Understanding the Systems Biology of Pathogen Virulence Using Semantic Methodologies

TL;DR

This paper presents 'toxoMine', a semantic-based integrative platform using the Sequence Ontology for systems biology analysis of Toxoplasma gondii virulence, enabling comprehensive multi-omics data integration and analysis.

Contribution

The paper introduces 'toxoMine', a novel semantic data warehouse platform that facilitates integrative systems biology analysis of pathogen virulence using ontological representations.

Findings

Successfully implemented 'toxoMine' for Toxoplasma gondii analysis

Enabled multi-omics data integration and analysis

Provided a foundation for further semantic knowledge discovery

Abstract

Systems biology approaches to the integrative study of cells, organs and organisms offer the best means of understanding in a holistic manner the diversity of molecular assays that can be now be implemented in a high throughput manner. Such assays can sample the genome, epigenome, proteome, metabolome and microbiome contemporaneously, allowing us for the first time to perform a complete analysis of physiological activity. The central problem remains empowering the scientific community to actually implement such an integration, across seemingly diverse data types and measurements. One promising solution is to apply semantic techniques on a self-consistent and implicitly correct ontological representation of these data types. In this paper we describe how we have applied one such solution, based around the InterMine data warehouse platform which uses as its basis the Sequence Ontology, to facilitate a systems biology analysis of virulence in the apicomplexan pathogen $Toxoplasma~gondii$, a common parasite that infects up to half the worlds population, with acute pathogenic risks for immuno-compromised individuals or pregnant mothers. Our solution, which we named `toxoMine', has provided both a platform for our collaborators to perform such integrative analyses and also opportunities for such cyberinfrastructure to be further developed, particularly to take advantage of possible semantic similarities of value to knowledge discovery in the Omics enterprise. We discuss these opportunities in the context of further enhancing the capabilities of this powerful integrative platform.