A Bayesian approach for accurate de novo transcriptome assembly

Abstract De 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 re...

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Autores principales:	Xu Shi, Xiao Wang, Andrew F. Neuwald, Leena Halakivi-Clarke, Robert Clarke, Jianhua Xuan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5c8b642b39f143d69538c2b2e7bf0e53
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spelling	oai:doaj.org-article:5c8b642b39f143d69538c2b2e7bf0e532021-12-02T19:04:35ZA Bayesian approach for accurate de novo transcriptome assembly10.1038/s41598-021-97015-x2045-2322https://doaj.org/article/5c8b642b39f143d69538c2b2e7bf0e532021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97015-xhttps://doaj.org/toc/2045-2322Abstract De 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.Xu ShiXiao WangAndrew F. NeuwaldLeena Halakivi-ClarkeRobert ClarkeJianhua XuanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xu Shi Xiao Wang Andrew F. Neuwald Leena Halakivi-Clarke Robert Clarke Jianhua Xuan A Bayesian approach for accurate de novo transcriptome assembly
description	Abstract De 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.
format	article
author	Xu Shi Xiao Wang Andrew F. Neuwald Leena Halakivi-Clarke Robert Clarke Jianhua Xuan
author_facet	Xu Shi Xiao Wang Andrew F. Neuwald Leena Halakivi-Clarke Robert Clarke Jianhua Xuan
author_sort	Xu Shi
title	A Bayesian approach for accurate de novo transcriptome assembly
title_short	A Bayesian approach for accurate de novo transcriptome assembly
title_full	A Bayesian approach for accurate de novo transcriptome assembly
title_fullStr	A Bayesian approach for accurate de novo transcriptome assembly
title_full_unstemmed	A Bayesian approach for accurate de novo transcriptome assembly
title_sort	bayesian approach for accurate de novo transcriptome assembly
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5c8b642b39f143d69538c2b2e7bf0e53
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A Bayesian approach for accurate de novo transcriptome assembly

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