Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods

Haas, B. J., Dobin, A., Li, B., Stransky, N., Pochet, N., Regev, A. (October 2019) Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods. Genome Biol, 20 (1). p. 213. ISSN 1474-7596

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URL: https://www.ncbi.nlm.nih.gov/pubmed/31730029 https...
DOI: 10.1186/s13059-019-1842-9

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.

Item Type: Paper
Additional Information: Genome biology
Uncontrolled Keywords: *Benchmarking *Cancer *Fusion *RNA-seq *STAR-Fusion *TrinityFusion
Subjects: diseases & disorders > cancer
bioinformatics > computational biology > algorithms
Investigative techniques and equipment > assays > RNA-seq
CSHL Authors:
Communities: CSHL labs > Dobin Lab
CSHL Cancer Center Program > Cancer Genetics and Genomics Program
Depositing User: Matthew Dunn
Date: 21 October 2019
Date Deposited: 08 Nov 2019 17:35
Last Modified: 05 Nov 2020 21:36
PMCID: PMC6802306
Related URLs:
URI: https://repository.cshl.edu/id/eprint/38703

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