Dataset Information

SNPDelScore: combining multiple methods to score deleterious effects of noncoding mutations in the human genome.

ABSTRACT: SUMMARY:Addressing deleterious effects of noncoding mutations is an essential step towards the identification of disease-causal mutations of gene regulatory elements. Several methods for quantifying the deleteriousness of noncoding mutations using artificial intelligence, deep learning and other approaches have been recently proposed. Although the majority of the proposed methods have demonstrated excellent accuracy on different test sets, there is rarely a consensus. In addition, advanced statistical and artificial learning approaches used by these methods make it difficult porting these methods outside of the labs that have developed them. To address these challenges and to transform the methodological advances in predicting deleterious noncoding mutations into a practical resource available for the broader functional genomics and population genetics communities, we developed SNPDelScore, which uses a panel of proposed methods for quantifying deleterious effects of noncoding mutations to precompute and compare the deleteriousness scores of all common SNPs in the human genome in 44 cell lines. The panel of deleteriousness scores of a SNP computed using different methods is supplemented by functional information from the GWAS Catalog, libraries of transcription factor-binding sites, and genic characteristics of mutations. SNPDelScore comes with a genome browser capable of displaying and comparing large sets of SNPs in a genomic locus and rapidly identifying consensus SNPs with the highest deleteriousness scores making those prime candidates for phenotype-causal polymorphisms. AVAILABILITY AND IMPLEMENTATION:https://www.ncbi.nlm.nih.gov/research/snpdelscore/. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

SUBMITTER: Alvarez RV

PROVIDER: S-EPMC5860207 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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SNPDelScore: combining multiple methods to score deleterious effects of noncoding mutations in the human genome.

Alvarez Roberto Vera RV Li Shan S Landsman David D Ovcharenko Ivan I

Bioinformatics (Oxford, England) 20180101 2

<h4>Summary</h4>Addressing deleterious effects of noncoding mutations is an essential step towards the identification of disease-causal mutations of gene regulatory elements. Several methods for quantifying the deleteriousness of noncoding mutations using artificial intelligence, deep learning and other approaches have been recently proposed. Although the majority of the proposed methods have demonstrated excellent accuracy on different test sets, there is rarely a consensus. In addition, advanc ...[more]

PMID: 28968739

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Project description:Multiple sclerosis (MS) is an immune-mediated disease of the central nervous system with genetics and environmental determinants. Studies focused on the neurogenetics of MS showed that mitochondrial DNA (mtDNA) mutations that can ultimately lead to mitochondrial dysfunction, alter brain energy metabolism and cause neurodegeneration. We analyzed the whole mitochondrial genome using next-generation sequencing (NGS) from 47 Saudi individuals, 23 patients with relapsing-remitting MS and 24 healthy controls to identify mtDNA disease-related mutations/variants. A large number of variants were detected in the D-loop and coding genes of mtDNA. While distinct unique variants were only present in patients or only occur in controls, a number of common variants were shared among the two groups. The prevalence of some common variants differed significantly between patients and controls, thus could be implicated in susceptibility to MS. Of the unique variants only present in the patients, 34 were missense mutations, located in different mtDNA-encoded genes. Seven of these mutations were not previously reported in MS, and predicted to be deleterious with considerable impacts on the functions and structures of encoded-proteins and may play a role in the pathogenesis of MS. These include two heteroplasmic mutations namely 10237T>C in MT-ND3 gene and 15884G>C in MT-CYB gene; and three homoplasmic mutations namely 9288A>G in MT-CO3 gene, 14484T>C in MT-ND6 gene, 15431G>A in MT-CYB gene, 8490T>C in MT-ATP8 gene and 5437C>T in MT-ND2 gene. Notably some patients harboured multiple mutations while other patients carried the same mutations. This study is the first to sequence the entire mitochondrial genome in MS patients in an Arab population. Our results expanded the mutational spectrum of mtDNA variants in MS and highlighted the efficiency of NGS in population-specific mtDNA variant discovery. Further investigations in a larger cohort are warranted to confirm the role of mtDNA MS.

Dataset Information

SNPDelScore: combining multiple methods to score deleterious effects of noncoding mutations in the human genome.

Publications

SNPDelScore: combining multiple methods to score deleterious effects of noncoding mutations in the human genome.

Similar Datasets

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