Knowledge Enrichment Analysis for Human Tissue- Specific Genes Uncover New Biological Insights

The expression and regulation of genes in different tissues are fundamental questions to be answered in biology. Knowledge enrichment analysis for tissue specific (TS) and housekeeping (HK) genes may help identify their roles in biological process or diseases and gain new biological insights.In this paper, we performed the knowledge enrichment analysis for 17,343 genes in 84 human tissues using Gene Set Enrichment Analysis (GSEA) and Hypergeometric Analysis (HA) against three biological ontologies: Gene Ontology (GO), KEGG pathways and Disease Ontology (DO) respectively.The analyses results demonstrated that the functions of most gene groups are consistent with their tissue origins. Meanwhile three interesting new associations for HK genes and the skeletal muscle tissuegenes are found. Firstly, Hypergeometric analysis against KEGG database for HK genes disclosed that three disease terms (Parkinson’s disease, Huntington’s disease, Alzheimer’s disease) are intensively enriched.Secondly, Hypergeometric analysis against the KEGG database for Skeletal Muscle tissue genes shows that two cardiac diseases of “Hypertrophic cardiomyopathy (HCM)” and “Arrhythmogenic right ventricular cardiomyopathy (ARVC)” are heavily enriched, which are also considered as no relationship with skeletal functions.Thirdly, “Prostate cancer” is intensively enriched in Hypergeometric analysis against the disease ontology (DO) for the Skeletal Muscle tissue genes, which is a much unexpected phenomenon

Knowledge representation for data integration and exploration in translational medicine

Tese de doutoramento, Informática (Bioinformática), Universidade de Lisboa, Faculdade de Ciências, 2014Biomedical research has evolved into a data-intensive science, where prodigious amounts of data can be collected from disparate resources at any time. However, the value of data can only be leveraged through its analysis, which ultimately results in the acquisition of knowledge. In domains such as translational medicine, data integration and interoperability are key requirements for an efficient data analysis. The semantic web and its technologies have been proposed as a solution for the problems of data integration and interoperability. One of the tools of the semantic web is the representation of domain knowledge with ontologies, which provide a formal description of that knowledge in a structured manner. The thesis underlying this work is that the representation of domain knowledge in ontologies can be exploited to improve the current knowledge about a disease, as well as improve the diagnosis and prognosis processes. The following two objectives were defined to validate this thesis: 1) to create a semantic model that represents and integrates the heterogeneous sources of data necessary for the characterization of a disease and of its prognosis process, exploiting semantic web technologies and existing ontologies; 2) to develop a methodology that exploits the knowledge represented in existing ontologies to improve the results of knowledge exploration methods obtained with translational medicine datasets. The first objective was accomplished and resulting in the following contributions: the methodology for the creation of a semantic model in the OWL language; a semantic model of the disease hypertrophic cardiomyopathy; and a review on the exploitation of semantic web resources in translation medicine systems. In the case of the second objective, also accomplished, the contributions are the adaptation of a standard enrichment analysis to use data from patients; and the application of the adapted enrichment analysis to improve the predictions made with a translational medicine dataset.Fundação para a Ciência e a Tecnologia (FCT, SFRH/BD/65257/2009