pVACtools: a computational toolkit to identify and visualize cancer neoantigens
Jasreet HundaliD, Susanna KiwalaiD, Joshua F. McMichaeliD, Christopher A. MilleriD, Alexander Wollam, Huiming XiaiD, Connor J. LiuiD, Sidi ZhaoiD, Yang-Yang FengiD, Aaron GraubertiD, Amber Z. WollamiD, Jonas Neichin, Megan Neveau, Jason WalkeriD, William E. GillandersiD, Elaine R. MardisiD, Obi L. GriffithiD, Malachi GriffithiD
Abstract
The elucidation of neoantigens is a critical step in predicting response to checkpoint blockade therapy and design of personalized cancer vaccines. We have developed an in silico sequence analysis method - pVACtools, to facilitate comprehensive neoantigen characterization. This modular workflow consists of tools for neoantigen prediction from somatic alterations (pVACseq and pVACfuse), prioritization and selection using a graphical web-based interface (pVACviz), and determining the optimal order of neoantigen candidates in a DNA vector-based vaccine(pVACvector).
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