Applications of computational algorithm tools to identify functional SNPs

Doss, C. George Priya; Sudandiradoss, C.; Rajasekaran, R.; Choudhury, Parikshit; Sinha, Priyanka; Hota, Pragnya; Batra, Udit Prakash; Sethumadhavan, Rao

doi:10.1007/s10142-008-0086-7

Cited by 45 publications

(20 citation statements)

References 26 publications

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“…For the p53 gene, TP53 located on chromosome 17p13.1, three tagSNPs were selected by reducing the MAF with 5% increments until three tagSNPs could be selected with a MAF higher than 5%. Two potentially functional SNPs were added (rs16956880 and rs11575997) that could have an effect on splicing [22].…”

Section: Methodsmentioning

confidence: 99%

Association between Variations in Cell Cycle Genes and Idiopathic Pulmonary Fibrosis

et al. 2012

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Idiopathic pulmonary fibrosis (IPF) is a devastating and progressive lung disease. Its aetiology is thought to involve damage to the epithelium and abnormal repair. Alveolar epithelial cells near areas of remodelling show an increased expression of proapoptotic molecules. Therefore, we investigated the role of genes involved in cell cycle control in IPF. Genotypes for five single nucleotide polymorphisms (SNPs) in the tumour protein 53 (TP53) gene and four SNPs in cyclin-dependent kinase inhibitor 1A (CDKN1A), the gene encoding p21, were determined in 77 IPF patients and 353 controls. In peripheral blood mononuclear cells (PBMC) from 16 healthy controls mRNA expression of TP53 and CDKN1A was determined.Rs12951053 and rs12602273, in TP53, were significantly associated with survival in IPF patients. Carriers of a minor allele had a 4-year survival of 22% versus 57% in the non-carrier group (p = 0.006). Rs2395655 and rs733590, in CDKN1A, were associated with an increased risk of developing IPF. In addition, the rs2395655 G allele correlated with progression of the disease as it increased the risk of a rapid decline in lung function. Functional experiments showed that rs733590 correlated significantly with CDKN1A mRNA expression levels in healthy controls.This is the first study to show that genetic variations in the cell cycle genes encoding p53 and p21 are associated with IPF disease development and progression. These findings support the idea that cell cycle control plays a role in the pathology of IPF. Variations in TP53 and CDKN1A can impair the response to cell damage and increase the loss of alveolar epithelial cells.

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Section: Methodsmentioning

confidence: 99%

Association between Variations in Cell Cycle Genes and Idiopathic Pulmonary Fibrosis

et al. 2012

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“…It is often time consuming and laborious to study the molecular basis of diseases like cancers, especially in cases where the number of mutations is very high. By contrast, detailed and useful information regarding the effect of nsSNP on protein structure and function can be readily obtained by in silico methods [2, 4]. Prioritizing the most interesting and likely pathogenic cases for further experimental analysis is another important application of the tested prediction methods.…”

Section: Discussionmentioning

confidence: 99%

“…In attaining this milestone, profiling the most common single nucleotide polymorphisms (SNPs) by computational approach became powerful and inexpensive enough to jumpstart the personalized genomics area [1–4]. SNPs are not only considered as markers in constructing genetic maps but also have the potential as direct functional polymorphic variants involved within complex diseases, as well as drug response.…”

Section: Introductionmentioning

confidence: 99%

IntegratingIn SilicoPrediction Methods, Molecular Docking, and Molecular Dynamics Simulation to Predict the Impact of ALK Missense Mutations in Structural Perspective

Doss

Chakraborty

Chen

et al. 2014

BioMed Research International

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Over the past decade, advancements in next generation sequencing technology have placed personalized genomic medicine upon horizon. Understanding the likelihood of disease causing mutations in complex diseases as pathogenic or neutral remains as a major task and even impossible in the structural context because of its time consuming and expensive experiments. Among the various diseases causing mutations, single nucleotide polymorphisms (SNPs) play a vital role in defining individual's susceptibility to disease and drug response. Understanding the genotype-phenotype relationship through SNPs is the first and most important step in drug research and development. Detailed understanding of the effect of SNPs on patient drug response is a key factor in the establishment of personalized medicine. In this paper, we represent a computational pipeline in anaplastic lymphoma kinase (ALK) for SNP-centred study by the application of in silico prediction methods, molecular docking, and molecular dynamics simulation approaches. Combination of computational methods provides a way in understanding the impact of deleterious mutations in altering the protein drug targets and eventually leading to variable patient's drug response. We hope this rapid and cost effective pipeline will also serve as a bridge to connect the clinicians and in silico resources in tailoring treatments to the patients' specific genotype.

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“…It was shown that conservation degree is in fact the most reliable method for predicting possible pathogenicity of a missense variant (Flanagan et al, 2010). For the purpose of both prioritization and functional analysis optimization, we recommend combining available annotation tools that employ a variety of prioritization features (George et al, 2008;Lee and Shatkay, 2008). A recent study implemented some of these variation classification methods on recorded SNPs in a target gene, in order to elucidate possible cancer causing mutations, reducing the initial number of suspected SNPs from thousands to less than 30 (Choura and Rebai, 2009).…”

Section: Variant Classificationmentioning

confidence: 99%