New methods for estimating species trees from genome-scale data Tandy Warnow The University of Illinois

Orangutan GorillaChimpanzee Human From the Tree of the Life Website, University of Arizona Phylogeny (evolutionary tree)

Phylogenomics = genome-scale phylogeny estimation Note: Jonathan Eisen coined this term, but used it to mean something else.

Phylogenomic pipeline Select taxon set and markers Gather and screen sequence data, possibly identify orthologs Compute multiple sequence alignment and “gene tree” for each locus Compute species tree or phylogenetic network from the gene trees or alignments Get statistical support on each branch (e.g., bootstrapping) Estimate dates on the nodes of the phylogeny Use species tree with branch support and dates to understand biology

Avian Phylogenomics Project E Jarvis, HHMI G Zhang, BGI Approx. 50 species, whole genomes genes, UCEs MTP Gilbert, Copenhagen S. Mirarab Md. S. Bayzid, UT-Austin UT-Austin T. Warnow UT-Austin Plus many many other people… Jarvis, Mirarab, et al., Science 2014

1KP: Thousand Transcriptome Project 1200 plant transcriptomes More than 13,000 gene families (most not single copy) G. Ka-Shu Wong U Alberta N. Wickett Northwestern J. Leebens-Mack U Georgia N. Matasci iPlant T. Warnow, S. Mirarab, N. Nguyen UT-Austin UT-Austin UT-Austin Wickett, Mirarab, et al., Proceedings of the National Academy of Sciences, 2014

Alternate title: “A Tale of Two Studies…” Tandy Warnow The University of Illinois

This talk Gene tree estimation and statistical consistency Gene tree conflict due to incomplete lineage sorting The multi-species coalescent model – Identifiability and statistical consistency The challenge of gene tree estimation error New methods for species tree estimation – Statistical Binning (Science 2014) – ASTRAL (ECCB 2014 and ISMB 2015)

Orangutan GorillaChimpanzee Human From the Tree of the Life Website, University of Arizona Phylogeny (evolutionary tree)

Orangutan GorillaChimpanzee Human From the Tree of the Life Website, University of Arizona Sampling multiple genes from multiple species

Gene Tree Incongruence Gene trees can differ from the species tree due to: – Duplication and loss – Horizontal gene transfer – Incomplete lineage sorting (ILS)

Gene trees inside the species tree (Coalescent Process) Present Past Courtesy James Degnan Gorilla and Orangutan are not siblings in the species tree, but they are in the gene tree.

Incomplete Lineage Sorting (ILS) papers in 2013 alone Confounds phylogenetic analysis for many groups: – Hominids – Birds – Yeast – Animals – Toads – Fish – Fungi There is substantial debate about how to analyze phylogenomic datasets in the presence of ILS.

... Analyze separately Summary Method Two competing approaches gene 1 gene 2... gene k... Concatenation Species

Statistically consistent under ILS? CA-ML (Concatenation using maximum likelihood) - NO MDC – NO GC (Greedy Consensus) – NO MRP (supertree method) – NO Coalescent-based summary methods: – MP-EST (Liu et al. 2010): maximum likelihood estimation of rooted species tree – YES – BUCKy-pop (Ané and Larget 2010): quartet-based Bayesian species tree estimation –YES Co-estimation methods: *BEAST (Heled and Drummond 2009): Bayesian co-estimation of gene trees and species trees - YES

Results on 11-taxon datasets with weak ILS * BEAST more accurate than summary methods (MP-EST, BUCKy, etc) CA-ML (concatenated analysis) most accurate Datasets from Chung and Ané, 2011 Bayzid & Warnow, Bioinformatics 2013

Impact of Gene Tree Estimation Error on MP-EST MP-EST has no error on true gene trees, but MP-EST has 9% error on estimated gene trees Datasets: 11-taxon strongILS conditions with 50 genes Similar results for other summary methods (MDC, Greedy, etc.).

Problem: poor gene trees Summary methods combine estimated gene trees, not true gene trees. The individual gene sequence alignments in the 11-taxon datasets have poor phylogenetic signal, and result in poorly estimated gene trees. Species trees obtained by combining poorly estimated gene trees have poor accuracy.

Problem: poor gene trees Summary methods combine estimated gene trees, not true gene trees. The individual gene sequence alignments in the 11-taxon datasets have poor phylogenetic signal, and result in poorly estimated gene trees. Species trees obtained by combining poorly estimated gene trees have poor accuracy.

Problem: poor gene trees Summary methods combine estimated gene trees, not true gene trees. The individual gene sequence alignments in the 11-taxon datasets have poor phylogenetic signal, and result in poorly estimated gene trees. Species trees obtained by combining poorly estimated gene trees have poor accuracy.

