4.6 Article

Evolution of Alternative Splicing Regulation: Changes in Predicted Exonic Splicing Regulators Are Not Associated with Changes in Alternative Splicing Levels in Primates

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PLOS ONE
卷 4, 期 6, 页码 -

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PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0005800

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  1. Intramural NIH HHS Funding Source: Medline

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Alternative splicing is tightly regulated in a spatio-temporal and quantitative manner. This regulation is achieved by a complex interplay between spliceosomal ( trans) factors that bind to different sequence (cis) elements. cis-elements reside in both introns and exons and may either enhance or silence splicing. Differential combinations of cis-elements allows for a huge diversity of overall splicing signals, together comprising a complex 'splicing code'. Many cis-elements have been identified, and their effects on exon inclusion levels demonstrated in reporter systems. However, the impact of interspecific differences in these elements on the evolution of alternative splicing levels has not yet been investigated at genomic level. Here we study the effect of interspecific differences in predicted exonic splicing regulators (ESRs) on exon inclusion levels in human and chimpanzee. For this purpose, we compiled and studied comprehensive datasets of predicted ESRs, identified by several computational and experimental approaches, as well as microarray data for changes in alternative splicing levels between human and chimpanzee. Surprisingly, we found no association between changes in predicted ESRs and changes in alternative splicing levels. This observation holds across different ESR exon positions, exon lengths, and 59 splice site strengths. We suggest that this lack of association is mainly due to the great importance of context for ESR functionality: many ESR-like motifs in primates may have little or no effect on splicing, and thus interspecific changes at short-time scales may primarily occur in these effectively neutral ESRs. These results underscore the difficulties of using current computational ESR prediction algorithms to identify truly functionally important motifs, and provide a cautionary tale for studies of the effect of SNPs on splicing in human disease.

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