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Network Modeling Analysis in Health Informatics and Bioinformatics

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01.12.2014 | Original Article

In silico studies of deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) of NRL gene

verfasst von: G. Divanshu, M. Lekshmi, V. Shanthi

Erschienen in: Network Modeling Analysis in Health Informatics and Bioinformatics | Ausgabe 1/2014

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Abstract

Single Nuclear Polymorphisms (SNPs) are the majority of genetic variations occurring in a genome and help in understanding the genetics behind many complex diseases. By knowing the function of SNPs, the human phenotypic variations can be well known. Identification of functional SNPs in a disease-related gene is still a challenge. In this work, using computational methods, we have analyzed the genetic variations which can affect the expression and functionality of NRL gene leading to many diseases. Out of 376 SNPs, 21 SNPs were filtered as non-synonymous missense mutations related with humans in the particular gene. The tools used were SIFT, Polyphen-2, I-Mutant 2.0, SNPs&GO, PHD-SNP. Among these 21, 5 SNPs were predicted to be deleterious by all the tools. 5 mutations out of 21 were predicted deleterious by all servers with amino acid changes D15A, R66W, F21S, L160P and L194P while S50T is predicted tolerated by SIFT. L160P and S50T have been reported in some cases of retinitis pigmentosa.

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Titel: In silico studies of deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) of NRL gene
verfasst von: G. Divanshu
M. Lekshmi
V. Shanthi
Publikationsdatum: 01.12.2014
Verlag: Springer Vienna
Erschienen in: Network Modeling Analysis in Health Informatics and Bioinformatics / Ausgabe 1/2014
Print ISSN: 2192-6662
Elektronische ISSN: 2192-6670
DOI: https://doi.org/10.1007/s13721-014-0059-9

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