As a consequence, the filter threshold option on fold change is removed.
    • Because the input format doesn’t allow for association of readcount, expression or transcript support level data, pVACbind doesn’t run the coverage filter or transcript support level filter.
    • No condensed report is generated.

    Please see the pVACbind documentation for more information.

  • pVACfuse now support annotated fusion files from AGFusion as input. The pVACfuse documentation has been updated with instructions on how to run AGFusion in the Prerequisites section.

  • The top score filter has been updated to take into account alternative known transcripts that might result in non-indentical peptide sequences/epitopes. The top score filter now picks the best epitope for every available transcript of a variant. If the resulting list of epitopes for one variant is not identical, the filter will output all eptiopes. If the resulting list of epitopes for one variant are identical, the filter only outputs the epitope for the transcript with the highest transcript expression value. If no expression data is available, or if multiple transcripts remain, the filter outputs the epitope for the transcripts with the lowest transcript Ensembl ID.

  • This version adds a few new options to the pvacseq generate_protein_fasta command:

    • The --mutant-only option can be used to only output mutant peptide sequences instead of mutant and wildtype sequences.
    • This command now has an option to provide a pVACseq all_eptiopes or filtered TSV file as an input (--input-tsv). This will limit the output fasta to only sequences that originated from the variants in that file.
  • This release adds a pvacfuse generate_protein_fasta command that works similarly to the pvacseq generate_protein_fasta command but works with Integrate-NEO or AGFusion input files.

  • We removed the sorting of the all_epitopes result file in order to reduce memory usage. Only the filtered files will be sorted. This version also updates the sorting algorithm of the filtered files as follows:

    • If the --top-score-metric is set to median the results are first sorted by the Median MT Score. If multiple epitopes have the same Median MT Score they are then sorted by the Corresponding Fold Change. The last sorting criteria is the Best MT Score.
    • If the --top-score-metric is set to lowest the results are first sorted by the Best MT Score. If multiple epitopes have the same Best MT Score they are then sorted by the Corresponding Fold Change. The last sorting criteria is the Median MT Score.
  • pVACseq, pVACfuse, and pVACbind now calculate manufacturability metrics for the predicted epitopes. Manufacturability metrics are also calculated for all protein sequences when running the pvacseq generate_protein_fasta and pvacfuse generate_protein_fasta commands. They are saved in the .manufacturability.tsv along to the result fasta.

  • The pVACseq score that gets calculated for epitopes in the condensed report is now converted into a rank. This will hopefully remove any confusion about whether the previous score could be treated as an absolute measure of immunogencity, which it was not intended for. Converting this score to a rank ensures that it gets treated in isolation for only the epitopes in the condensed file.

  • The condensed report now also outputs the mutation position as well as the full set of lowest and median wildtype and mutant scores.

  • This version adds a clear cache function to pVACapi that can be called by running pvacapi clear_cache. Sometimes pVACapi can get into a state where the cache file contains conflicting data compared to the actual process outputs which results in errors. Clearing the cache using the pvacapi clear_cache function can be used in that situation to resolve these errors.

Past release notes can be found on our Release Notes page.

To stay up-to-date on the latest pVACtools releases please join our Mailing List.


Jasreet Hundal , Susanna Kiwala , Joshua McMichael, Chris Miller, Huiming Xia, Alex Wollam, Conner Liu, Sidi Zhao, Yang-Yang Feng, Aaron Graubert, Amber Wollam, Jonas Neichin, Megan Neveau, Jason Walker, William Gillanders, Elaine Mardis, Obi Griffith, Malachi Griffith. pVACtools: A Computational Toolkit to Identify and Visualize Cancer Neoantigens. Cancer Immunology Research. 2020 Mar;8(3):409-420. doi: 10.1158/2326-6066.CIR-19-0401. PMID: 31907209.

Jasreet Hundal, Susanna Kiwala, Yang-Yang Feng, Connor J. Liu, Ramaswamy Govindan, William C. Chapman, Ravindra Uppaluri, S. Joshua Swamidass, Obi L. Griffith, Elaine R. Mardis, and Malachi Griffith. Accounting for proximal variants improves neoantigen prediction. Nature Genetics. 2018, DOI: 10.1038/s41588-018-0283-9. PMID: 30510237.

Jasreet Hundal, Beatriz M. Carreno, Allegra A. Petti, Gerald P. Linette, Obi L. Griffith, Elaine R. Mardis, and Malachi Griffith. pVACseq: A genome-guided in silico approach to identifying tumor neoantigens. Genome Medicine. 2016, 8:11, DOI: 10.1186/s13073-016-0264-5. PMID: 26825632.

Source code

The pVACtools source code is available in GitHub.


This project is licensed under NPOSL-3.0.