TopHat-Fusion: an algorithm for discovery of novel fusion transcripts

By Daehwan Kim, Steven L. Salzberg. Genome Biology, 2011

785 citations recorded by the source corpus.

TopHat-Fusion is an algorithm designed to discover transcripts representing fusion gene products, which result from the breakage and re-joining of two different chromosomes, or from rearrangements within a chromosome. TopHat-Fusion is an enhanced version of TopHat, an efficient program that aligns RNA-seq reads without relying on existing annotation. Because it is independent of gene annotation, TopHat-Fusion can discover fusion products deriving from known genes, unknown genes and unannotated splice variants of known genes. Using RNA-seq data from breast and prostate cancer cell lines, we detected both previously reported and novel fusions with solid supporting evidence. TopHat-Fusion is available at http://tophat-fusion.sourceforge.net/.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d1bdb863b74275ab3b5533148dd6dd219dfc84e6

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Citations in the corpus

Structural variant calling: the long and the short of it (2019, methodology): “One of the earliest fusion detection methods was TopHat-Fusion [56], which used a specialized version of TopHat [91].”
Best practices for variant calling in clinical sequencing (2020, methodology): “Gene fusions (RNA-seq) fusionCatcher [56], fusionMap [57], mapSplice [58], SOAPfuse [59], STAR-Fusion [60], TopHat-Fusion [61]”
PacBio Sequencing and Its Applications (2015, background): “In the human MCF7 breast cancer cell line, IDP-fusion detected fusion genes at a much higher precision than non-hybrid tools that use TGS alone and SGS alone (69% true positive for IDP-fusion vs. 31% for TRUP [61], 23% for TopHat-Fusion [62], and 21% for Iso-Seq [54]), with similar sensitivity.”
Lessons from the cancer genome. (2013, neutral): “of foreign DNA (such as from viruses) (Beroukhim et al., 2007; Chen et al., 2009; Cibulskis et al., 2013; Dees et al., 2012; Kim and Salzberg, 2011; Kostic et al., 2011; Trapnell et al., 2009).”
ANOVA-Like Differential Expression (ALDEx) Analysis for Mixed Population RNA-Seq (2013, background): “RNASeq can be used to discover unanticipated transcripts, to identify and characterize novel splice and promoter variants[4-6], and has a much larger potential dynamic range than microarrays[7].”
Integrative clinical genomics of advanced prostate cancer (2015, methodology): “Paired-end transcriptome sequencing reads were aligned to the human reference genome (GRCh37/hg19) using a RNA-seq spliced read mapper Tophat2 (Kim and Salzberg, 2011) (Tophat 2.0.4), with ‘‘–fusion-search’’ option turned on to detect potential gene fusion transcripts.”
Repetitive DNA and next-generation sequencing: computational challenges and solutions (2011, methodology): “Other aligners, including TopHat‑ Fusion 63 , FusionSeq 64 , ShortFuse 65 and SplitSeek 61 have been designed to scan RNA‑seq data and to detect fusion genes that are caused by chromosome breakage and rejoining: a common event in cancer cells.”
Gaining comprehensive biological insight into the transcriptome by performing a broad-spectrum RNA-seq analysis (2017, methodology): “Several tools have been proposed to identify fusion from transcriptomic data such as JAFFA 57 , STAR-Fusion 14 , TopHat-Fusion 58 , FusionCatcher 59 , and SOAPfuse 60 .”