Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods
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. Resul...
| Main Author: | |
|---|---|
| Other Authors: | , |
| Format: | Article |
| Language: | English |
| Published: |
Springer Science and Business Media LLC,
2020-04-30T17:31:22Z.
|
| Subjects: | |
| Online Access: | Get fulltext |
| Summary: | 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. National Cancer Institute (U.S.) (Grant U24CA180922) National Cancer Institute (U.S.) (Grant R50CA211461) National Cancer Institute (U.S.) (Grant R21CA209940) National Cancer Institute (U.S.) (Grant U01CA214846) |
|---|