Computational Biology Seminar

  • Gates Hillman Centers
  • ASA Conference Room 6115
  • Professor and Chair
  • Computer Science Department
  • Rice University

Statistical Inference of Reticulate Evolutionary Histories Using Data from Unlinked Loci

Using genome-wide data for phylogenetic inference and analysis has become commonplace in the post-genomic era, giving rise to the field of phylogenomics. The multispecies coalescent (MSC) model has emerged as the main stochastic process that helps capture the intricate relationship between species trees and gene trees.  Combined with models of sequence evolution, the MSC can be viewed as a generative model of genomic sequence data in the context of a (species) phylogenetic tree.

A significant outcome of the use of genome-wide data has been the increasing evidence, or hypotheses, of reticulation (e.g., hybridization) during the evolution of various groups of eukaryotic species. Reticulate evolutionary histories are best represented as phylogenetic networks, which extend the tree model to allow for admixtures of genetic material. In this talk, I will describe the multispecies network coalescent (MSNC) model, which extends the MSC model so that it operates within the branches of a phylogenetic network. This extended model naturally allows for modeling vertical and horizontal evolutionary processes acting within and across species boundaries. In particular, it simultaneously accounts for gene tree incongruence across loci due to both hybridization and incomplete lineage sorting. I will then describe a likelihood function for this model, as well as a method for Bayesian sampling of phylogenetic networks and their parameters using reversible-jump Markov chain Monte Carlo (RJMCMC). All the methods I describe have been implemented in our open-source software package, PhyloNet, which is publicly available at

Luay Nakhleh is Professor and Chair of the Computer Science Department at Rice University. He received the B.Sc. degree in Computer Science from the Technion (Israel), the Master’s degree in Computer Science from Texas A&M University, and the Ph.D. degree in Computer Science from The University of Texas in Austin. He conducts research in the areas of bioinformatics and computational biology, focusing mainly on questions in evolutionary biology. Luay is a recipient of the DoE CAREER award, the NSF CAREER award, the Sloan Fellowship, and the Guggenheim Fellowship.

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