pVACtools

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

pVACseq

A cancer immunotherapy pipeline for identifying and prioritizing neoantigens from a list of tumor mutations.

pVACfuse

A tool for detecting neoantigens resulting from gene fusions.

pVACvector

A tool designed to aid specifically in the construction of DNA-based cancer vaccines.

pVACviz

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 1.4.2

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

  • This releases fixes a concurrency issue with pVACapi/pVACviz that would occurr when users would try to visualize multiple files at the same time

New in version 1.4

This version adds the following features:

  • pVACvector now tests spacers iteratively. During the first iteration, the first spacer in the list of --spacers gets tested. In the next iteration, the next spacer in the list gets added to the pool of spacers to tests, and so on. If at any point a valid ordering is found, pVACvector will finish its run and output the result. This might result in slightly less optimal (but still valid) ordering but improves runtime significantly.

  • If, after testing all spacers, no valid ordering if found, pVACvector will clip the beginning and/or ends of problematic peptides by one amino acid. The ordering finding process is then repeated on the updated list of peptides. This process may be repeated up to a maximum set by the --max-clip-length parameter.

  • This version adds a standalone command to create the pVACvector visualizations that can be run by calling pvacvector visualize using a pVACvector result file as the input.

  • We removed the --aditional-input-file-list option to pVACseq. Readcount and expression information are now taken directly from the VCF annotations. Instructions on how to add these annotations to your input VCF can be found on the Input File Preparation page.

  • We added support for variants to pVACseq that are only annotated as protein_altering_variant without a more specific consequence of missense_variant, inframe_insertion, inframe_deletion, or frameshift_variant.

  • We resolved some syntax differences that prevented pVACtools from being run under python 3.6 or python 3.7. pVACtools should now be compatible with all python3 versions.

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.

Citations

Jasreet Hundal, Susanna Kiwala, Joshua McMichael, Christopher A Miller, Alexander T Wollam, Huiming Xia, Connor J Liu, Sidi Zhao, Yang-Yang Feng, Aaron P Graubert, Amber Z Wollam, Jonas Neichin, Megan Neveau, Jason Walker, William E Gillanders, Elaine R Mardis, Obi L Griffith, Malachi Griffith. pVACtools: a computational toolkit to select and visualize cancer neoantigens. bioRxiv 501817; doi: https://doi.org/10.1101/501817

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.

License

This project is licensed under NPOSL-3.0.