Prediction and Quantification of Splice Events from RNA-Seq Data

Analysis of splice variants from short read RNA-seq data remains a challenging problem. Here we present a novel method for the genome-guided prediction and quantification of splice events from RNA-seq data, which enables the analysis of unannotated and complex splice events. Splice junctions and exo...

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Main Authors:	Goldstein, Leonard D., Cao, Yi, Pau, Gregoire, Lawrence, Michael, Wu, Thomas D., Seshagiri, Somasekar, Gentleman, Robert
Format:	Online
Language:	English
Published:	Public Library of Science 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4878813/

id	pubmed-4878813
recordtype	oai_dc
spelling	pubmed-48788132016-06-09 Prediction and Quantification of Splice Events from RNA-Seq Data Goldstein, Leonard D. Cao, Yi Pau, Gregoire Lawrence, Michael Wu, Thomas D. Seshagiri, Somasekar Gentleman, Robert Research Article Analysis of splice variants from short read RNA-seq data remains a challenging problem. Here we present a novel method for the genome-guided prediction and quantification of splice events from RNA-seq data, which enables the analysis of unannotated and complex splice events. Splice junctions and exons are predicted from reads mapped to a reference genome and are assembled into a genome-wide splice graph. Splice events are identified recursively from the graph and are quantified locally based on reads extending across the start or end of each splice variant. We assess prediction accuracy based on simulated and real RNA-seq data, and illustrate how different read aligners (GSNAP, HISAT2, STAR, TopHat2) affect prediction results. We validate our approach for quantification based on simulated data, and compare local estimates of relative splice variant usage with those from other methods (MISO, Cufflinks) based on simulated and real RNA-seq data. In a proof-of-concept study of splice variants in 16 normal human tissues (Illumina Body Map 2.0) we identify 249 internal exons that belong to known genes but are not related to annotated exons. Using independent RNA samples from 14 matched normal human tissues, we validate 9/9 of these exons by RT-PCR and 216/249 by paired-end RNA-seq (2 x 250 bp). These results indicate that de novo prediction of splice variants remains beneficial even in well-studied systems. An implementation of our method is freely available as an R/Bioconductor package SGSeq. Public Library of Science 2016-05-24 /pmc/articles/PMC4878813/ /pubmed/27218464 http://dx.doi.org/10.1371/journal.pone.0156132 Text en © 2016 Goldstein 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 (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Goldstein, Leonard D. Cao, Yi Pau, Gregoire Lawrence, Michael Wu, Thomas D. Seshagiri, Somasekar Gentleman, Robert
spellingShingle	Goldstein, Leonard D. Cao, Yi Pau, Gregoire Lawrence, Michael Wu, Thomas D. Seshagiri, Somasekar Gentleman, Robert Prediction and Quantification of Splice Events from RNA-Seq Data
author_facet	Goldstein, Leonard D. Cao, Yi Pau, Gregoire Lawrence, Michael Wu, Thomas D. Seshagiri, Somasekar Gentleman, Robert
author_sort	Goldstein, Leonard D.
title	Prediction and Quantification of Splice Events from RNA-Seq Data
title_short	Prediction and Quantification of Splice Events from RNA-Seq Data
title_full	Prediction and Quantification of Splice Events from RNA-Seq Data
title_fullStr	Prediction and Quantification of Splice Events from RNA-Seq Data
title_full_unstemmed	Prediction and Quantification of Splice Events from RNA-Seq Data
title_sort	prediction and quantification of splice events from rna-seq data
description	Analysis of splice variants from short read RNA-seq data remains a challenging problem. Here we present a novel method for the genome-guided prediction and quantification of splice events from RNA-seq data, which enables the analysis of unannotated and complex splice events. Splice junctions and exons are predicted from reads mapped to a reference genome and are assembled into a genome-wide splice graph. Splice events are identified recursively from the graph and are quantified locally based on reads extending across the start or end of each splice variant. We assess prediction accuracy based on simulated and real RNA-seq data, and illustrate how different read aligners (GSNAP, HISAT2, STAR, TopHat2) affect prediction results. We validate our approach for quantification based on simulated data, and compare local estimates of relative splice variant usage with those from other methods (MISO, Cufflinks) based on simulated and real RNA-seq data. In a proof-of-concept study of splice variants in 16 normal human tissues (Illumina Body Map 2.0) we identify 249 internal exons that belong to known genes but are not related to annotated exons. Using independent RNA samples from 14 matched normal human tissues, we validate 9/9 of these exons by RT-PCR and 216/249 by paired-end RNA-seq (2 x 250 bp). These results indicate that de novo prediction of splice variants remains beneficial even in well-studied systems. An implementation of our method is freely available as an R/Bioconductor package SGSeq.
publisher	Public Library of Science
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4878813/
_version_	1613583690959421440

Prediction and Quantification of Splice Events from RNA-Seq Data

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