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
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author Zhang, Jin
Mardis, Elaine R
Maher, Christopher A
author_facet Zhang, Jin
Mardis, Elaine R
Maher, Christopher A
author_sort Zhang, Jin
collection PubMed
description 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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spelling pubmed-54088002017-05-03 INTEGRATE-neo: a pipeline for personalized gene fusion neoantigen discovery Zhang, Jin Mardis, Elaine R Maher, Christopher A Bioinformatics Applications Notes 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. Oxford University Press 2017-02-15 2016-11-24 /pmc/articles/PMC5408800/ /pubmed/27797777 http://dx.doi.org/10.1093/bioinformatics/btw674 Text en © The Author 2016. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Applications Notes
Zhang, Jin
Mardis, Elaine R
Maher, Christopher A
INTEGRATE-neo: a pipeline for personalized gene fusion neoantigen discovery
title INTEGRATE-neo: a pipeline for personalized gene fusion neoantigen discovery
title_full INTEGRATE-neo: a pipeline for personalized gene fusion neoantigen discovery
title_fullStr INTEGRATE-neo: a pipeline for personalized gene fusion neoantigen discovery
title_full_unstemmed INTEGRATE-neo: a pipeline for personalized gene fusion neoantigen discovery
title_short INTEGRATE-neo: a pipeline for personalized gene fusion neoantigen discovery
title_sort integrate-neo: a pipeline for personalized gene fusion neoantigen discovery
topic Applications Notes
url 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
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AT mardiselainer integrateneoapipelineforpersonalizedgenefusionneoantigendiscovery
AT maherchristophera integrateneoapipelineforpersonalizedgenefusionneoantigendiscovery

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