Using Ontology Fingerprint to enhance analysis of high throughput experimental results

High-throughput experiments are employed more and more in the investigation of the pathology, biological mechanism, or genetic epidemiology of different diseases and traits (Ozaki and Tanaka 2005; Smyth et al. 2006; Willer et al. 2008; Cookson et al. 2009; Rhodes et al. 2004). Analyses of the results from these experiments yield candidate genes that can provide biological insights into disease mechanisms and potentially act as biomarkers with significant clinical impact. However, making inferences from these studies proves difficult due to the large number of statistical tests that must be performed and the complex nature of most common diseases and disease-related traits (Hunter and Kraft 2007; Kane et al. 2000; Thomas, Haile, and Duggan 2005; Yauk et al. 2004). Many current approaches use "pathway analysis'' to address the challenges. But relying on functional annotations has shortcomings because of the limited annotations for most of the human genes and genomes. To ameliorate this difficulty, we introduced Ontology Fingerprinting (OntoFing), a bioinformatics approach that characterizes genes and other biological concepts (phenotypes, pathways, diseases, etc.) through the identification of a set of Gene Ontology (GO) terms, standardised controlled vocabularies to represent biological attributes for genes/gene products, being overrepresented among PubMed abstracts annotated with the gene or biological concept in question. We used OntoFing as a tool to evaluate the results of high throughput experiments and provided literature-supported evidence to assist researchers in making biological inferences. The objectives of this study were the following: (1) to employ ontology and biomedical literature to develop the ontology fingerprinting (OntoFing) approach for quantifying biological relevance between different biological concepts; (2) to develop an OntoFing-derived gene-gene network to identify functional gene nodules: (3) to apply the OntoFing approach to GWA results to inter potential polygenic effects; and (4) to apply the OntoFing approach to the results of a proposed meta-analysis method of microarray data to make biological inference.;The OntoFing approach is a novel method that combines gene-phenotype and gene-gene relationships to provide researchers additional biological knowledge derived from ontological information and literature data. By using candidate genes from the results of high-throughput experiments, we also illustrate that OntoFing can use the network approach to identify functional gene modules relevant to the phenotype being studied.