NetAn: A Python Toolbox Leveraging Network Topology for Comprehensive Gene Annotation Enrichments

Abstract

BackgroundGene annotation enrichment analysis is the gold standard for studying the biological context of a set of genes, but available tools often overlook important network properties of the underlying gene regulatory system. ResultsWe present the NETwork ANnotation enrichment package, NetAn, built in Python, which augments annotation analysis with approaches such as inference of closely related genes to include local neighbors in the analysis, the extraction of separate network sub-clusters, and the following over-representation analyses based on network clustering. By using NetAn, we demonstrate how these approaches enhance the identification of relevant annotations in human gene sets. In a specific case study on Multiple Sclerosis (MS), NetAns approach of incorporating neighboring genes through network-based expansion demonstrates a distinct advantage in identifying immune-related genes critical to MS pathology. Furthermore, we demonstrate the ability of NetAn to stratify MS annotations to also identify relevant neuron-related enrichments. Lastly, we compare NetAn to alternative network-based approaches, and find it to have greater specificity compared to broader approaches like NET-GE. ConclusionsWe present NetAn, a novel network-based approach that can stratify annotation enrichment analyses by integrating gene interactions and network topology, thereby strengthening biological signals through the inclusion of associated genes. This approach allows for enhanced identification of disease-relevant annotations, as demonstrated in the MS case study.