Abstract
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.
Highlights
Large-scale transcriptome sequence changes are known to be associated with cancer [1, 2]
Adjacencies that are present in this rearranged genome but not present in the original reference are proposed as predicted Transcriptomic structural variant (TSV)
SQUID exhibits higher precision at similar sensitivities compared with whole-genome sequencing (WGS)-based structural variation (SV) detection methods and pipelines of de novo transcriptome assembly and transcript-to-genome alignment
Summary
Large-scale transcriptome sequence changes are known to be associated with cancer [1, 2] Those changes are usually a consequence of genomic structural variation (SV). By pulling different genomic regions together or separating one region into pieces, structural variants can potentially cause severe alteration to transcribed or translated products. BCR-ABL1 is a well-known fusion oncogene for chronic myeloid leukemia [3], and the TMPRSS2-ERG fusion product leads to over-expression of ERG and helps triggers prostate cancer [4]. These fusion events are used as biomarkers for early diagnosis or treatment targets [5]. There have been fewer studies on these TSVs between transcribed and non-transcribed regions, but their ability to alter downstream RNA and protein structure is likely to lead to similar results as fusion-gene TSVs
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