Tandy Warnow The University of Illinois New Scalable Coalescent-Based Species Tree Estimation Methods: BBCA, ASTRAL, and ASTRID Tandy Warnow The University of Illinois

BBCA, ASTRAL, and ASTRID BBCA is a simple way of making *BEAST scalable to large numbers of genes (but doesn’t address large numbers of species) ASTRAL and ASTRID: summary methods that are statistically consistent in the presence of ILS, and that run in polynomial time. Both can analyze very large datasets (1000 species and 1000 genes – or more) with high accuracy. The relative accuracy depends on the model condition – sometimes ASTRAL is better, sometimes ASTRID is better.

Main competing approaches gene 1 gene 2 . . . gene k . . . Species Concatenation . . . Analyze separately point out that supertree methods take overlaping trees and produce a tree, and that the whole process of first generating small trees and then applying a supertree method is often referred to as the “supertree approach”. Summary Method

Incomplete Lineage Sorting (ILS) is a dominant cause of gene tree heterogeneity

*BEAST Heled and Drummond, MBE 2010 Input: set of multiple sequence alignments for collection of genes Techique: Uses MCMC to co-estimate gene trees and species trees Highly accurate Limited in practice to small numbers of genes and species, due to convergence issues

BBCA: improving *BEAST Zimmermann, Mirarab, and Warnow, BMC Genomics 2014: Randomly partition genes into bins of at most 25 genes Apply *BEAST to each bin, and take the gene trees it computes Apply favored summary method to the gene trees Matches accuracy of *BEAST Improves scalability to large # genes

ASTRAL Mirarab and Warnow, Bioinformatics 2014 https://github.com/smirarab/ASTRAL Tutorial in Species Tree Workshop

ASTRID ASTRID: Accurate species trees using internode distances, Vachaspati and Warnow, RECOMB-CG 2015 and BMC Genomics 2015 Algorithmic design: Computes a matrix of average leaf-to-leaf topological distances, and then computes a tree using FastME (more accurate than neighbor Joining and faster, too). Related to NJst (Liu and Yu, 2010), which computes the same matrix but then computes the tree using neighbor joining (NJ). Statistically consistent under the MSC O(kn2 + n3) time where there are k gene trees and n species

BBCA, ASTRAL, and ASTRID BBCA is a simple way of making *BEAST scalable to large numbers of genes (but doesn’t address large numbers of species) ASTRAL and ASTRID: summary methods that are statistically consistent in the presence of ILS, and that run in polynomial time. Both can analyze very large datasets (1000 species and 1000 genes – or more) with high accuracy. The relative accuracy depends on the model condition – sometimes ASTRAL is better, sometimes ASTRID is better.

Acknowledgments Software ASTRAL: Available at https://github.com/smirarab ASTRID: Available at https://github.com/pranjalv123 Others at http://tandy.cs.illinois.edu/software.html NSF grant DBI-1461364 (joint with Noah Rosenberg at Stanford and Luay Nakhleh at Rice): http://tandy.cs.illinois.edu/PhylogenomicsProject.html NSF graduate fellowship to Pranjal Vachaspati HHMI graduate fellowship to Siavash Mirarab Papers available at http://tandy.cs.illinois.edu/papers.html