Analysis of Genome Wide Association Study Data
eQTL
Pathway Analysis
Cancer Mutation Cluster Identifications
Human Genome Annotations
- GenoCanyon (Server) (Web Application) A statistical framework to predict functional non-coding regions in the human genome through integrated analysis of genomic conservation measures and biochemical annotations.
- GenoSkyline (Website) Tissue-specific functional annotation through integrative analysis of Roadmap epigenomic data
- GenoSkyline Plus(Website) GenoSkyline-Plus is a comprehensive update of GenoSkyline that incorporates more annotation data into the framework and extends to 127 integrated annotation tracks covering a spectrum of human tissue and cell types.
Analysis of Genome Wide Association Study Data
- GWAS.PC (User Manual Code) GWAS.PC (GWAS Power Calculation) is an R package that does power analysis in genome wide association studies. In particular, genotyping error is considered in power calculation.
- GenoWAP (Website) Post-GWAS prioritization through integrated analysis of GWAS summary statistics and GenoCanyon genomic functional annotation
- GPA (Website) A statistical approach to prioritizing GWAS results by integrating pleiotropy information and annotation data.
- GBR (Download Example) This is an R package implementing a post-GWAS prioritization algorithm, which incorporates the rewiring information of co-expression network to prioritize GWAS signals.
- GWAS with MRF pathway (Related Paper) (Example.R fun_network.R network.csv pval.txt) This is a program to implement the Markov Random Field (MRF) method to incorporate pathway topology for genome wide association studies.
eQTL
- LORS (Download) A Low-Rank representation and Sparse regression for eQTL mapping. This algorithm accounts for confounding factors such as unobserved covariates, experimental artifacts, and unknown environmental perturbations.
Pathway Analysis
- Pathway Analysis using Random Forests (Source code and supplemental data) An R code for pathway-based classification and regression using Random Forests.
- GRAPE (Source code and sample datasets) GRAPE is a template method that allows for identification of perturbed pathways in individual tumor samples relative to a reference collection of samples (e.g., matched healthy tissue). GRAPE is sensitive to biological variability, robust to batch effects and can be applied to any gene expression platform.
- COSINE (Related Paper) (Software)
An R package to extract the globally most discriminative sub-network from multiple gene expression data sets with integration of protein-protein interactions data.
Cancer Mutation Cluster Identifications
- iPAC (More information) iPAC (Identification of Protein Amino acid mutation Clustering) finds mutation clusters on the amino acid level while taking into account the protein structure.
- GraphPAC (More information) A bioconductor R package for identifying mutational clusters of amino acids in a protein while utilizing the protein tertiary structure via a graph theoretical model.