Epigenetics has become a cornerstone of cancer research and is an increasingly important factor in the continuous efforts to try and unravel the biology of oncogenesis. Consequently the analyses of epigenetic data have evolved towards genome wide and high-throughput approaches, generating large data sets for which computational data mining is indispensable. Bioinformatics has proven to be useful and beneficial for a plethora of tasks, going beyond elemental data management, and is now crucial for adequate candidate gene selection, data integration, comparison and correlation as well as providing insights into cancer biology. Computational approaches are used even in routine tasks like primer design, since multiple layers of information can be incorporated into a more efficient and consistent strategy. Almost every analysis feeds back information into both the biology and the tools we use during these experiments. As the cancer epigenetics field evolves rapidly, the combination with bioinformatics will create a synergy that increases our insights into cancer biology rapidly. This review summarizes some of the frequently used bioinformatics tools in large-scale or nextgeneration analyses in epigenetics that would not have been possible without the use of well-conceived computational strategies.