I am dealing a bioinformatics problem and need help in approaching it in the right direction.
I have a set of variables that represent different kinds of genomic data and are real valued. I am trying to compare all these different variables among two species (though in the future I might be interested in generalizations to more than 2 species). What I need to do is to develop a graph theoretical method to calculate an entropy measure (or a better evolutionary distance measure) among all different genomic interval and cluster the pairs according to a high/low entropy measure.
So in genomics in the case of multiple species/two species there would be genomic regions which have a similar DNA sequence (known as orthologus regions). These regions would have a epigenetic/epigenomic mark associated with them. Such marks generally affect the gene expression/the overall chromatin make up of the genome or chromosome. What I want to do is identify regions of orthologus DNA which have high evolutionary conservation and high epigenomic conservation. I also want to identify regions of DNA between 2 or multiple species which have high evolutionary and epigenomic conservation or low evolutionary conservation and high epigenomic conservation and vice versa. What I want to do at the end is to develop a method that would help ,make multi-species /pairwise comparison at the end. Since I am not a formally trained Computer Scientist I don't know what would be a good approach/algorithm to use.
PS: epigenomic data would generally be in the form of counts (normalized to RPKM (https://wiki.nci.nih.gov/pages/viewpage.action;jsessionid=5265E39473A073390CF868D3E56E619A?pageId=71439191). While RPKM of epigenomic signals is available for a span of regions (say 200 base pairs) the evolutionary conservation data is available for every base of DNA (the evolutionary conservation value of a base lies between 0 and 1)