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...
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| Format: | Article |
| Language: | English |
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Springer Science and Business Media LLC
2020
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| Online Access: | https://hdl.handle.net/1721.1/124943 |
| _version_ | 1811094692090085376 |
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| author | Regev, Aviv |
| author2 | Massachusetts Institute of Technology. Department of Biology |
| author_facet | Massachusetts Institute of Technology. Department of Biology Regev, Aviv |
| author_sort | Regev, Aviv |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T16:03:32Z |
| format | Article |
| id | mit-1721.1/124943 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T16:03:32Z |
| publishDate | 2020 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1249432022-09-29T17:56:11Z Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods Regev, Aviv Massachusetts Institute of Technology. Department of Biology Koch Institute for Integrative Cancer Research at MIT 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) 2020-04-30T17:31:22Z 2020-04-30T17:31:22Z 2019-10-21 2020-01-28T19:09:16Z Article http://purl.org/eprint/type/JournalArticle 1474-760X https://hdl.handle.net/1721.1/124943 Haas, Brian J. et al. “Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods.” Genome biology 20 (2019): 213 © 2019 The Author(s) en 10.1186/s13059-019-1842-9 Genome biology Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Springer Science and Business Media LLC BioMed Central (BMC) |
| spellingShingle | Regev, Aviv Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods |
| title | Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods |
| title_full | Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods |
| title_fullStr | Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods |
| title_full_unstemmed | Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods |
| title_short | Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods |
| title_sort | accuracy assessment of fusion transcript detection via read mapping and de novo fusion transcript assembly based methods |
| url | https://hdl.handle.net/1721.1/124943 |
| work_keys_str_mv | AT regevaviv accuracyassessmentoffusiontranscriptdetectionviareadmappinganddenovofusiontranscriptassemblybasedmethods |