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124943 |
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|a Regev, Aviv
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|a Massachusetts Institute of Technology. Department of Biology
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|a Koch Institute for Integrative Cancer Research at MIT
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|a Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods
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|b Springer Science and Business Media LLC,
|c 2020-04-30T17:31:22Z.
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|z Get fulltext
|u https://hdl.handle.net/1721.1/124943
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|a Background: Accurate fusion transcript detection is essential for comprehensive characterization of cancer transcriptomes. Over the last decade, multiple bioinformatic tools have been developed to predict fusions from RNA-seq, based on either read mapping or de novo fusion transcript assembly. Results: We benchmark 23 different methods including applications we develop, STAR-Fusion and TrinityFusion, leveraging both simulated and real RNA-seq. Overall, STAR-Fusion, Arriba, and STAR-SEQR are the most accurate and fastest for fusion detection on cancer transcriptomes. Conclusion: The lower accuracy of de novo assembly-based methods notwithstanding, they are useful for reconstructing fusion isoforms and tumor viruses, both of which are important in cancer research.
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|a National Cancer Institute (U.S.) (Grant U24CA180922)
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|a National Cancer Institute (U.S.) (Grant R50CA211461)
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|a National Cancer Institute (U.S.) (Grant R21CA209940)
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|a National Cancer Institute (U.S.) (Grant U01CA214846)
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|a Article
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|t 10.1186/s13059-019-1842-9
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|t Genome biology
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