(w)HOL(e)ISTIC gene ontology and pathway analysis of data using open source web tools

Abstract Objective Downstream analysis of next generation sequencing (NGS) experiments provides researchers a means of deciphering their results. These downstream analyses elucidate clusters of genes or networks of biological interest under the conditions being studied. One convention for examining gene interactions is to conduct downstream investigations based on gene ontology (GO), pathway, and network analyses of statistically significant genes of interest. Unfortunately, the software available for these types of analyses is expensive, not species specific, and subject to gaps in annotation. These difficulties can cause studies to omit this downstream step, limiting the utility of the data. In order to facilitate pathway and network analyses of candidate gene lists from NGS studies, a workflow was constructed based on the use of open-sourced freely available software and genomic databases termed the “(w)HOL(e)ISTIC GO enrichment” approach.Results Overlap of multiple open-source software was used to annotate, analyze, and visualize biological networks. It is a 3-stage process in which stage 1 (Annotation) is the generation of alias identifiers. Stage 2 (Analysis) is a two-part process generating ontologies and networks with statistical inferences. Stage 2 relies on information from databases such as Reactome, KEGG, and InterPro. Stage 3 (Visualization) allows for figure creation.