pVACtools is a cancer immunotherapy 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 vector-based cancer vaccines.
A browser-based user interface that assists users in launching, managing, reviewing, and visualizing the results of pVACtools processes. pVACviz relies on the pVACapi and a client application. The source code for the client application can be found here.
The pVACapi provides a HTTP REST interface to the pVACtools suite.
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. pVAC-Seq: 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.
pVACtools requires Python 3.5. Before running any installation steps check the Python version installed on your system:
If you don’t have Python 3.5 installed, we recommend using Conda to emulate a Python 3.5. environment. We’ve encountered problems with users that already have Python 2.x installed when they also try to install Python 3.5. The defaults will not be set correctly in that case. If you already have Python 2.x installed we strongly recommmend using Conda instead of installing Python 3.5 locally.
Once you have set up your Python 3.5 environment correctly you can use pip to install pVACtools. Make sure you have pip installed. pip is generally included in python distributions, but may need to be upgraded before use. See the instructions for installing or upgrading pip.
After you have pip installed/upgraded, type the following command on your Terminal:
pip install pvactools
You can check that pVACtools has been installed under the default environment by listing all installed packages:
You can also check the installed version:
pip will fetch and install pVACtools and its dependencies for you. After installing, each tool of the pVACtools suite is available with its own command line tree directly from the Terminal.
If you have an old version of pVACtools installed you might want to consider upgrading to the latest version:
pip install pvactools --upgrade
The pVACtools documentation can be found on ReadTheDocs.
pVACtools is available as a Docker Image at DockerHub griffithlab/pvactools.