Inference of alternative splicing from RNA-Seq data with probabilistic splice graphs | Zendy

Laura LeGault | Zendy; Colin N. Dewey | Zendy

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Inference of alternative splicing from RNA-Seq data with probabilistic splice graphs

Author(s) -

Laura LeGault,

Colin N. Dewey

Publication year - 2013

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btt396

Subject(s) - inference , computer science , probabilistic logic , rna seq , identifiability , computational biology , rna splicing , alternative splicing , theoretical computer science , rna , machine learning , gene , artificial intelligence , biology , genetics , transcriptome , gene expression , messenger rna

Alternative splicing and other processes that allow for different transcripts to be derived from the same gene are significant forces in the eukaryotic cell. RNA-Seq is a promising technology for analyzing alternative transcripts, as it does not require prior knowledge of transcript structures or genome sequences. However, analysis of RNA-Seq data in the presence of genes with large numbers of alternative transcripts is currently challenging due to efficiency, identifiability and representation issues.

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