Software

EGAD: ultra-fast functional analysis of gene networks

Summary: Evaluating gene networks with respect to known biology is a common task but often a computationally costly one. Many computational experiments are difficult to apply exhaustively in network analysis due to run-times. To permit …

Using predictive specificity to determine when gene set analysis is biologically meaningful

Gene set analysis, which translates gene lists into enriched functions, is among the most common bioinformatic methods. Yet few would advocate taking the results at face value. Not only is there no agreement on the algorithms themselves, there is no …

Measuring the wisdom of the crowds in network-based gene function inference

MOTIVATION: Network-based gene function inference methods have proliferated in recent years, but measurable progress remains elusive. We wished to better explore performance trends by controlling data and algorithm implementation, with a particular …

Novel therapeutics for coronary artery disease from genome-wide association study data

BACKGROUND: Coronary artery disease (CAD), one of the leading causes of death globally, is influenced by both environmental and genetic risk factors. Gene-centric genome-wide association studies (GWAS) involving cases and controls have been …

Analysis of genome-wide association study data using the protein knowledge base

BACKGROUND: Genome-wide association studies (GWAS) aim to identify causal variants and genes for complex disease by independently testing a large number of SNP markers for disease association. Although genes have been implicated in these studies, few …

Web tools for the prioritization of candidate disease genes

Despite increasing sequencing capacity, genetic disease investigation still frequently results in the identification of loci containing multiple candidate disease genes that need to be tested for involvement in the disease. This process can be …