Computational Biology - Publications

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Browsing Computational Biology - Publications by Author "Acharya, Vishal"
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    A low prevalence of MYH7/MYBPC3 mutations among Familial Hypertrophic Cardiomyopathy patients in India
    ( 2012-01-01) Bashyam, Murali D. ; Purushotham, Guroji ; Chaudhary, Ajay K. ; Rao, Katika Madhumohan ; Acharya, Vishal ; Mohammad, Tabrez A. ; Nagarajaram, Hampapathalu A. ; Hariram, Vuppaladadhiam ; Narasimhan, Calambur
    Familial Hypertrophic Cardiomyopathy (FHC) is an autosomal dominant disorder affecting the cardiac muscle and exhibits varied clinical symptoms because of genetic heterogeneity. Several disease causing genes have been identified and most code for sarcomere proteins. In the current study, we have carried out clinical and molecular analysis of FHC patients from India. FHC was detected using echocardiography and by analysis of clinical symptoms and family history. Disease causing mutations in the β-cardiac myosin heavy chain (MYH7) and Myosin binding protein C3 (MYBPC3) genes were identified using Polymerase Chain Reaction-Deoxyribose Nucleic Acid (PCR-DNA) sequencing. Of the 55 patient samples screened, mutations were detected in only nineteen in the two genes; MYBPC3 mutations were identified in 12 patients while MYH7 mutations were identified in five, two patients exhibited double heterozygosity. All four MYH7 mutations were missense mutations, whereas only 3/9 MYPBC3 mutations were missense mutations. Four novel mutations in MYBPC3 viz. c.456delC, c.2128G > A (p.E710K), c.3641G > A (p.W1214X), and c.3656T > C (p.L1219P) and one in MYH7 viz. c.965C > T (p.S322F) were identified. A majority of missense mutations affected conserved amino acid residues and were predicted to alter the structure of the corresponding mutant proteins. The study has revealed a greater frequency of occurrence of MYBPC3 mutations when compared to MYH7 mutations. © 2011 Springer Science+Business Media, LLC.
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    Hansa: An automated method for discriminating disease and neutral human nsSNPs
    ( 2012-02-01) Acharya, Vishal ; Nagarajaram, Hampapathalu A.
    Variations are mostly due to nonsynonymous single nucleotide polymorphisms (nsSNPs), some of which are associated with certain diseases. Phenotypic effects of a large number of nsSNPs have not been characterized. Although several methods have been developed to predict the effects of nsSNPs as "disease" or "neutral," there is still a need for development of methods with improved prediction accuracies. We, therefore, developed a support vector machine (SVM) based method named Hansa which uses a novel set of discriminatory features to classify nsSNPs into disease (pathogenic) and benign (neutral) types. Validation studies on a benchmark dataset and further on an independent dataset of wellcharacterized known disease and neutral mutations show that Hansa outperforms the other known methods. For example, fivefold cross-validation studies using the benchmark HumVar dataset reveal that at the false positive rate (FPR) of 20% Hansa yields a true positive rate (TPR) of 82% that is about 10% higher than the best-known method. © 2011 Wiley Periodicals, Inc.
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    Response to: Statistical Analysis of Missense Mutation Classifiers
    ( 2013-02-01) Acharya, Vishal ; Nagarajaram, Hampapathalu A.