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SQUID: Transcriptomic Structural Variation Detection from RNA-seq

Cong Ma, Mingfu Shao, Carl Kingsford
doi: https://doi.org/10.1101/162776
Cong Ma
1Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA
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Mingfu Shao
1Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA
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Carl Kingsford
1Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA
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  • For correspondence: carlk@cs.cmu.edu
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Abstract

Transcripts are frequently modified by structural variations, which leads to a fused transcript of either multiple genes (known as a fusion gene) or a gene and a previously non-transcribing sequence. Detecting these modifications (called transcriptomic structural variations, or TSVs), especially in cancer tumor sequencing, is an important and challenging computational problem. We introduce SQUID, a novel algorithm to accurately predict both fusion-gene and non-fusion-gene TSVs from RNA-seq alignments. SQUID unifies both concordant and discordant read alignments into one model, and doubles the accuracy on simulation data compared to other approaches. With SQUID, we identified novel non-fusion-gene TSVs on TCGA samples.

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Posted September 06, 2017.
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SQUID: Transcriptomic Structural Variation Detection from RNA-seq
Cong Ma, Mingfu Shao, Carl Kingsford
bioRxiv 162776; doi: https://doi.org/10.1101/162776
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SQUID: Transcriptomic Structural Variation Detection from RNA-seq
Cong Ma, Mingfu Shao, Carl Kingsford
bioRxiv 162776; doi: https://doi.org/10.1101/162776

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