Literature DB >> 21803170

In silico SNP analysis and bioinformatics tools: a review of the state of the art to aid drug discovery.

James T L Mah1, Esther S H Low, Edmund Lee.   

Abstract

SNPs can alter protein function and phenotype, leading to altered pharmacogenomic drug profiles. The exponential number of SNPs makes it impossible to perform wet laboratory experiments to determine the biological significance of each one. However, bioinformatics tools can be used to screen for potentially deleterious SNPs that might affect important drug targets before further investigation by wet laboratory techniques. As there are numerous web-based bioinformatics tools, much time and effort is needed to select the most appropriate tool to use. Here, we review state-of-the-art bioinformatics tools to help researchers analyze and select the most promising SNPs for drug discovery in the shortest time possible.
Copyright © 2011 Elsevier Ltd. All rights reserved.

Mesh:

Year:  2011        PMID: 21803170     DOI: 10.1016/j.drudis.2011.07.005

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  12 in total

1.  MEGA-MD: molecular evolutionary genetics analysis software with mutational diagnosis of amino acid variation.

Authors:  Glen Stecher; Li Liu; Maxwell Sanderford; Daniel Peterson; Koichiro Tamura; Sudhir Kumar
Journal:  Bioinformatics       Date:  2014-01-11       Impact factor: 6.937

Review 2.  Role of Structural Bioinformatics in Drug Discovery by Computational SNP Analysis: Analyzing Variation at the Protein Level.

Authors:  David K Brown; Özlem Tastan Bishop
Journal:  Glob Heart       Date:  2017-03-13

3.  Missense variants in CFTR nucleotide-binding domains predict quantitative phenotypes associated with cystic fibrosis disease severity.

Authors:  David L Masica; Patrick R Sosnay; Karen S Raraigh; Garry R Cutting; Rachel Karchin
Journal:  Hum Mol Genet       Date:  2014-12-08       Impact factor: 6.150

4.  Leucine to proline substitution by SNP at position 197 in Caspase-9 gene expression leads to neuroblastoma: a bioinformatics analysis.

Authors:  Arpita Kundu; Susmita Bag; Sudha Ramaiah; Anand Anbarasu
Journal:  3 Biotech       Date:  2012-09-18       Impact factor: 2.406

Review 5.  Candidate gene association studies: a comprehensive guide to useful in silico tools.

Authors:  Radhika Patnala; Judith Clements; Jyotsna Batra
Journal:  BMC Genet       Date:  2013-05-09       Impact factor: 2.797

6.  Integrative visual analysis of protein sequence mutations.

Authors:  Nadezhda T Doncheva; Karsten Klein; John H Morris; Michael Wybrow; Francisco S Domingues; Mario Albrecht
Journal:  BMC Proc       Date:  2014-08-28

7.  Common sequence variants affect molecular function more than rare variants?

Authors:  Yannick Mahlich; Jonas Reeb; Maximilian Hecht; Maria Schelling; Tjaart Andries Petrus De Beer; Yana Bromberg; Burkhard Rost
Journal:  Sci Rep       Date:  2017-05-09       Impact factor: 4.379

8.  DDIG-in: discriminating between disease-associated and neutral non-frameshifting micro-indels.

Authors:  Huiying Zhao; Yuedong Yang; Hai Lin; Xinjun Zhang; Matthew Mort; David N Cooper; Yunlong Liu; Yaoqi Zhou
Journal:  Genome Biol       Date:  2013-03-13       Impact factor: 13.583

9.  Combined sequence and sequence-structure based methods for analyzing FGF23, CYP24A1 and VDR genes.

Authors:  Selvaraman Nagamani; Kh Dhanachandra Singh; Karthikeyan Muthusamy
Journal:  Meta Gene       Date:  2016-03-31

10.  Computational Analysis of nsSNPs of ADA Gene in Severe Combined Immunodeficiency Using Molecular Modeling and Dynamics Simulation.

Authors:  Soukaina Essadssi; Al Mehdi Krami; Lamiae Elkhattabi; Zouhair Elkarhat; Ghita Amalou; Houria Abdelghaffar; Hassan Rouba; Abdelhamid Barakat
Journal:  J Immunol Res       Date:  2019-11-03       Impact factor: 4.818

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