NeoFuse: predicting fusion neoantigens from RNA sequencing data

作者：Georgios Fotakis, Dietmar Rieder, Marlene Haider, Zlatko Trajanoski, Francesca Finotello · 发表于：Bioinformatics · 年份：2019 · DOI：10.1093/bioinformatics/btz879 · 被引用次数：53 · 研究领域：vaccines and immunoinformatics approaches、Immunotherapy and Immune Responses、T-cell and B-cell Immunology

SUMMARY: Gene fusions can generate immunogenic neoantigens that mediate anticancer immune responses. However, their computational prediction from RNA sequencing (RNA-seq) data requires deep bioinformatics expertise to assembly a computational workflow covering the prediction of: fusion transcripts, their translated proteins and peptides, Human Leukocyte Antigen (HLA) types, and peptide-HLA binding affinity. Here, we present NeoFuse, a computational pipeline for the prediction of fusion neoantigens from tumor RNA-seq data. NeoFuse can be applied to cancer patients' RNA-seq data to identify fusion neoantigens that might expand the repertoire of suitable targets for immunotherapy. AVAILABILITY AND IMPLEMENTATION: NeoFuse source code and documentation are available under GPLv3 license at https://icbi.i-med.ac.at/NeoFuse/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.