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A Statistical Method for the Detection of Alternative Splicing Using RNA-Seq

Deep sequencing of transcriptome (RNA-seq) provides unprecedented opportunity to interrogate plausible mRNA splicing patterns by mapping RNA-seq reads to exon junctions (thereafter junction reads). In most previous studies, exon junctions were detected by using the quantitative information of juncti...

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Detalles Bibliográficos
Autores principales: Wang, Liguo, Xi, Yuanxin, Yu, Jun, Dong, Liping, Yen, Laising, Li, Wei
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2798953/
https://www.ncbi.nlm.nih.gov/pubmed/20072613
http://dx.doi.org/10.1371/journal.pone.0008529
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author Wang, Liguo
Xi, Yuanxin
Yu, Jun
Dong, Liping
Yen, Laising
Li, Wei
author_facet Wang, Liguo
Xi, Yuanxin
Yu, Jun
Dong, Liping
Yen, Laising
Li, Wei
author_sort Wang, Liguo
collection PubMed
description Deep sequencing of transcriptome (RNA-seq) provides unprecedented opportunity to interrogate plausible mRNA splicing patterns by mapping RNA-seq reads to exon junctions (thereafter junction reads). In most previous studies, exon junctions were detected by using the quantitative information of junction reads. The quantitative criterion (e.g. minimum of two junction reads), although is straightforward and widely used, usually results in high false positive and false negative rates, owning to the complexity of transcriptome. Here, we introduced a new metric, namely Minimal Match on Either Side of exon junction (MMES), to measure the quality of each junction read, and subsequently implemented an empirical statistical model to detect exon junctions. When applied to a large dataset (>200M reads) consisting of mouse brain, liver and muscle mRNA sequences, and using independent transcripts databases as positive control, our method was proved to be considerably more accurate than previous ones, especially for detecting junctions originated from low-abundance transcripts. Our results were also confirmed by real time RT-PCR assay. The MMES metric can be used either in this empirical statistical model or in other more sophisticated classifiers, such as logistic regression.
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spelling pubmed-27989532010-01-14 A Statistical Method for the Detection of Alternative Splicing Using RNA-Seq Wang, Liguo Xi, Yuanxin Yu, Jun Dong, Liping Yen, Laising Li, Wei PLoS One Research Article Deep sequencing of transcriptome (RNA-seq) provides unprecedented opportunity to interrogate plausible mRNA splicing patterns by mapping RNA-seq reads to exon junctions (thereafter junction reads). In most previous studies, exon junctions were detected by using the quantitative information of junction reads. The quantitative criterion (e.g. minimum of two junction reads), although is straightforward and widely used, usually results in high false positive and false negative rates, owning to the complexity of transcriptome. Here, we introduced a new metric, namely Minimal Match on Either Side of exon junction (MMES), to measure the quality of each junction read, and subsequently implemented an empirical statistical model to detect exon junctions. When applied to a large dataset (>200M reads) consisting of mouse brain, liver and muscle mRNA sequences, and using independent transcripts databases as positive control, our method was proved to be considerably more accurate than previous ones, especially for detecting junctions originated from low-abundance transcripts. Our results were also confirmed by real time RT-PCR assay. The MMES metric can be used either in this empirical statistical model or in other more sophisticated classifiers, such as logistic regression. Public Library of Science 2010-01-08 /pmc/articles/PMC2798953/ /pubmed/20072613 http://dx.doi.org/10.1371/journal.pone.0008529 Text en Wang et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Wang, Liguo
Xi, Yuanxin
Yu, Jun
Dong, Liping
Yen, Laising
Li, Wei
A Statistical Method for the Detection of Alternative Splicing Using RNA-Seq
title A Statistical Method for the Detection of Alternative Splicing Using RNA-Seq
title_full A Statistical Method for the Detection of Alternative Splicing Using RNA-Seq
title_fullStr A Statistical Method for the Detection of Alternative Splicing Using RNA-Seq
title_full_unstemmed A Statistical Method for the Detection of Alternative Splicing Using RNA-Seq
title_short A Statistical Method for the Detection of Alternative Splicing Using RNA-Seq
title_sort statistical method for the detection of alternative splicing using rna-seq
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2798953/
https://www.ncbi.nlm.nih.gov/pubmed/20072613
http://dx.doi.org/10.1371/journal.pone.0008529
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