Unconstrained mining of transcript data reveals increased alternative splicing complexity in the human transcriptome.

ABSTRACT: Mining massive amounts of transcript data for alternative splicing information is paramount to help understand how the maturation of RNA regulates gene expression. We developed an algorithm to cluster transcript data to annotated genes to detect unannotated splice variants. A higher number of alternatively spliced genes and isoforms were found compared to other alternative splicing databases. Comparison of human and mouse data revealed a marked increase, in human, of splice variants incorporating novel exons and retained introns. Previously unannotated exons were validated by tiling array expression data and shown to correspond preferentially to novel first exons. Retained introns were validated by tiling array and deep sequencing data. The majority of retained introns were shorter than 50

SUBMITTER: Mollet IG 

PROVIDER: S-EPMC2919708 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature