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

Chandra V, Ramakrishnan R, Ramanathan S - Bioinformation (2011)

Bottom Line: Human genetic variations primarily result from single nucleotide polymorphisms (SNPs) that occurs approximately every 1000 bases in the overall human population.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.

View Article: PubMed Central - PubMed

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.

No MeSH data available.


Related in: MedlinePlus

Organizational chart of deleterious nsSNP prediction system. Theinput to the method is in two forms: dbSNP id and amino acid sequence. If thesequence is dbSNP id, search the database for similar entries. The outputcontains general information of the gene and nsSNP, information on the variantand sequence based information.
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Figure 1: Organizational chart of deleterious nsSNP prediction system. Theinput to the method is in two forms: dbSNP id and amino acid sequence. If thesequence is dbSNP id, search the database for similar entries. The outputcontains general information of the gene and nsSNP, information on the variantand sequence based information.

Mentions: The system architecture of the prediction system is given in Figure 1. In thissystem, a database search and a prediction model are incorporated. Two formsof input (dbSNP id and amino acid sequence) are acceptable for the predictionsystem. For dbSNP id inputs, database search is carried out. For an amino acidsequence input (in fasta / raw format), after removing the invalid characters,calculate the parameters. These values are given as the input of the ANNpredictor. The prediction model is deployed into a web server, which findsutility of nsSNP identification and it may leads to tumor studies for scientificcommunity. The database search and a prediction models are incorporated inthe web server. The interface of the deleterious nsSNP web server is shown inFigure 2.


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

Chandra V, Ramakrishnan R, Ramanathan S - Bioinformation (2011)

Organizational chart of deleterious nsSNP prediction system. Theinput to the method is in two forms: dbSNP id and amino acid sequence. If thesequence is dbSNP id, search the database for similar entries. The outputcontains general information of the gene and nsSNP, information on the variantand sequence based information.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3064852&req=5

Figure 1: Organizational chart of deleterious nsSNP prediction system. Theinput to the method is in two forms: dbSNP id and amino acid sequence. If thesequence is dbSNP id, search the database for similar entries. The outputcontains general information of the gene and nsSNP, information on the variantand sequence based information.
Mentions: The system architecture of the prediction system is given in Figure 1. In thissystem, a database search and a prediction model are incorporated. Two formsof input (dbSNP id and amino acid sequence) are acceptable for the predictionsystem. For dbSNP id inputs, database search is carried out. For an amino acidsequence input (in fasta / raw format), after removing the invalid characters,calculate the parameters. These values are given as the input of the ANNpredictor. The prediction model is deployed into a web server, which findsutility of nsSNP identification and it may leads to tumor studies for scientificcommunity. The database search and a prediction models are incorporated inthe web server. The interface of the deleterious nsSNP web server is shown inFigure 2.

Bottom Line: Human genetic variations primarily result from single nucleotide polymorphisms (SNPs) that occurs approximately every 1000 bases in the overall human population.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.

View Article: PubMed Central - PubMed

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.

No MeSH data available.


Related in: MedlinePlus