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Abstract Description The identification and prioritization of cancer-specific neoepitopes from next-generation sequencing data for personalized immunotherapies such as cancer vaccines remains challenging and requires the use of complex bioinformatics approaches. Here, we present GeNeo2, an updated version with enhanced features of the GeNeo toolbox for predicting neoepitopes from matched tumor/normal exome sequencing data coupled with tumor RNA-Seq data (Al Seesi et al., 2023). Unlike GeNeo, which identifies neoepitopes generated by single nucleotide variants, GeNeo2 also predicts neoepitopes generated by somatic indels. A distinguishing feature in GeNeo2 is that it integrates tools for analyzing mass spectrometry immunepeptidomic data, which can reveal neoantigens derived from both canonical and noncanonical sources. Finally, GeNeo2 integrates novel machine-learning approaches to improve the accuracy of somatic variant calling and peptide identification from mass spectrometry data. GeNeo2 tools can be accessed via web-based interfaces deployed on a Galaxy portal accessible at https://neo.engr.uconn.edu/. A virtual machine image for running GeNeo2 locally is also available to academic users upon request. Topic Categories Computational and Systems Immunology (COMP)