University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Coalescent-based Species Tree Inference from Gene Tree Topologies Under Incomplete Lineage Sorting by Maximum Likelihood

Coalescent-based Species Tree Inference from Gene Tree Topologies Under Incomplete Lineage Sorting by Maximum Likelihood

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Phylogenetics

Incomplete lineage sorting can cause incongruence between phylogenetic history of genes (the gene tree) and that of the species (the species tree), which can complicate the inference of phylogenies. Developing robust computational inference approaches is currently of interests in studying incomplete lineage sorting. In this talk, I will present a new coalescent-based algorithm for inferring species tree with maximum likelihood. I will first describe an improved method for computing the probability of a gene tree topology for a species tree, which is much faster than an existing algorithm. Based on this method, I will present a practical algorithm that takes a set of gene tree topologies and infers species trees with maximum likelihood. In this algorithm, we search for the best species tree by starting from candidate species trees found by a parsimony method and performing local search to obtain better trees with higher likelihood. This algorithm, called {STELLS}, has been imp lemented in a program that is downloadable from the author’s web page. The simulation results show that the STELLS algorithm is more accurate than several existing methods for many datasets, especially when there is noise (in terms of topology, branch lengths and rooting) in gene trees. We also show the STELLS algorithm is efficient and can be applied to real biological datasets.

This talk is part of the Isaac Newton Institute Seminar Series series.

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