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. 2020 Apr 1;36(7):2260-2261.
doi: 10.1093/bioinformatics/btz879.

NeoFuse: predicting fusion neoantigens from RNA sequencing data

Georgios Fotakis  1 , Dietmar Rieder  1 , Marlene Haider  1 , Zlatko Trajanoski  1 , Francesca Finotello  1
Affiliations

NeoFuse: predicting fusion neoantigens from RNA sequencing data

Georgios Fotakis et al. Bioinformatics. .

Abstract

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.

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Figures

Fig. 1.
Fig. 1.
Schematization of the NeoFuse pipeline: computational modules represented as dark-grey boxes (with tool names in square brackets) and output files as light-grey boxes
See this image and copyright information in PMC

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