Choose wisely: Network, ontology and annotation resources for the analysis of Staphylococcus aureus omics data

Abstract

Staphylococcus aureus (S. aureus) is a prominent human and livestock pathogen investigated widely using omic technologies. Critically, due to availability, low visibility or scattered resources, robust network and statistical contextualisation of the resulting data is generally under-represented. Here, we present novel meta-analyses of freely-accessible molecular network and gene ontology annotation information resources for S. aureus omics data interpretation. Furthermore, through the application of the gene ontology annotation resources we demonstrate their value and ability (or lack-there-of) to summarise and statistically interpret the emergent properties of gene expression and protein abundance changes using publically available data. This analysis provides simple metrics for network selection and demonstrates the availability and impact that gene ontology annotation selection can have on the contextualisation of bacterial omics data.

Keywords: Bioinformatics; Gene ontology; Gene ontology annotation; MRSA; Molecular network; Non-model organism; Omics; Staphylococcus aureus; Systems biology.