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Computational Method Puts Finer Point on Multispecies Genomic Comparisons

By Carnegie Mellon ­niversity

July 5, 2018

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A computational model developed by Carnegie Mellon University (CMU) researchers enables more detailed comparative analysis of genome function across multiple species, which could offer insights into areas such as evolution and human disease.

The team, led by CMU's Jian Ma, includes participants from the University of Virginia, Florida State University, and the University of Connecticut.

The team used its Phylogenetic Hidden Markov Gaussian Processes (Phylo-HMGP) model to analyze a new dataset for DNA replication timing across five primate species, including human.

Phylo-HMGP addresses the "Starbucks problem" in multispecies analyses, says Ma, referring to analysis tools that characterize functional genomic data as low, medium, or high.

The Phylo-HMGP model allows researchers to look at each functional genomic value as a continuous signal that shows actual activity, rather than just a rough estimate, says CMU's Yang Yang.

The team proved Phylo-HMGP could be used to discover genomic regions with distinct evolutionary patterns of replication timing, says Ma.

From Carnegie Mellon University
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