In silico characterization of functional single nucleotide polymorphisms of folate pathway genes

In silico characterization of functional single nucleotide polymorphisms of folate pathway genes Folate metabolism genes are pivotal to critical biological processes and are related to several conditions, including developmental, cognitive, and cardiovascular anomalies. A systematic catalog of genetic polymorphisms in protein coding regions, regulatory transcription factor binding sites, and miRNA binding sites associated with folate pathway genes may contribute to personalized medicine. We performed a comprehensive computational survey of single nucleotide polymorphisms (SNPs) of folate pathway genes to highlight functional polymorphisms in the coding region, transcription factor binding sites, and miRNAs binding sites. Folate pathway genes were searched through PubMed and Kyoto Encyclopedia of Genes and Genomes pathway databases. SNPs were identified and characterized using the University of California, Santa Cruz genome browser and SNPnexus tool. Functional characterization of nonsynonymous SNPs (nsSNPS) was performed using bioinformatics tools, and common deleterious nsSNPs were identified. We identified 48 genes of folate pathway containing 287 SNPs in the coding regions. Out of these SNPs, rs5742905, rs45511401, and rs1801133 were predicted to be deleterious through four different bioinformatics tools. Three‐dimensional structures of two proteins with and without deleterious nsSNPs were predicted by SWISSPDB viewer and SuperPose. Besides, a total of 237 SNPs was identified in transcription factor binding sites using the Genomatix software suite and six miRNA target site SNPs using miRNASNP. This systematic and extensive in silico analysis of functional SNPs of folate pathway may provide a foundation for future targeted mechanistic, structure‐function, and genetic epidemiological studies. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Human Genetics Wiley

In silico characterization of functional single nucleotide polymorphisms of folate pathway genes

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Publisher
Wiley
Copyright
Copyright © 2018 John Wiley & Sons Ltd/University College London
ISSN
0003-4800
eISSN
1469-1809
D.O.I.
10.1111/ahg.12231
Publisher site
See Article on Publisher Site

Abstract

Folate metabolism genes are pivotal to critical biological processes and are related to several conditions, including developmental, cognitive, and cardiovascular anomalies. A systematic catalog of genetic polymorphisms in protein coding regions, regulatory transcription factor binding sites, and miRNA binding sites associated with folate pathway genes may contribute to personalized medicine. We performed a comprehensive computational survey of single nucleotide polymorphisms (SNPs) of folate pathway genes to highlight functional polymorphisms in the coding region, transcription factor binding sites, and miRNAs binding sites. Folate pathway genes were searched through PubMed and Kyoto Encyclopedia of Genes and Genomes pathway databases. SNPs were identified and characterized using the University of California, Santa Cruz genome browser and SNPnexus tool. Functional characterization of nonsynonymous SNPs (nsSNPS) was performed using bioinformatics tools, and common deleterious nsSNPs were identified. We identified 48 genes of folate pathway containing 287 SNPs in the coding regions. Out of these SNPs, rs5742905, rs45511401, and rs1801133 were predicted to be deleterious through four different bioinformatics tools. Three‐dimensional structures of two proteins with and without deleterious nsSNPs were predicted by SWISSPDB viewer and SuperPose. Besides, a total of 237 SNPs was identified in transcription factor binding sites using the Genomatix software suite and six miRNA target site SNPs using miRNASNP. This systematic and extensive in silico analysis of functional SNPs of folate pathway may provide a foundation for future targeted mechanistic, structure‐function, and genetic epidemiological studies.

Journal

Annals of Human GeneticsWiley

Published: Jan 1, 2018

Keywords: ; ; ; ;

References

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