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

Genome Biol. 2019 Oct 21;20(1):213. doi: 10.1186/s13059-019-1842-9.


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.

Keywords: Benchmarking; Cancer; Fusion; RNA-seq; STAR-Fusion; TrinityFusion.

Publication types

  • Evaluation Study
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Benchmarking
  • Gene Fusion*
  • Genomics / methods*
  • Neoplasms / genetics
  • Neoplasms / metabolism*
  • Sequence Analysis, RNA
  • Software*
  • Transcriptome*