Alternative isoform regulation in human tissue transcriptomes View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2008-11

AUTHORS

Eric T. Wang, Rickard Sandberg, Shujun Luo, Irina Khrebtukova, Lu Zhang, Christine Mayr, Stephen F. Kingsmore, Gary P. Schroth, Christopher B. Burge

ABSTRACT

Through alternative processing of pre-messenger RNAs, individual mammalian genes often produce multiple mRNA and protein isoforms that may have related, distinct or even opposing functions. Here we report an in-depth analysis of 15 diverse human tissue and cell line transcriptomes on the basis of deep sequencing of complementary DNA fragments, yielding a digital inventory of gene and mRNA isoform expression. Analyses in which sequence reads are mapped to exon-exon junctions indicated that 92-94% of human genes undergo alternative splicing, 86% with a minor isoform frequency of 15% or more. Differences in isoform-specific read densities indicated that most alternative splicing and alternative cleavage and polyadenylation events vary between tissues, whereas variation between individuals was approximately twofold to threefold less common. Extreme or 'switch-like' regulation of splicing between tissues was associated with increased sequence conservation in regulatory regions and with generation of full-length open reading frames. Patterns of alternative splicing and alternative cleavage and polyadenylation were strongly correlated across tissues, suggesting coordinated regulation of these processes, and sequence conservation of a subset of known regulatory motifs in both alternative introns and 3' untranslated regions suggested common involvement of specific factors in tissue-level regulation of both splicing and polyadenylation. More... »

PAGES

470

References to SciGraph publications

Journal

TITLE

Nature

ISSUE

7221

VOLUME

456

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/nature07509

DOI

http://dx.doi.org/10.1038/nature07509

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1029002744

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/18978772


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