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. 2022 Jan 27;38(4):1131-1132.
doi: 10.1093/bioinformatics/btab759.

nextNEOpi: a comprehensive pipeline for computational neoantigen prediction

Dietmar Rieder  1 , Georgios Fotakis  1 , Markus Ausserhofer  1 , Geyeregger René  2 , Wolfgang Paster  2 , Zlatko Trajanoski  1 , Francesca Finotello  1   3   4
Affiliations

nextNEOpi: a comprehensive pipeline for computational neoantigen prediction

Dietmar Rieder et al. Bioinformatics. .

Abstract

Summary: Somatic mutations and gene fusions can produce immunogenic neoantigens mediating anticancer immune responses. However, their computational prediction from sequencing data requires complex computational workflows to identify tumor-specific aberrations, derive the resulting peptides, infer patients' Human Leukocyte Antigen types and predict neoepitopes binding to them, together with a set of features underlying their immunogenicity. Here, we present nextNEOpi (nextflow NEOantigen prediction pipeline) a comprehensive and fully automated bioinformatic pipeline to predict tumor neoantigens from raw DNA and RNA sequencing data. In addition, nextNEOpi quantifies neoepitope- and patient-specific features associated with tumor immunogenicity and response to immunotherapy.

Availability and implementation: nextNEOpi source code and documentation are available at https://github.com/icbi-lab/nextNEOpi.

Contact: dietmar.rieder@i-med.ac.at or francesca.finotello@uibk.ac.at.

Supplementary information: Supplementary data are available at Bioinformatics online.

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Figures

Fig. 1.
Fig. 1.
Schematization of nextNEOpi pipeline with main input data (white boxes), data flow (lines), intermediate (grey boxes) and final (grey boxes with borders) outputs.
See this image and copyright information in PMC

References

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