KGG:A systematic biological Knowledge-based mining system for Genome-wide Genetic studies

Links of MX Li's tools:

KGG (Knowledge-based mining system for Genome-wide Genetic studies) is a software tool to perform knowledge-based analysis for genome-wide association studies(GWAS). At present, it has three major functions, 1) prioritizing SNPs through a knowledge-based weighting method; 2) efficiently conducting powerful gene-based association tests using SNP p-values from GWAS; 3) advanced biological module-level analysis (pathway and PPI network enrichment) of significant genes suggested by the analyses of 1) or 2).
New analysis procedure in the KGG2: Please read the user-manual of KGG2 for details.
Currently eight classes of biological resources are involved in the knowledge-based analysis on KGG: 1) SNPs’ gene features (e.g. intron, missense, splicing site, etc.) from dbSNP 2) Conservation scores from the UCSC Genome Browser website 3) Positive selection scores of SNPs from Voight et al (2006) 4) Human microRNA target gene binding site information from Sanger’s miRBase () 5) Disease genes from OMIM 6) Tissue specific-expression genes from an analyzed dataset of mRNA expression arrays by Greco et al (2008) 7) Biological pathways genes from KEGG and BioCarta 8) Protein-protein interaction information from STRING (V8.3) with confidence score >=0.7

Comments and suggestions are welcome, please e-mail *limx54@yahoo.com*

Reference:
Miao-Xin Li, Hong-Sheng Gui, Johnny SH Kwan and Pak C Sham. GATES: A rapid and powerful gene-based association test using extended Simes procedure. Am J Hum Genet. 2011 Mar 11;88(3):283-293. PubMed AJHG
Li MX, Sham PC, Cherny SS, Song YQ.(2010) A knowledge-based weighting framework to boost the power of genome-wide association studies. PLoS One Dec 31;5(12):e14480. PubMed Plos One