ICSNPathway is a web server developed to discover candidate causal SNPs and corresponding candidate causal pathways from genome-wide association study (GWAS).
::DEVELOPER
Bioinformatics Lab, Institute of Psychology, Chinese Academy of Sciences
geneSLOPE is a method for estimating the vector of coefficients in linear model. In the first step of GWAS, SNPs are clumped according to their correlations and distances. Then, SLOPE is performed on data where each clump has one representative.
GWAS4D is designed to systematically analyze GWAS summary data and identify context-specific regulatory variants by integrating latest multidimensional functional genomics resources and our recently published algorithms. In general, the web server introduces following six major features: (1) prioritizes the regulatory variant by cepip (Li MJ et.al. Genome Biology. 2017); (2) incorporates 127 tissue/cell type-specific epigenomes data; (3) integrates and refines TF motifs from eight public resources for 1480 transcriptional regulators; (4) uniformly processes Hi-C data and calls significant interactions at 5kb resolution across 45 tissues/cell types, links variant to its target regions; (5) annotates non-coding variant with comprehensive functional annotations; (6) equips a highly interactive visualization function for variant-target interaction.
Huang D, Yi X, Zhang S, Zheng Z, Wang P, Xuan C, Sham PC, Wang J, Li MJ*.
GWAS4D: Multidimensional analysis of context-specific regulatory variant for human complex diseases and traits.
Nucleic Acids Res. 2018; gky407
Matapax is a platform for high throughput collaborative genome wide association studies (GWAS). Computational time is reduced by processing user-supplied trait data in parallel. Result analysis is facilitated by displaying annotated candidate markers in tabular format and in a genome browser.
The i-GSEA4GWAS (improved GSEA for GWAS) web server is a web-based resource for analysis of GWAS data (typically each SNP’s -log(P-value)) to identify pathways/gene sets correlated to certain traits by implementing an improved Gene Set Enrichment Analysis (i-GSEA) approach. i-GSEA4GWAS aims to establish an open platform to help further interpret the GWAS data to provide new insights in complex disease study, especially in complementation to the standard single variant/gene based analysis.
::DEVELOPER
Bioinformatics Lab, Institute of Psychology, Chinese Academy of Sciences
