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

A cancer immunotherapy pipeline for identifying and prioritizing neoantigens from a list of tumor mutations.
A tool for detecting neoantigens resulting from gene fusions.
A tool designed to aid specifically in the construction of DNA-based cancer vaccines.
pVACtools immunotherapy workflow

New in version 1.0.2

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

  • The epitope length used for generating the peptide fasta when running with multiple epitope lengths was incorrect. This would potentially result in including fasta sequences that were shorter than the largest epitope length which would cause an error during calls to IEDB.
  • pVACseq would fail with a nondescript error message if the input VCF was not annotated with VEP before running. A more descriptive error message has been added.
  • IEDB changed the format of class II IEDB alleles which would cause an error when running with those alleles. pVACtools will now handle transposing the affected alleles into the new format.
  • The standalone binding filters had a few bugs that would result in syntax errors during runtime.
  • The indexes created for each fusion entry with pVACfuse had the potential to not be unique which would result in parsing errors downstream.
  • pVACseq had the potential to use the incorrect VEP allele for positions with multiple alternate alleles which would result in the incorrect CSQ entry getting used for some of those alternate alleles.
  • pVACseq would throw an error if the chosen peptide sequence length exceeds the wildtype protein sequence length of a transcript.

Coming soon

A browser-based user interface that assists users in launching, managing, reviewing, and visualizing the results of pVACtools processes.
The pVACapi will provide a HTTP REST interface to the pVACtools suite.


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.


This project is licensed under NPOSL-3.0.