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Changes in exon–intron structure during vertebrate evolution affect the splicing pattern of exons
Author(s) -
Sahar Gelfman,
David Burstein,
Osnat Penn,
Anna Savchenko,
Maayan Amit,
Schraga Schwartz,
Tal Pupko,
Gil Ast
Publication year - 2011
Publication title -
genome research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 9.556
H-Index - 297
eISSN - 1549-5469
pISSN - 1088-9051
DOI - 10.1101/gr.119834.110
Subject(s) - exon , intron , biology , splice site mutation , genetics , rna splicing , tandem exon duplication , alternative splicing , splice , evolutionary biology , vertebrate , genome , exon shuffling , gene , rna
Exon-intron architecture is one of the major features directing the splicing machinery to the short exons that are located within long flanking introns. However, the evolutionary dynamics of exon-intron architecture and its impact on splicing is largely unknown. Using a comparative genomic approach, we analyzed 17 vertebrate genomes and reconstructed the ancestral motifs of both 3' and 5' splice sites, as also the ancestral length of exons and introns. Our analyses suggest that vertebrate introns increased in length from the shortest ancestral introns to the longest primate introns. An evolutionary analysis of splice sites revealed that weak splice sites act as a restrictive force keeping introns short. In contrast, strong splice sites allow recognition of exons flanked by long introns. Reconstruction of the ancestral state suggests these phenomena were not prevalent in the vertebrate ancestor, but appeared during vertebrate evolution. By calculating evolutionary rate shifts in exons, we identified cis-acting regulatory sequences that became fixed during the transition from early vertebrates to mammals. Experimental validations performed on a selection of these hexamers confirmed their regulatory function. We additionally revealed many features of exons that can discriminate alternative from constitutive exons. These features were integrated into a machine-learning approach to predict whether an exon is alternative. Our algorithm obtains very high predictive power (AUC of 0.91), and using these predictions we have identified and successfully validated novel alternatively spliced exons. Overall, we provide novel insights regarding the evolutionary constraints acting upon exons and their recognition by the splicing machinery.

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