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.
- A browser-based user interface that assists users in launching, managing, reviewing, and visualizing the results of pVACtools processes.
New in version 1.3.3¶
This version is a hotfix release. It fixes the following issues:
- We were previously using our own locking logic while multithreading which
contained a bug that could result in runs getting stuck waiting on a lock.
This release switches to using the locking implementation provided by the
- In an attempt to reduce cluttered output generated by Tenserflow we were
previously repressing any message generated during the import of MHCflurry and
MHCnuggets. As a side effect, this would also supress any legitimate error messages
generated during these imports which would result in the
pvacvectorcommands exiting without output. This release updates to code so that actual errors still get output.
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, 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.