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      Single Nucleotide Polymorphism–Based Validation of Exonic Splicing Enhancers

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          Abstract

          Because deleterious alleles arising from mutation are filtered by natural selection, mutations that create such alleles will be underrepresented in the set of common genetic variation existing in a population at any given time. Here, we describe an approach based on this idea called VERIFY (variant elimination reinforces functionality), which can be used to assess the extent of natural selection acting on an oligonucleotide motif or set of motifs predicted to have biological activity. As an application of this approach, we analyzed a set of 238 hexanucleotides previously predicted to have exonic splicing enhancer (ESE) activity in human exons using the relative enhancer and silencer classification by unanimous enrichment (RESCUE)-ESE method. Aligning the single nucleotide polymorphisms (SNPs) from the public human SNP database to the chimpanzee genome allowed inference of the direction of the mutations that created present-day SNPs. Analyzing the set of SNPs that overlap RESCUE-ESE hexamers, we conclude that nearly one-fifth of the mutations that disrupt predicted ESEs have been eliminated by natural selection (odds ratio = 0.82 ± 0.05). This selection is strongest for the predicted ESEs that are located near splice sites. Our results demonstrate a novel approach for quantifying the extent of natural selection acting on candidate functional motifs and also suggest certain features of mutations/SNPs, such as proximity to the splice site and disruption or alteration of predicted ESEs, that should be useful in identifying variants that might cause a biological phenotype.

          Abstract

          A computational analysis of single nucleotide polymorphisms showed that one-fifth of mutations that disrupt predicted exonic splicing enhancers have been eliminated by natural selection

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          Most cited references31

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          Recent segmental duplications in the human genome.

          Primate-specific segmental duplications are considered important in human disease and evolution. The inability to distinguish between allelic and duplication sequence overlap has hampered their characterization as well as assembly and annotation of our genome. We developed a method whereby each public sequence is analyzed at the clone level for overrepresentation within a whole-genome shotgun sequence. This test has the ability to detect duplications larger than 15 kilobases irrespective of copy number, location, or high sequence similarity. We mapped 169 large regions flanked by highly similar duplications. Twenty-four of these hot spots of genomic instability have been associated with genetic disease. Our analysis indicates a highly nonrandom chromosomal and genic distribution of recent segmental duplications, with a likely role in expanding protein diversity.
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            Predictive identification of exonic splicing enhancers in human genes.

            Specific short oligonucleotide sequences that enhance pre-mRNA splicing when present in exons, termed exonic splicing enhancers (ESEs), play important roles in constitutive and alternative splicing. A computational method, RESCUE-ESE, was developed that predicts which sequences have ESE activity by statistical analysis of exon-intron and splice site composition. When large data sets of human gene sequences were used, this method identified 10 predicted ESE motifs. Representatives of all 10 motifs were found to display enhancer activity in vivo, whereas point mutants of these sequences exhibited sharply reduced activity. The motifs identified enable prediction of the splicing phenotypes of exonic mutations in human genes.
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              Exonic splicing enhancers: mechanism of action, diversity and role in human genetic diseases.

              Exonic splicing enhancers (ESEs) are discrete sequences within exons that promote both constitutive and regulated splicing. The precise mechanism by which ESEs facilitate the assembly of splicing complexes has been controversial. However, recent studies have provided insights into this question and have led to a new model for ESE function. Other recent work has suggested that ESEs are comprised of diverse sequences and occur frequently within exons. Ominously, these latter studies predict that many human genetic diseases linked to mutations within exons might be caused by the inactivation of ESEs.
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                Author and article information

                Journal
                PLoS Biol
                pbio
                PLoS Biology
                Public Library of Science (San Francisco, USA )
                1544-9173
                1545-7885
                September 2004
                31 August 2004
                : 2
                : 9
                : e268
                Affiliations
                [1] 1Center for Cancer Research, Massachusetts Institute of Technology Cambridge, MassachusettsUnited States of America
                [2] 2Department of Biology, Massachusetts Institute of Technology Cambridge, MassachusettsUnited States of America
                [3] 3McGovern Institute, Massachusetts Institute of Technology Cambridge, MassachusettsUnited States of America
                Article
                10.1371/journal.pbio.0020268
                514884
                15340491
                57aa3284-10be-4ea7-84d6-b5a498d3aa47
                Copyright: © Fairbrother 2004 et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
                History
                : 18 March 2004
                : 15 June 2004
                Categories
                Research Article
                Bioinformatics/Computational Biology
                Cell Biology
                Genetics/Genomics/Gene Therapy
                Molecular Biology/Structural Biology
                Primates
                Homo (Human)

                Life sciences
                Life sciences

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