Computational tools for comparative phenomics: the role and promise of ontologies

Abstract

A major aim of the biological sciences is to gain an understanding of human physiology and disease. One important step towards such a goal is the discovery of the function of genes that will lead to a better understanding of the physiology and pathophysiology of organisms, which will ultimately lead to better diagnosis and therapy. Our increasing ability to phenotypically characterise genetic variants of model organisms coupled with systematic and hypothesis-driven mutagenesis is resulting in a wealth of information that could potentially provide insight into the functions of all genes in an organism. The challenge we are now facing is to develop computational methods that can integrate and analyse such data. The introduction of formal ontologies that make their semantics explicit and accessible to automated reasoning provides the tantalizing possibility of standardizing biomedical knowledge allowing for novel, powerful queries that bridge multiple domains, disciplines, species, and levels of granularity. We review recent computational approaches that facilitate the integration of experimental data from model organisms with clinical observations in humans. These methods foster novel cross-species analysis approaches, thereby enabling comparative phenomics and leading to the potential of translating basic discoveries from the model systems into diagnostic and therapeutic advances at the clinical level.

Figure 1

A plot of the true positive rate vs. the false positive rate for the task of identifying associations between mouse models and diseases. The set of true positive instances is taken from the MGI database while negative and unknown associations between a mouse model and a disease constitute negative instances. The area under the ROC curve (AUC) of PhenomeNet for this task is 0.868.