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

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.

SUBMITTER: Haas BJ

PROVIDER: S-EPMC6802306 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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

Haas Brian J BJ Dobin Alexander A Li Bo B Stransky Nicolas N Pochet Nathalie N Regev Aviv A

Genome biology 20191021 1

<h4>Background</h4>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.<h4>Results</h4>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- ...[more]

PMID: 31639029

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