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

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 applicat...

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Detalles Bibliográficos
Autores principales: Zhang, Jin, Mardis, Elaine R, Maher, Christopher A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Applications Notes
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5408800/
https://www.ncbi.nlm.nih.gov/pubmed/27797777
http://dx.doi.org/10.1093/bioinformatics/btw674
Descripción
Sumario: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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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