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 VCF file.

pVACbind

A cancer immunotherapy pipeline for identifying and prioritizing neoantigens from a FASTA file.

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.

pVACview

An application based on R Shiny that assists users in reviewing, exploring and prioritizing neoantigens from the results of pVACtools processes for personalized cancer vaccine design.

Contents

• pVACseq
  • Features
  • Input File Preparation
  • Getting Started
  • Usage
  • Output Files
  • Filtering Commands
  • Additional Commands
  • Optional Downstream Analysis Tools
  • Common Errors
  • Frequently Asked Questions
• pVACbind
  • Features
  • Prerequisites
  • Getting Started
  • Usage
  • Output Files
  • Filtering Commands
  • Additional Commands
  • Optional Downstream Analysis Tools
• pVACfuse
  • Features
  • Prerequisites
  • Getting Started
  • Usage
  • Output Files
  • Filtering Commands
  • Additional Commands
  • Optional Downstream Analysis Tools
• pVACvector
  • Prerequisites
  • Getting Started
  • Usage
  • Additional Commands
  • Output Files
• pVACview
  • Prerequisites
  • Getting Started
  • Features
  • Commonly Asked Questions

• Installation
• Tools Used By pVACtools
• Frequently Asked Questions
• Release Notes
• License
• Citations
• How To Contribute
• Contact
• Mailing List

New in Release 3.1.1

This is a bugfix release. It fixes the following problem(s):

• The --exclude-NAs flag was not being passed along correctly to the main pipeline and didn’t have any effect on downstream filtering.

• The aggregate report creation step had some inefficiencies which caused its runtime to be much longer than necessary.

• An unneeded import statement for the PyVCF package had the potential to cause errors while running the pVACseq pipeline.

New in Version 3.1

This release adds the following new features:

• When running the pipelines with the --netmhc-stab flag enabled, the NetMHCstab allele column now also reports the distance of the NetMHCstabpan allele when that allele is not identical to the HLA Allele of the original result.

• When running the pipelines with a set of individual class II alleles, the pipeline now also auto-generates valid combinations of these alleles so that users no longer need to explicitly provide these combinations.

This release also fixes the following problem(s):

• Some class I alleles are not supported by NetMHCstabpan and will lead to an error when attempt to make predictions with them. This release will skip such alleles for the stability prediction step.

• For very large result sets the filtering steps would stall or be killed because the steps would run out of memory. This release fixes this issue.

• This release adds better handling of timeout errors while running

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, 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.

License

This project is licensed under BSD 3-Clause Clear License.