Summary methods combine estimated gene trees, not true gene trees. The individual gene sequence alignments in the 11-taxon datasets have poor phylogenetic signal, and result in poorly estimated gene trees. Species trees obtained by combining poorly estimated gene trees have poor accuracy. TYPICAL PHYLOGENOMICS PROBLEM: many poor gene trees

Summary methods combine estimated gene trees, not true gene trees. The individual gene sequence alignments in the 11-taxon datasets have poor phylogenetic signal, and result in poorly estimated gene trees. Species trees obtained by combining poorly estimated gene trees have poor accuracy. THIS IS A KEY ISSUE IN THE DEBATE ABOUT HOW TO COMPUTE SPECIES TREES

Avian Phylogenomics Project E Jarvis, HHMI G Zhang, BGI Approx. 50 species, whole genomes, 14,000 loci MTP Gilbert, Copenhagen S. Mirarab Md. S. Bayzid, UT-Austin UT-Austin T. Warnow UT-Austin Plus many many other people… Challenges: Concatenation analysis used >200 CPU years But also Massive gene tree conflict suggestive of ILS, and most gene trees had very low bootstrap support Coalescent-based analysis using MP-EST produced tree that conflicted with concatenation analysis

Avian Phylogenomics Project E Jarvis, HHMI G Zhang, BGI Approx. 50 species, whole genomes, 14,000 loci MTP Gilbert, Copenhagen S. Mirarab Md. S. Bayzid, UT-Austin UT-Austin T. Warnow UT-Austin Plus many many other people… Solution: New method to improve coalescent-based analysis: Statistical Binning (Mirarab, Bayzid, Boussau, and Warnow, Science 2014) Species tree estimated using Statistical Binning with MP-EST (Jarvis, Mirarab, et al., Science 2014)

Statistical binning vs. unbinned Mirarab, et al., Science 2014 Binning produces bins with approximate 5 to 7 genes each Datasets: 11-taxon strongILS datasets with 50 genes, Chung and Ané, Systematic Biology

Comparing Binned and Un-binned MP-EST on the Avian Dataset Unbinned MP-EST strongly rejects Columbea, a major finding by Jarvis, Mirarab,et al. Binned MP-EST is largely consistent with the ML concatenation analysis. The trees presented in Science 2014 were the ML concatenation and Binned MP-EST

Statistical Binning Statistical binning uses bootstrap support to group loci into “supergene” datasets, and computes supergene trees on these bins. These supergene trees can be used by a coalescent-based method to construct a tree. Weighted statistical binning is statistically consistent under the MSC; unweighted statistical binning is not! Simulations show both types of statistical binning improve accuracy of species tree topologies and branch lengths, under nearly all conditions we tested. (Exceptions: very small numbers of taxa with very high ILS.)

1KP: Thousand Transcriptome Project 103 plant transcriptomes (1200 in the next phase) single copy “genes” (more than 13,000 gene families, most not single copy, in the next phase) G. Ka-Shu Wong U Alberta N. Wickett Northwestern J. Leebens-Mack U Georgia N. Matasci iPlant T. Warnow, S. Mirarab, N. Nguyen UT-Austin UT-Austin UT-Austin Wickett, Mirarab, et al., Proceedings of the National Academy of Sciences, 2014 Challenges: Massive gene tree heterogeneity consistent with ILS Could not use MP-EST due to missing data (many gene trees could not be rooted) and large number of species

1KP: Thousand Transcriptome Project 103 plant transcriptomes (1200 in the next phase) single copy “genes” (more than 13,000 gene families, most not single copy, in the next phase) G. Ka-Shu Wong U Alberta N. Wickett Northwestern J. Leebens-Mack U Georgia N. Matasci iPlant T. Warnow, S. Mirarab, N. Nguyen UT-Austin UT-Austin UT-Austin Wickett, Mirarab, et al., Proceedings of the National Academy of Sciences, 2014 Solution: New coalescent-based method ASTRAL ASTRAL is statistically consistent, polynomial time, and uses unrooted gene trees.

Maximum Quartet Support Tree Input: Set T ={t 1,t 2,…,t k } of unrooted gene trees, with each tree on taxon set S, and set X of allowed bipartitions Output: Unrooted tree T on leafset S, maximizing the total quartet tree similarity to T Theorem: An exact solution to this problem is statistically consistent under the MSC. Conjecture: MQST is NP-hard

Scientific challenges: Ultra-large multiple-sequence alignment Alignment-free phylogeny estimation Supertree estimation Estimating species trees from many gene trees Genome rearrangement phylogeny Reticulate evolution Visualization of large trees and alignments Data mining techniques to explore multiple optima Theoretical guarantees under Markov models of evolution Applications to metagenomics, protein structure and function prediction, etc. Techniques: machine learning, applied probability theory, graph theory, combinatorial optimization, supercomputing, and heuristics The Tree of Life: Multiple Challenges

Acknowledgments PhD students: Siavash Mirarab* and Md. S. Bayzid** Funding: Guggenheim Foundation, NSF, David Bruton Jr. Centennial Professorship, TACC (Texas Advanced Computing Center), and Founder Professorship in Bioengineering. TACC and UTCS computational resources * Supported by HHMI Predoctoral Fellowship ** Supported by Fulbright Foundation Predoctoral Fellowship