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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Autores principales: Haas, Brian J., Dobin, Alexander, Li, Bo, Stransky, Nicolas, Pochet, Nathalie, Regev, Aviv
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6802306/
https://www.ncbi.nlm.nih.gov/pubmed/31639029
http://dx.doi.org/10.1186/s13059-019-1842-9
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author Haas, Brian J.
Dobin, Alexander
Li, Bo
Stransky, Nicolas
Pochet, Nathalie
Regev, Aviv
author_facet Haas, Brian J.
Dobin, Alexander
Li, Bo
Stransky, Nicolas
Pochet, Nathalie
Regev, Aviv
author_sort Haas, Brian J.
collection PubMed
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.
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spelling pubmed-68023062019-10-22 Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods Haas, Brian J. Dobin, Alexander Li, Bo Stransky, Nicolas Pochet, Nathalie Regev, Aviv Genome Biol Research 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. BioMed Central 2019-10-21 /pmc/articles/PMC6802306/ /pubmed/31639029 http://dx.doi.org/10.1186/s13059-019-1842-9 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Haas, Brian J.
Dobin, Alexander
Li, Bo
Stransky, Nicolas
Pochet, Nathalie
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
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6802306/
https://www.ncbi.nlm.nih.gov/pubmed/31639029
http://dx.doi.org/10.1186/s13059-019-1842-9
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