Dataset Information

An ANN model for the identification of deleterious nsSNPs in tumor suppressor genes.

ABSTRACT: UNLABELLED:Human genetic variations primarily result from single nucleotide polymorphisms (SNPs) that occurs approximately every 1000 bases in the overall human population. The non-synonymous SNPs (nsSNPs), lead to amino acid changes in the protein product may account for nearly half of the known genetic variations linked to inherited human diseases and cancer. One of the main problems of medical genetics today is to identify nsSNPs that underlie disease-related phenotypes in humans. An attempt was made to develop a new approach to predict such nsSNPs. This would enhance our understanding of genetic diseases and helps to predict the disease. We detect nsSNPs and all possible and reliable alleles by ANN, a soft computing model using potential SNP information. Reliable nsSNPs are identified, based on the reconstructed alleles and on sequence redundancy. The model gives good results with mean specificity (95.85&), sensitivity (97.40&) and accuracy (96.25&). Our results indicate that ANNs can serve as a useful method to analyze quantitative effect of nsSNPs on protein function and would be useful for large-scale analysis of genomic nsSNP data. AVAILABILITY:The database is available for free at http://www.snp.mirworks.in.

SUBMITTER: Chandra V

PROVIDER: S-EPMC3064852 | biostudies-other | 2011 Mar

REPOSITORIES: biostudies-other

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An ANN model for the identification of deleterious nsSNPs in tumor suppressor genes.

Chandra Vinod V Ramakrishnan Rejimoan R Ramanathan Shalini S

Bioinformation 20110302 1

<h4>Unlabelled</h4>Human genetic variations primarily result from single nucleotide polymorphisms (SNPs) that occurs approximately every 1000 bases in the overall human population. The non-synonymous SNPs (nsSNPs), lead to amino acid changes in the protein product may account for nearly half of the known genetic variations linked to inherited human diseases and cancer. One of the main problems of medical genetics today is to identify nsSNPs that underlie disease-related phenotypes in humans. An ...[more]

PMID: 21464845

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Project description:Tumor suppressor genes (TSGs) are sometimes inactivated by transcriptional silencing through promoter hypermethylation. To identify novel methylated TSGs in melanoma, we carried out global mRNA expression proﬁling on a panel of 12 melanoma cell lines treated with a combination of 5-Aza-2-deoxycytidine (5AzadC) and an inhibitor of histone deacetylase, Trichostatin A. Reactivation of gene expression after drug treatment was assessed using Illumina whole-genome microarrays. After qRT-PCR conﬁrmation, we followed up 8 genes (AKAP12, ARHGEF16, ARHGAP27, ENC1, PPP1R3C, PPP1R14C, RARRES1, and TP53INP1) by quantitative DNA methylation analysis using mass spectrometry of base-speciﬁc cleaved ampliﬁcation products in panels of melanoma cell lines and fresh tumors. PPP1R3C, ENC1, RARRES1, and TP53INP1, showed reduced mRNA expression in 35–59% of the melanoma cell lines compared to melanocytes and which was correlated with a high proportion of promoter methylation (>40–60%). The same genes also showed extensive promoter methylation in 6–25% of the tumor samples, thus conﬁrming them as novel candidate TSGs in melanoma. We sought to identify melanoma TSGs silenced by promoter methylation by carrying out an array-based analysis in a well-annotated panel of 12 cell lines after combined treatment with 5AzadC and an inhibitor of histone deacetylase, Trichostatin A (TSA). Expression profiles were generated for each cell line before and after drug treatment using Illumina Sentrix Human-6 Expression version 2 BeadChips. Genes reactivated in all 12 cell lines were removed from further analysis since they are likely responding to drug treatment as part of the ‘‘cellular stress response,’’ or due to promoter demethylation of genes normally silenced in the melanocytic lineage. Genes were further ﬁltered to identify those with an average of >4-fold increased expression in at least four samples in the panel of 12 lines and >10-fold increase in at least one of the cell lines.

Dataset Information

An ANN model for the identification of deleterious nsSNPs in tumor suppressor genes.

Publications

An ANN model for the identification of deleterious nsSNPs in tumor suppressor genes.

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