Computational method puts finer point on multispecies genomic comparisons | 6/20/2018 | Staff
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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 today 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.

Research - Team - Scientists - University - Virginia

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.

Genetic differences in protein-coding genes alone cannot account for the dramatic variation between species, so scientists increasingly focus on differences in gene regulation—mechanisms that control how and to what degree genes are activated.

Differences - Species - Regions - Genome - Elements

"The differences among primate species may be mostly in the noncoding regions of the genome, the regulatory elements, not the genes themselves," Ma explained. High-throughput technologies produce a large amount of functional genomic data, which should help scientists better understand how genomes evolved.

Ma said Phylo-HMGP addresses what might be called the "Starbucks problem" in these multi-species analyses. Just as coffee...
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