GFusion: an Effective Algorithm to Identify Fusion Genes from Cancer RNA-Seq Data

Abstract Fusion gene derived from genomic rearrangement plays a key role in cancer initiation. The discovery of novel gene fusions may be of significant importance in cancer diagnosis and treatment. Meanwhile, next generation sequencing technology provide a sensitive and efficient way to identify ge...

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Autores principales:	Jian Zhao, Qi Chen, Jing Wu, Ping Han, Xiaofeng Song
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a6ce249b08754520b1b3408c7789b6e9
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spelling	oai:doaj.org-article:a6ce249b08754520b1b3408c7789b6e92021-12-02T15:06:25ZGFusion: an Effective Algorithm to Identify Fusion Genes from Cancer RNA-Seq Data10.1038/s41598-017-07070-62045-2322https://doaj.org/article/a6ce249b08754520b1b3408c7789b6e92017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-07070-6https://doaj.org/toc/2045-2322Abstract Fusion gene derived from genomic rearrangement plays a key role in cancer initiation. The discovery of novel gene fusions may be of significant importance in cancer diagnosis and treatment. Meanwhile, next generation sequencing technology provide a sensitive and efficient way to identify gene fusions in genomic levels. However, there are still many challenges and limitations remaining in the existing methods which only rely on unmapped reads or discordant alignment fragments. In this work we have developed GFusion, a novel method using RNA-Seq data, to identify the fusion genes. This pipeline performs multiple alignments and strict filtering algorithm to improve sensitivity and reduce the false positive rate. GFusion successfully detected 34 from 43 previously reported fusions in four cancer datasets. We also demonstrated the effectiveness of GFusion using 24 million 76 bp paired-end reads simulation data which contains 42 artificial fusion genes, among which GFusion successfully discovered 37 fusion genes. Compared with existing methods, GFusion presented higher sensitivity and lower false positive rate. The GFusion pipeline can be accessed freely for non-commercial purposes at: https://github.com/xiaofengsong/GFusion .Jian ZhaoQi ChenJing WuPing HanXiaofeng SongNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jian Zhao Qi Chen Jing Wu Ping Han Xiaofeng Song GFusion: an Effective Algorithm to Identify Fusion Genes from Cancer RNA-Seq Data
description	Abstract Fusion gene derived from genomic rearrangement plays a key role in cancer initiation. The discovery of novel gene fusions may be of significant importance in cancer diagnosis and treatment. Meanwhile, next generation sequencing technology provide a sensitive and efficient way to identify gene fusions in genomic levels. However, there are still many challenges and limitations remaining in the existing methods which only rely on unmapped reads or discordant alignment fragments. In this work we have developed GFusion, a novel method using RNA-Seq data, to identify the fusion genes. This pipeline performs multiple alignments and strict filtering algorithm to improve sensitivity and reduce the false positive rate. GFusion successfully detected 34 from 43 previously reported fusions in four cancer datasets. We also demonstrated the effectiveness of GFusion using 24 million 76 bp paired-end reads simulation data which contains 42 artificial fusion genes, among which GFusion successfully discovered 37 fusion genes. Compared with existing methods, GFusion presented higher sensitivity and lower false positive rate. The GFusion pipeline can be accessed freely for non-commercial purposes at: https://github.com/xiaofengsong/GFusion .
format	article
author	Jian Zhao Qi Chen Jing Wu Ping Han Xiaofeng Song
author_facet	Jian Zhao Qi Chen Jing Wu Ping Han Xiaofeng Song
author_sort	Jian Zhao
title	GFusion: an Effective Algorithm to Identify Fusion Genes from Cancer RNA-Seq Data
title_short	GFusion: an Effective Algorithm to Identify Fusion Genes from Cancer RNA-Seq Data
title_full	GFusion: an Effective Algorithm to Identify Fusion Genes from Cancer RNA-Seq Data
title_fullStr	GFusion: an Effective Algorithm to Identify Fusion Genes from Cancer RNA-Seq Data
title_full_unstemmed	GFusion: an Effective Algorithm to Identify Fusion Genes from Cancer RNA-Seq Data
title_sort	gfusion: an effective algorithm to identify fusion genes from cancer rna-seq data
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/a6ce249b08754520b1b3408c7789b6e9
work_keys_str_mv	AT jianzhao gfusionaneffectivealgorithmtoidentifyfusiongenesfromcancerrnaseqdata AT qichen gfusionaneffectivealgorithmtoidentifyfusiongenesfromcancerrnaseqdata AT jingwu gfusionaneffectivealgorithmtoidentifyfusiongenesfromcancerrnaseqdata AT pinghan gfusionaneffectivealgorithmtoidentifyfusiongenesfromcancerrnaseqdata AT xiaofengsong gfusionaneffectivealgorithmtoidentifyfusiongenesfromcancerrnaseqdata
_version_	1718388470517334016

GFusion: an Effective Algorithm to Identify Fusion Genes from Cancer RNA-Seq Data

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