GeNeo: A Bioinformatics Toolbox for Genomics-Guided Neoepitope Prediction

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Abstract

High-Throughput DNA and RNA sequencing are revolutionizing precision oncology, enabling personalized therapies such as cancer vaccines designed to target tumor-specific neoepitopes generated by somatic mutations expressed in cancer cells. Identification of these neoepitopes from next-generation sequencing data of clinical samples remains challenging and requires the use of complex bioinformatics pipelines. In this paper, we present GeNeo, a bioinformatics toolbox for genomics-guided neoepitope prediction. GeNeo includes a comprehensive set of tools for somatic variant calling and filtering, variant validation, and neoepitope prediction and filtering. For ease of use, GeNeo tools can be accessed via web-based interfaces deployed on a Galaxy portal publicly accessible at https://neo.engr.uconn.edu/. A virtual machine image for running GeNeo locally is also available to academic users upon request.

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Al Seesi, S., Al-Okaily, A., Shcheglova, T. V., Sherafat, E., Alqahtani, F. H., Hagymasi, A. T., … Mǎndoiu, I. I. (2023). GeNeo: A Bioinformatics Toolbox for Genomics-Guided Neoepitope Prediction. Journal of Computational Biology, 30(4), 538–551. https://doi.org/10.1089/cmb.2022.0491

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