The University of Illinois at Urbana-Champaign
New methods for species tree estimation in the
presence of gene tree heterogeneity
Friday, January 15, 2016
Estimating the Tree of Life will likely involve a two-step procedure, where in the first step trees are estimated on many genes, and then the gene trees are combined into a tree on all the taxa. However, the true gene trees may not agree with the species tree due to biological processes such as deep coalescence, gene duplication and loss, and horizontal gene transfer. Statistically consistent methods based on the multi-species coalescent model have been developed to estimate species trees in the presence of incomplete lineage sorting; however, the relative accuracy of these methods compared to the usual “concatenation” approach is a matter of substantial debate within the research community.
I will present results showing that coalescent-based estimation methods are impacted by gene tree estimation error, so that they can be less accurate than concatenation in many cases. I will also present two new methods, ASTRAL (Mirarab et al., Bioinformatics 2014) and statistical binning (Mirarab et al., Science 2014, Bayzid et al., PLOS One 2015) for estimating species trees in the presence of gene tree conflict due to ILS. Statistical binning and weighted statistical binning are used to improve gene tree estimation, while ASTRAL is a coalescent-based method that is provably statistically consistent and that can construct very accurate large species trees. Finally, I will present theoretical results investigating whether statistically consistent accurate species tree estimation is possible when gene trees have estimation error, and discuss the controversy about statistical binning (Liu and Edwards, Science 2015, Mirarab et al. Science 2015).
See Dr. Warnow’s home page for more information on her work: http://tandy.cs.illinois.edu
Host: Jonathan Eisen