Pathway analysis software tools | Genome-wide association study
Genome-wide association (GWA) studies have typically focused on the analysis of single markers, which often lacks the power to uncover the relatively small effect sizes conferred by most genetic variants. Recently, pathway-based approaches have been developed, which use prior biological knowledge on gene function to facilitate more powerful analysis of GWA study data sets. These approaches typically examine whether a group of related genes in the same functional pathway are jointly associated with a trait of interest.
A free, open-source whole genome association analysis toolset, designed to perform a range of basic, large-scale analyses in a computationally efficient manner. The focus of PLINK is purely on analysis of genotype/phenotype data, so there is no support for steps prior to this (e.g. study design and planning, generating genotype or CNV calls from raw data). Through integration with gPLINK and Haploview, there is some support for the subsequent visualization, annotation and storage of results.
A user-driven tool that displays large datasets (e.g. gene expression data from Arabidopsis Affymetrix arrays) onto diagrams of metabolic pathways or other processes. MapMan was developed for use with Arabidopsis, but has already been extended for use with several other species. These tools are available as downloadable and web-based versions.
Aggregates association strength of individual markers into pre-specified biological pathways. VEGAS2 is a a versatile pathway-based approach for genome-wide association studies (GWAS) data that accounts for gene size and linkage disequilibrium between markers using simulations from the multivariate normal distribution. First, it calculates the gene-based test statistics for all genes using the VEGAS (VErsatile Gene-based Association Study) approach which accounts for the linkage disequilibrium (LD) between the single nucleotide polymorphisms (SNPs) within a gene through simulation. Second, for each of a set of pre-specified gene-sets, the relevant gene-based results are carried forward to compute a pathway-based test.
Assists users in performing large-scale analyses. Inbix is a command-line bioinformatics toolbox including software for machine learning and epistasis network analysis for high-dimensional data, such as genome-wide association study (GWAS), microarray, RNA-Seq, or expression quantitative trait loci (eQTLs). It includes Relief-based and evaporative cooling-based algorithms for feature selection to detect main effects and interaction effects for case-control and quantitative trait data.
A web app and a package for gene prediction which precisely predicts all kinds of prokaryotic genes from a single or a set of anonymous genomic sequences having a variety of lengths. MetaGeneAnnotator integrates statistical models of prophage genes, in addition to those of bacterial and archaeal genes, and also uses a self-training model from input sequences for predictions. The MGA can precisely predict genes even on short genomic sequences. Both typical and atypical genes can be sensitively and precisely detected while keeping high specificity.
Assists users for genome wide association studies. INRICH is a gene-set enrichment analysis tool that was developed for detecting enriched association signals of linkage disequilibrium (LD) independent genomic regions within biologically relevant gene sets. This method takes a set of independent, nominally associated genomic intervals and then tests for the enrichment of predefined gene-sets.
A program for testing for Gene Ontology categories over-represented on a list of significant SNPs from a GWA analysis. This method corrects for linkage disequilibrium between SNPs, variable gene size, and multiple testing of nonindependent pathways.