Publication | Open Access
pVACtools: a computational toolkit to identify and visualize cancer neoantigens
21
Citations
48
References
2018
Year
Unknown Venue
EngineeringImmunologyPathologyTumor BiologyAbstract IdentificationMolecular GraphicMolecular CharacterizationTumor HeterogeneityComprehensive Neoantigen CharacterizationBiological Network VisualizationComputational ToolkitMolecular DiagnosticsRadiation OncologyCancer ResearchTranslational BioinformaticsMedicineOmicsNeoantigen PredictionPathway AnalysisBioinformaticsFunctional GenomicsTumor MicroenvironmentComputational BiologyCancer GenomicsBiomedical Data AnalysisSystems BiologyOncology
Abstract Identification 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 toolkit - pVACtools, to facilitate comprehensive neoantigen characterization. pVACtools supports a modular workflow consisting of tools for neoantigen prediction from somatic alterations (pVACseq and pVACfuse), prioritization and selection using a graphical web-based interface (pVACviz) and design of DNA vector-based vaccines (pVACvector) and synthetic long peptide vaccines. pVACtools is available at pvactools.org .
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