Genetic and epigenetic mechanisms play vital roles in the initiation and progression of cancer. The motivation of the work reported here is thus to support research in this area, by investigating genetic and epigenetic mechanisms and the inter-relationships between them through provision of a platform (in-house biomedical resource (StatEpigen)) for data collation and analysis. StatEpigen is targeted initially to collating information on colon cancer and the basic aim of this project is to enhance, evaluate and ensure robustness of this resource. Elements involved in building towards a more comprehensive 'picture of needs' to date include: a comparative study of available epigenetic/epigenomic biomedical resources, manual augmentation of StatEpigen database resource and an in-depth analysis of a set of germline-mutated colon cancer genes from the phylogenetic perspective, to link resource provision to the experimental base and address key bioinformatics questions. Comparative study has confirmed the current importance of epigenetic studies and provided information on resources that may offer integration potential for StatEpigen. Manual data augmentatrion (15% contribution to the current datasource, URL: http://statepigen.sci-sym.dcu.ie/) permitted assessment of the data curation process itself, and also motivation and planning for some degree of future automation. The in-depth genetic analysis addresses a specific-research question relating to the suitability ofthe murine model as a reference organism for colon cancer in humans. Analysis of the mouse parallel (following 180MY of independent evolution) revealed that some genes can not be used as suitable cancer model for humans. This finding provided stimulus for developed analyses (e.g. through StatEpigen) of related epigenetic characteristics and genetic-epigenetic interactions that are influential in the initiation and progression of the disease. A future focus for StatEpigen includes exploitation ofthe data already gathered, as well as tool development for automation of the data augmentation process.