SNPs3D: Candidate Gene and SNP Selection for Association Studies

Overview

Journal BMC Bioinformatics

Publisher Biomed Central

Specialty Biology

Date 2006 Mar 23

PMID 16551372

Citations 236

Authors

Peng Yue

Eugene Melamud

John Moult

Affiliations

Soon will be listed here.

Abstract

Background: The relationship between disease susceptibility and genetic variation is complex, and many different types of data are relevant. We describe a web resource and database that provides and integrates as much information as possible on disease/gene relationships at the molecular level.

Description: The resource http://www.SNPs3D.org has three primary modules. One module identifies which genes are candidates for involvement in a specified disease. A second module provides information about the relationships between sets of candidate genes. The third module analyzes the likely impact of non-synonymous SNPs on protein function. Disease/candidate gene relationships and gene-gene relationships are derived from the literature using simple but effective text profiling. SNP/protein function relationships are derived by two methods, one using principles of protein structure and stability, the other based on sequence conservation. Entries for each gene include a number of links to other data, such as expression profiles, pathway context, mouse knockout information and papers. Gene-gene interactions are presented in an interactive graphical interface, providing rapid access to the underlying information, as well as convenient navigation through the network. Use of the resource is illustrated with aspects of the inflammatory response and hypertension.

Conclusion: The combination of SNP impact analysis, a knowledge based network of gene relationships and candidate genes, and access to a wide range of data and literature allow a user to quickly assimilate available information, and so develop models of gene-pathway-disease interaction.

Citing Articles

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Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors.

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