In silico prediction of splice-altering single nucleotide variants in the human genome

In silico tools have been developed to predict variants that may have an impact on pre-mRNA splicing. The major limitation of the application of these tools to basic research and clinical practice is the difficulty in interpreting the output. Most tools only predict potential splice sites given a DN...

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Vydáno v:Nucleic acids research Ročník 42; číslo 22; s. 13534 - 13544
Hlavní autoři: Jian, Xueqiu, Boerwinkle, Eric, Liu, Xiaoming
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Oxford University Press 16.12.2014
Témata:
Alternative Splicing
Artificial Intelligence
Computer Simulation
Data Resources and Analyses
Genes, Neoplasm
Genetic Variation
Genome, Human
Genomics - methods
Humans
Position-Specific Scoring Matrices
RNA Splice Sites
ISSN:0305-1048, 1362-4962, 1362-4962
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Shrnutí:In silico tools have been developed to predict variants that may have an impact on pre-mRNA splicing. The major limitation of the application of these tools to basic research and clinical practice is the difficulty in interpreting the output. Most tools only predict potential splice sites given a DNA sequence without measuring splicing signal changes caused by a variant. Another limitation is the lack of large-scale evaluation studies of these tools. We compared eight in silico tools on 2959 single nucleotide variants within splicing consensus regions (scSNVs) using receiver operating characteristic analysis. The Position Weight Matrix model and MaxEntScan outperformed other methods. Two ensemble learning methods, adaptive boosting and random forests, were used to construct models that take advantage of individual methods. Both models further improved prediction, with outputs of directly interpretable prediction scores. We applied our ensemble scores to scSNVs from the Catalogue of Somatic Mutations in Cancer database. Analysis showed that predicted splice-altering scSNVs are enriched in recurrent scSNVs and known cancer genes. We pre-computed our ensemble scores for all potential scSNVs across the human genome, providing a whole genome level resource for identifying splice-altering scSNVs discovered from large-scale sequencing studies.
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ISSN:0305-1048
1362-4962
1362-4962
DOI:10.1093/nar/gku1206