Computational Method Puts Finer Point on Multispecies Genomic Comparisons

Probabilistic model could provide insights into what makes a human a human A new computational tool will potentially help geneticists to better understand what makes a human a human, or how to differentiate species in general, by providing more detailed comparative information about genome function. In a report published online this week by the journal Cell Systems , researchers led by Jian Ma , associate professor of computational biology at Carnegie Mellon University, describe a new model for performing comparative analyses of genome function across multiple species. Such analysis may provide insights into not only evolution, but also human disease. The research team, including scientists from the University of Virginia, Florida State University and the University of Connecticut, developed the Phylogenetic Hidden Markov Gaussian Processes model, or Phylo-HMGP, to analyze functional genomic data. They used the model to analyze a new dataset for DNA replication timing across five primate species, including human. A new algorithm, Phylo-HMGP, is used to compare how replication timing - the order in which DNA segments are replicated - differs among five species of primates. The five tracks represent the different values from replication timing experiments for each species and the color bars represent different evolutionary patterns of DNA replication timing.