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Abstract

Five years ago, the human genome sequence was published, an important milestone towards understanding human biology. However, basic cell processes cannot be explained by the genome sequence alone. Instead, further layers of control such as the epigenome will be important for significant advances towards better understanding of normal and disease-related phenotypes. A new research field in computational biology is currently emerging that is concerned with the analysis of functional information beyond the human genome sequence. Our goal is to provide biologists with means to navigate the large amounts of epigenetic data and with tools to screen these data for biologically interesting associations. We developed a statistical learning methodology that facilitates mapping of epigenetic data against the human genome, identifies areas of over- and underrepresentation, and finds significant correlations with DNA-related attributes. We implemented this methodology in a software toolkit called EpiGRAPHregression. EpiGRAPHregression is a prototype of a genome analysis tool that enables the user to analyze relationships between many attributes, and it provides a quick test whether a newly analyzed attribute can be efficiently predicted from already known attributes. Thereby, EpiGRAPHregression may significantly speed up the analysis of new types of genomic and epigenomic data.