Dataset Information

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

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.

SUBMITTER: Sboner A

PROVIDER: S-EPMC3218660 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Sboner Andrea A Habegger Lukas L Pflueger Dorothee D Terry Stephane S Chen David Z DZ Rozowsky Joel S JS Tewari Ashutosh K AK Kitabayashi Naoki N Moss Benjamin J BJ Chee Mark S MS Demichelis Francesca F Rubin Mark A MA Gerstein Mark B MB

Genome biology 20101021 10

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 kno ...[more]

PMID: 20964841

Similar Datasets

Project description:BackgroundA cancer genome is derived from the germline genome through a series of somatic mutations. Somatic structural variants - including duplications, deletions, inversions, translocations, and other rearrangements - result in a cancer genome that is a scrambling of intervals, or "blocks" of the germline genome sequence. We present an efficient algorithm for reconstructing the block organization of a cancer genome from paired-end DNA sequencing data.ResultsBy aligning paired reads from a cancer genome - and a matched germline genome, if available - to the human reference genome, we derive: (i) a partition of the reference genome into intervals; (ii) adjacencies between these intervals in the cancer genome; (iii) an estimated copy number for each interval. We formulate the Copy Number and Adjacency Genome Reconstruction Problem of determining the cancer genome as a sequence of the derived intervals that is consistent with the measured adjacencies and copy numbers. We design an efficient algorithm, called Paired-end Reconstruction of Genome Organization (PREGO), to solve this problem by reducing it to an optimization problem on an interval-adjacency graph constructed from the data. The solution to the optimization problem results in an Eulerian graph, containing an alternating Eulerian tour that corresponds to a cancer genome that is consistent with the sequencing data. We apply our algorithm to five ovarian cancer genomes that were sequenced as part of The Cancer Genome Atlas. We identify numerous rearrangements, or structural variants, in these genomes, analyze reciprocal vs. non-reciprocal rearrangements, and identify rearrangements consistent with known mechanisms of duplication such as tandem duplications and breakage/fusion/bridge (B/F/B) cycles.ConclusionsWe demonstrate that PREGO efficiently identifies complex and biologically relevant rearrangements in cancer genome sequencing data. An implementation of the PREGO algorithm is available at http://compbio.cs.brown.edu/software/.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets