Work

INTEGRATE-neo: a pipeline for personalized gene fusion neoantigen discovery

Year: 2016

Type: article

Abstract: Motivation: While high-throughput sequencing (HTS) has been used successfully to discover tumor-specific mutant peptides (neoantigens) from somatic missense mutations, the field currently lacks a method for identifying which gene fusions may generate neoantigens. Results: We demonstrate the application of our gene fusion neoantigen discovery pipeline, called INTEGRATE-Neo, by identifying gene fusions in prostate cancers that may produce neoantigens. Availability and Implementation: INTEGRATE-Neo is implemented in C ++ and Python. Full source code and installation instructions are freely available from https://github.com/ChrisMaherLab/INTEGRATE-Neo . Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Source: Bioinformatics

DOI: 10.1093/bioinformatics/btw674

Authors: Jin Zhang, Elaine R Mardis, Christopher A Maher

Institutions: Washington University in St. Louis, Alvin J. Siteman Cancer Center

Language: English

Topic: Cancer Immunotherapy and Biomarkers

Subfield: Oncology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,989