biostudies-literature00500Ma CGordon and Betty Moore FoundationPennsylvania Department of HealthNIGMS NIH HHSNIH HHSNational Science Foundation52https://www.ebi.ac.uk/biostudies/studies/S-EPMC5896115biostudies-literatureUnknown19(1)Transcripts are frequently modified by structural variations, which lead to fused transcripts of either multiple genes, known as a fusion gene, or a gene and a previously non-transcribed sequence. Detecting these modifications, called transcriptomic structural variations (TSVs), especially in cancer tumor sequencing, is an important and challenging computational problem. We introduce SQUID, a novel algorithm to predict both fusion-gene and non-fusion-gene TSVs accurately from RNA-seq alignments. SQUID unifies both concordant and discordant read alignments into one model and doubles the precision on simulation data compared to other approaches. Using SQUID, we identify novel non-fusion-gene TSVs on TCGA samples.Genome biologySQUID: transcriptomic structural variation detection from RNA-seq.PMC5896115R01 GM122935CCF-1256087GBMF4554CCF-1319998R01HG0071044100070287R21HG006913Ma CKingsford CShao M50falseSQUID: transcriptomic structural variation detection from RNA-seq.Transcripts are frequently modified by structural variations, which lead to fused transcripts of either multiple genes, known as a fusion gene, or a gene and a previously non-transcribed sequence. Detecting these modifications, called transcriptomic structural variations (TSVs), especially in cancer tumor sequencing, is an important and challenging computational problem. We introduce SQUID, a novel algorithm to predict both fusion-gene and non-fusion-gene TSVs accurately from RNA-seq alignments. SQUID unifies both concordant and discordant read alignments into one model and doubles the precision on simulation data compared to other approaches. Using SQUID, we identify novel non-fusion-gene TSVs on TCGA samples.2018-01-01T00:00:00Z2018 Apr2021-02-20T06:08:08Z2019-03-26T23:31:24ZS-EPMC58961152965002610.1186/s13059-018-1421-5