Accuracy Assessment of Fusion Transcript Detection via Read-Mapping and De Novo Fusion Transcript Assembly-based Methods
Oct 21, 2019·,
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0 min read
B. j. haas
A. dobin
Dr. Bo Li
N. stransky
N. pochet
A. regev
Abstract
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.
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.
Type
Publication
Genome Biology
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Principal Scientist II
Dr. Bo Li is a Principal Scientist at Genentech, Inc. His research focuses on large-scale single-cell genomics data analysis.
Before joining in Genentech, he was an Assistant Professor of Medicine at Harvard Medical School and the director of Bioinformatics and Computational Biology at Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital.
He received his Ph.D. in computer science from UW-Madison and completed two postdoctoral trainings with Dr. Lior Pachter at UC Berkeley and Dr. Aviv Regev at Broad Institute.
He is best known for developing RSEM, an impactful RNA-seq transcript quantification software. RSEM is cited 22,602 times (Google Scholar) and adopted by several big consortia such as TCGA, ENCODE, GTEx and TOPMed.
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