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.