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

Brian J Haas; Alexander Dobin; Bo Li; Nicolas Stransky; Nathalie Pochet; Aviv Regev

doi:10.1186/s13059-019-1842-9

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.

Authors

Brian J Haas¹, Alexander Dobin², Bo Li^{3

4}, Nicolas Stransky⁵, Nathalie Pochet^{3

6}, Aviv Regev^{3

7}

Affiliations

¹ Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA. [email protected].
² Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA.
³ Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
⁴ Center for Immunology and Inflammatory Diseases, Division of Rheumatology, Allergy, and Immunology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02129, USA.
⁵ Celsius Therapeutics, Cambridge, MA, 02139, USA.
⁶ Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
⁷ Howard Hughes Medical Institute, and Koch Institute for Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, 02140, USA.

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.

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*

Abstract

Publication types

MeSH terms

Grants and funding