FusionSeq: a modular framework for finding gene fusions by analyzing paired-end RNA-sequencing data.
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.
Program in Computational Biology and Bioinformatics, Yale University, 300 George Street, New Haven, CT 06511, USA. andrea.sboner@yale.edu
Andrea
A
Sboner
Lukas
L
Habegger
Dorothee
D
Pflueger
Stephane
S
Terry
David Z
DZ
Chen
Joel S
JS
Rozowsky
Ashutosh K
AK
Tewari
Naoki
N
Kitabayashi
Benjamin J
BJ
Moss
Mark S
MS
Chee
Francesca
F
Demichelis
Mark A
MA
Rubin
Mark B
MB
Gerstein
D001483:Base Sequence; D045744:Cell Line, Tumor; D019295:Computational Biology / Q000379:methods*; D020224:Expressed Sequence Tags; D020869:Gene Expression Profiling; D050939:Gene Fusion; D015321:Gene Rearrangement; D006801:Humans; D008297:Male; D008969:Molecular Sequence Data; D009369:Neoplasms / Q000235:genetics*; D011471:Prostatic Neoplasms / Q000235:genetics; D012334:RNA, Neoplasm / Q000235:genetics; D020133:Reverse Transcriptase Polymerase Chain Reaction; D017423:Sequence Analysis, RNA / Q000379:methods*
D012334:RNA, Neoplasm