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... read moresequences at breakpoint junctions. FusionSeq
detected known and novel fusions in a specially sequenced calibration data set,
including eight cancers with and without known rearrangements.
Keywords: difference between the observed and analytically calculated
expected SPER, expressed sequence tag, Illumina Genome Analyzer II, nucleotide,
paired-end, ratio of empirically computed SPERs, mRNA sequencing, reads per kilobase
of exon model per million mapped reads, supportive PE reads, University of California
Sboner, Andrea, 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, and Mark B.
Gerstein. "FusionSeq: a modular framework for finding gene fusions by analyzing
paired-end RNA-sequencing data." Genome Biology 11, no. 12 (10, 2010):