Dataset Information

Ab initio prediction of mutation-induced cryptic splice-site activation and exon skipping.

ABSTRACT: Mutations that affect splicing of precursor messenger RNAs play a major role in the development of hereditary diseases. Most splicing mutations have been found to eliminate GT or AG dinucleotides that define the 5' and 3' ends of introns, leading to exon skipping or cryptic splice-site activation. Although accurate description of the mis-spliced transcripts is critical for predicting phenotypic consequences of these alterations, their exact nature in affected individuals cannot often be determined experimentally. Using a comprehensive collection of exons that sustained cryptic splice-site activation or were skipped as a result of splice-site mutations, we have developed a multivariate logistic discrimination procedure that distinguishes the two aberrant splicing outcomes from DNA sequences. The new algorithm was validated using an independent sample of exons and implemented as a free online utility termed CRYP-SKIP (http://www.dbass.org.uk/cryp-skip/). The web application takes up one or more mutated alleles, each consisting of one exon and flanking intronic sequences, and provides a list of important predictor variables and their values, the overall probability of activating cryptic splice vs exon skipping, and the location and intrinsic strength of predicted cryptic splice sites in the input sequence. These results will facilitate phenotypic prediction of splicing mutations and provide further insights into splicing enhancer and silencer elements and their relative importance for splice-site selection in vivo.

SUBMITTER: Divina P

PROVIDER: S-EPMC2947103 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature

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Publications

Ab initio prediction of mutation-induced cryptic splice-site activation and exon skipping.

Divina Petr P Kvitkovicova Andrea A Buratti Emanuele E Vorechovsky Igor I

European journal of human genetics : EJHG 20090114 6

Mutations that affect splicing of precursor messenger RNAs play a major role in the development of hereditary diseases. Most splicing mutations have been found to eliminate GT or AG dinucleotides that define the 5' and 3' ends of introns, leading to exon skipping or cryptic splice-site activation. Although accurate description of the mis-spliced transcripts is critical for predicting phenotypic consequences of these alterations, their exact nature in affected individuals cannot often be determin ...[more]

PMID: 19142208

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Project description:BACKGROUND:Identifying splice sites is a necessary step to analyze the location and structure of genes. Two dinucleotides, GT and AG, are highly frequent on splice sites, and many other patterns are also on splice sites with important biological functions. Meanwhile, the dinucleotides occur frequently at the sequences without splice sites, which makes the prediction prone to generate false positives. Most existing tools select all the sequences with the two dimers and then focus on distinguishing the true splice sites from those pseudo ones. Such an approach will lead to a decrease in false positives; however, it will result in non-canonical splice sites missing. RESULT:We have designed SpliceFinder based on convolutional neural network (CNN) to predict splice sites. To achieve the ab initio prediction, we used human genomic data to train our neural network. An iterative approach is adopted to reconstruct the dataset, which tackles the data unbalance problem and forces the model to learn more features of splice sites. The proposed CNN obtains the classification accuracy of 90.25%, which is 10% higher than the existing algorithms. The method outperforms other existing methods in terms of area under receiver operating characteristics (AUC), recall, precision, and F1 score. Furthermore, SpliceFinder can find the exact position of splice sites on long genomic sequences with a sliding window. Compared with other state-of-the-art splice site prediction tools, SpliceFinder generates results in about half lower false positive while keeping recall higher than 0.8. Also, SpliceFinder captures the non-canonical splice sites. In addition, SpliceFinder performs well on the genomic sequences of Drosophila melanogaster, Mus musculus, Rattus, and Danio rerio without retraining. CONCLUSION:Based on CNN, we have proposed a new ab initio splice site prediction tool, SpliceFinder, which generates less false positives and can detect non-canonical splice sites. Additionally, SpliceFinder is transferable to other species without retraining. The source code and additional materials are available at https://gitlab.deepomics.org/wangruohan/SpliceFinder.

Dataset Information

Ab initio prediction of mutation-induced cryptic splice-site activation and exon skipping.

Publications

Ab initio prediction of mutation-induced cryptic splice-site activation and exon skipping.

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