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

By Brian J. Haas, Alexander Dobin, Bo Li, Nicolas Stransky, Nathalie Pochet, Aviv Regev. Genome Biology, 2019

532 citations recorded by the source corpus.

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. 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. 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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3dd6f8a1074b75601f3c8c5c08fd97256ae95880

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Citations in the corpus

• Accurate and efficient detection of gene fusions from RNA sequencing data (2021, background): “4.0 [Haas et al. 2019]) with respect to speed and accuracy.”