期刊
GENOME BIOLOGY
卷 20, 期 1, 页码 -出版社
BMC
DOI: 10.1186/s13059-019-1842-9
关键词
Fusion; RNA-seq; Cancer; Benchmarking; STAR-Fusion; TrinityFusion
资金
- Howard Hughes Medical Institute
- Klarman Cell Observatory
- National Cancer Institute [U24CA180922, R50CA211461, R21CA209940, U01CA214846]
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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