Dataset Information

Exon first nucleotide mutations in splicing: evaluation of in silico prediction tools.

ABSTRACT: Mutations in the first nucleotide of exons (E(+1)) mostly affect pre-mRNA splicing when found in AG-dependent 3' splice sites, whereas AG-independent splice sites are more resistant. The AG-dependency, however, may be difficult to assess just from primary sequence data as it depends on the quality of the polypyrimidine tract. For this reason, in silico prediction tools are commonly used to score 3' splice sites. In this study, we have assessed the ability of sequence features and in silico prediction tools to discriminate between the splicing-affecting and non-affecting E(+1) variants. For this purpose, we newly tested 16 substitutions in vitro and derived other variants from literature. Surprisingly, we found that in the presence of the substituting nucleotide, the quality of the polypyrimidine tract alone was not conclusive about its splicing fate. Rather, it was the identity of the substituting nucleotide that markedly influenced it. Among the computational tools tested, the best performance was achieved using the Maximum Entropy Model and Position-Specific Scoring Matrix. As a result of this study, we have now established preliminary discriminative cut-off values showing sensitivity up to 95% and specificity up to 90%. This is expected to improve our ability to detect splicing-affecting variants in a clinical genetic setting.

SUBMITTER: Grodecka L

PROVIDER: S-EPMC3931810 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Exon first nucleotide mutations in splicing: evaluation of in silico prediction tools.

Grodecká Lucie L Lockerová Pavla P Ravčuková Barbora B Buratti Emanuele E Baralle Francisco E FE Dušek Ladislav L Freiberger Tomáš T

PloS one 20140221 2

Mutations in the first nucleotide of exons (E(+1)) mostly affect pre-mRNA splicing when found in AG-dependent 3' splice sites, whereas AG-independent splice sites are more resistant. The AG-dependency, however, may be difficult to assess just from primary sequence data as it depends on the quality of the polypyrimidine tract. For this reason, in silico prediction tools are commonly used to score 3' splice sites. In this study, we have assessed the ability of sequence features and in silico predi ...[more]

PMID: 24586880

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Project description:BackgroundAbnormalities of pre-mRNA splicing are increasingly recognized as an important mechanism through which gene mutations cause disease. However, apart from the mutations in the donor and acceptor sites, the effects on splicing of other sequence variations are difficult to predict. Loosely defined exonic and intronic sequences have been shown to affect splicing efficiency by means of silencing and enhancement mechanisms. Thus, nucleotide substitutions in these sequences can induce aberrant splicing. Web-based resources have recently been developed to facilitate the identification of nucleotide changes that could alter splicing. However, computer predictions do not always correlate with in vivo splicing defects. The issue of unclassified variants in cancer predisposing genes is very important both for the correct ascertainment of cancer risk and for the understanding of the basic mechanisms of cancer gene function and regulation. Therefore we aimed to verify how predictions that can be drawn from in silico analysis correlate with results obtained in an in vivo splicing assay.ResultsWe analysed 99 hMLH1 and hMSH2 missense mutations with six different algorithms. Transfection of three different cell lines with 20 missense mutations, showed that a minority of them lead to defective splicing. Moreover, we observed that some exons and some mutations show cell-specific differences in the frequency of exon inclusion.ConclusionOur results suggest that the available algorithms, while potentially helpful in identifying splicing modulators especially when they are located in weakly defined exons, do not always correspond to an obvious modification of the splicing pattern. Thus caution must be used in assessing the pathogenicity of a missense or silent mutation with prediction programs. The variations observed in the splicing proficiency in three different cell lines suggest that nucleotide changes may dictate alternative splice site selection in a tissue-specific manner contributing to the widely observed phenotypic variability in inherited cancers.

Dataset Information

Exon first nucleotide mutations in splicing: evaluation of in silico prediction tools.

Publications

Exon first nucleotide mutations in splicing: evaluation of in silico prediction tools.

Similar Datasets

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