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A Bayesian approach for accurate de novo transcriptome assembly

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dc.contributor.authorShi, Xuen
dc.contributor.authorWang, Xiaoen
dc.contributor.authorNeuwald, Andrew F.en
dc.contributor.authorHilakivi-Clarke, Leenaen
dc.contributor.authorClarke, Roberten
dc.contributor.authorXuan, Jianhuaen
dc.date.accessioned2022-03-30T12:07:08Zen
dc.date.available2022-03-30T12:07:08Zen
dc.date.issued2021-09-03en
dc.description.abstractDe novo transcriptome assembly from billions of RNA-seq reads is very challenging due to alternative splicing and various levels of expression, which often leads to incorrect, mis-assembled transcripts. BayesDenovo addresses this problem by using both a read-guided strategy to accurately reconstruct splicing graphs from the RNA-seq data and a Bayesian strategy to estimate, from these graphs, the probability of transcript expression without penalizing poorly expressed transcripts. Simulation and cell line benchmark studies demonstrate that BayesDenovo is very effective in reducing false positives and achieves much higher accuracy than other assemblers, especially for alternatively spliced genes and for highly or poorly expressed transcripts. Moreover, BayesDenovo is more robust on multiple replicates by assembling a larger portion of common transcripts. When applied to breast cancer data, BayesDenovo identifies phenotype-specific transcripts associated with breast cancer recurrence.en
dc.description.notesThis work is supported by National Institutes of Health (NIH) (CA149653, CA164384, CA149147 and GM125878).en
dc.description.sponsorshipNational Institutes of Health (NIH)United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [CA149653, CA164384, CA149147, GM125878]en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1038/s41598-021-97015-xen
dc.identifier.issn2045-2322en
dc.identifier.issue1en
dc.identifier.other17663en
dc.identifier.pmid34480063en
dc.identifier.urihttp://hdl.handle.net/10919/109501en
dc.identifier.volume11en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleA Bayesian approach for accurate de novo transcriptome assemblyen
dc.title.serialScientific Reportsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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