pVACview: an interactive visualization tool for efficient neoantigen prioritization and selection
Huiming Xia, My Hoang, Evelyn Schmidt, Susanna Kiwala, Joshua, McMichael, Zachary L. Skidmore, Bryan Fisk, Jonathan J. Song, Jasreet Hundal,, Thomas Mooney, Jason R. Walker, S. Peter Goedegebuure, Christopher A. Miller,, William E. Gillanders, Obi L. Griffith, Malachi Griffith

TL;DR
pVACview is an interactive visualization tool that simplifies the complex process of neoantigen prioritization, enabling researchers to efficiently select personalized neoantigen candidates for cancer immunotherapy.
Contribution
It introduces the first user-friendly, interactive platform for neoantigen prioritization, integrating multiple prediction algorithms and visualization features to improve accuracy and efficiency.
Findings
Enhanced visualization of neoantigen features
Streamlined candidate selection process
Improved accuracy in neoantigen prioritization
Abstract
Neoantigen targeting therapies including personalized vaccines have shown promise in the treatment of cancers. Accurate identification/prioritization of neoantigens is highly relevant to designing clinical trials, predicting treatment response, and understanding mechanisms of resistance. With the advent of massively parallel sequencing technologies, it is now possible to predict neoantigens based on patient-specific variant information. However, numerous factors must be considered when prioritizing neoantigens for use in personalized therapies. Complexities such as alternative transcript annotations, various binding, presentation and immunogenicity prediction algorithms, and variable peptide lengths/registers all potentially impact the neoantigen selection process. While computational tools generate numerous algorithmic predictions for neoantigen characterization, results from these…
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