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
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,
significantly speed up the analysis of new types of genomic and epigenomic