FusionSeq: a modular framework for finding gene fusions by analyzing paired-end RNA-sequencing data

Andrea Sboner; Lukas Habegger; Dorothee Pflueger; Stephane Terry; David Z Chen; Joel S Rozowsky; Ashutosh K Tewari; Naoki Kitabayashi; Benjamin J Moss; Mark S Chee; Francesca Demichelis; Mark A Rubin; Mark B Gerstein

doi:10.1186/gb-2010-11-10-r104

FusionSeq: a modular framework for finding gene fusions by analyzing paired-end RNA-sequencing data

Genome Biol. 2010;11(10):R104. doi: 10.1186/gb-2010-11-10-r104. Epub 2010 Oct 21.

Authors

Andrea Sboner¹, Lukas Habegger, Dorothee Pflueger, Stephane Terry, David Z Chen, Joel S Rozowsky, Ashutosh K Tewari, Naoki Kitabayashi, Benjamin J Moss, Mark S Chee, Francesca Demichelis, Mark A Rubin, Mark B Gerstein

Affiliation

¹ Program in Computational Biology and Bioinformatics, Yale University, 300 George Street, New Haven, CT 06511, USA. andrea.sboner@yale.edu

Abstract

We have developed FusionSeq to identify fusion transcripts from paired-end RNA-sequencing. FusionSeq includes filters to remove spurious candidate fusions with artifacts, such as misalignment or random pairing of transcript fragments, and it ranks candidates according to several statistics. It also has a module to identify exact sequences at breakpoint junctions. FusionSeq detected known and novel fusions in a specially sequenced calibration data set, including eight cancers with and without known rearrangements.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Base Sequence
Cell Line, Tumor
Computational Biology / methods*
Expressed Sequence Tags
Gene Expression Profiling
Gene Fusion*
Gene Rearrangement
Humans
Male
Molecular Sequence Data
Neoplasms / genetics*
Prostatic Neoplasms / genetics
RNA, Neoplasm / genetics
Reverse Transcriptase Polymerase Chain Reaction
Sequence Analysis, RNA / methods*

Substances

RNA, Neoplasm

Abstract

Publication types

MeSH terms

Substances

Grants and funding