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dc.contributor.authorBadr, Emanen
dc.contributor.authorElHefnawi, Mahmouden
dc.contributor.authorHeath, Lenwood S.en
dc.date.accessioned2018-08-01T17:31:52Zen
dc.date.available2018-08-01T17:31:52Zen
dc.date.issued2016-11-18en
dc.identifier.othere0166978en
dc.identifier.urihttp://hdl.handle.net/10919/84465en
dc.description.abstractAlternative splicing is a vital process for regulating gene expression and promoting proteomic diversity. It plays a key role in tissue-specific expressed genes. This specificity is mainly regulated by splicing factors that bind to specific sequences called splicing regulatory elements (SREs). Here, we report a genome-wide analysis to study alternative splicing on multiple tissues, including brain, heart, liver, and muscle. We propose a pipeline to identify differential exons across tissues and hence tissue-specific SREs. In our pipeline, we utilize the DEXSeq package along with our previously reported algorithms. Utilizing the publicly available RNA-Seq data set from the Human BodyMap project, we identified 28,100 differentially used exons across the four tissues. We identified tissue-specific exonic splicing enhancers that overlap with various previously published experimental and computational databases. A complicated exonic enhancer regulatory network was revealed, where multiple exonic enhancers were found across multiple tissues while some were found only in specific tissues. Putative combinatorial exonic enhancers and silencers were discovered as well, which may be responsible for exon inclusion or exclusion across tissues. Some of the exonic enhancers are found to be co-occurring with multiple exonic silencers and vice versa, which demonstrates a complicated relationship between tissue-specific exonic enhancers and silencers.en
dc.format.mimetypeapplication/pdfen
dc.language.isoen_USen
dc.publisherPLOSen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleComputational Identification of Tissue-Specific Splicing Regulatory Elements in Human Genes from RNA-Seq Dataen
dc.typeArticle - Refereeden
dc.description.versionPeer Revieweden
dc.contributor.departmentComputer Scienceen
dc.title.serialPLOS ONEen
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0166978en
dc.identifier.volume11en
dc.identifier.issue11en
dc.type.dcmitypeTexten
dc.identifier.pmid27861625en
dc.identifier.eissn1932-6203en


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Creative Commons Attribution 4.0 International
License: Creative Commons Attribution 4.0 International