Genome-scale Es-ma-on of the Tree of Life. Tandy Warnow The University of Illinois

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1 Genome-scale Es-ma-on of the Tree of Life Tandy Warnow The University of Illinois

2 Phylogeny (evolu9onary tree) Orangutan Gorilla Chimpanzee Human From the Tree of the Life Website, University of Arizona

3 Phylogenomics = Species trees from whole genomes Nothing in biology makes sense except in the light of evolu9on - Dobhzansky

4 The Tree of Life: Mul$ple Challenges Scien9fic challenges: Ultra-large mul9ple-sequence alignment Alignment-free phylogeny es9ma9on Supertree es9ma9on Es9ma9ng species trees from many gene trees Genome rearrangement phylogeny Re9culate evolu9on Visualiza9on of large trees and alignments Data mining techniques to explore mul9ple op9ma Theore9cal guarantees under Markov models of evolu9on Applica9ons: metagenomics protein structure and func9on predic9on trait evolu9on detec9on of co-evolu9on systems biology Techniques: Graph theory (especially chordal graphs) Probability theory and sta9s9cs Hidden Markov models Combinatorial op9miza9on Heuris9cs Supercompu9ng

5 phylogenomics Orangutan Chimpanzee gene 1 gene 2 gene 999 gene 1000 Gorilla Human ACTGCACACCG ACTGC-CCCCG AATGC-CCCCG -CTGCACACGG CTGAGCATCG CTGAGC-TCG ATGAGC-TC- CTGA-CAC-G AGCAGCATCGTG AGCAGC-TCGTG AGCAGC-TC-TG C-TA-CACGGTG CAGGCACGCACGAA AGC-CACGC-CATA ATGGCACGC-C-TA AGCTAC-CACGGAT gene here refers to a portion of the genome (not a functional gene) I ll use the term gene to refer to c-genes : recombination-free orthologous stretches of the genome 2

6 Gene tree discordance Incomplete Lineage Sor9ng (ILS) is a dominant cause of gene tree heterogeneity gene 1 gene1000 Gorilla Human Chimp Orang. Gorilla Chimp Human Orang. 3

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

8 Incomplete Lineage Sor9ng (ILS) Confounds phylogene9c analysis for many groups: Hominids, Birds, Yeast, Animals, Toads, Fish, Fungi, etc. There is substan9al debate about how to analyze phylogenomic datasets in the presence of ILS, focused around sta9s9cal consistency guarantees (theory) and performance on data.

9 Avian Phylogenomics Project E Jarvis, HHMI MTP Gilbert, Copenhagen G Zhang, BGI T. Warnow UT-Aus9n S. Mirarab Md. S. Bayzid, UT-Aus9n UT-Aus9n Plus many many other people Approx. 50 species, whole genomes, 14,000 loci Jarvis, Mirarab, et al., Science 2014 Major challenges: Concatena9on analysis took > 250 CPU years, and suggested a rapid radia9on Massive gene tree heterogeneity consistent with incomplete lineage sor9ng Standard coalescent-based species tree es9ma9on methods contradicted concatena9on analysis and prior studies

10 1KP: Thousand Transcriptome Project G. Ka-Shu Wong U Alberta J. Leebens-Mack U Georgia N. Wickett Northwestern N. Matasci iplant T. Warnow, S. Mirarab, N. Nguyen UT-Austin UT-Austin UT-Austin l 103 plant transcriptomes, single copy genes l Next phase will be much bigger l Wickeh, Mirarab et al., PNAS 2014 Challenges: Massive gene tree heterogeneity consistent with ILS Could not use exis9ng coalescent methods due to missing data (many gene trees could not be rooted) and large number of species

11 This talk Gene tree heterogeneity due to incomplete lineage sor9ng, modelled by the mul9-species coalescent (MSC) Sta9s9cally consistent es9ma9on of species trees under the MSC, and the impact of gene tree es9ma9on error New methods in phylogenomics: Sta9s9cal binning (Science 2014) and Weighted Sta9s9cal Binning (PLOS One 2015): improving gene trees ASTRAL (Bioinforma9cs 2014, 2015): quartet-based es9ma9on Open ques9ons

12 Sampling mul9ple genes from mul9ple species Orangutan Gorilla Chimpanzee Human From the Tree of the Life Website, University of Arizona

13 A species tree defines a probability distribu9on on gene trees under the Mul9-Species Coalescent (MSC) Model Past Present Courtesy James Degnan Gorilla and Orangutan are not siblings in the species tree, but they are in the gene tree.

14 Sta9s9cal Consistency error Data

15 Main compe9ng approaches Species gene 1 gene 2... gene k... Concatenation Analyze separately... Summary Method

16

17 Sta9s9cally consistent under MSC? CA-ML (Concatena9on using unpar99oned maximum likelihood) - NO Most frequent gene tree NO Minimize Deep Coalescences (MDC) NO Greedy Consensus (GC) NO Matrix Representa9on with Parsimony (MRP, supertree method) NO Hence, none of these standard approaches are proven to converge to the true species tree as the number of loci increases. Many of them are posi9vely misleading (will converge to the wrong tree)!

18 Anomaly zone An anomalous gene tree (AGT) is one that is more probable than the true species tree under the mul9-species coalescent model.

19 Anomaly zone An anomalous gene tree (AGT) is one that is more probable than the true species tree under the mul9-species coalescent model. Theorem (Degnan 2013, Rosenberg 2013): For n>3, there are model species trees with rooted AGTs, and for n>4 there are model species trees with unrooted AGTs.

20 Anomaly zone An anomalous gene tree (AGT) is one that is more probable than the true species tree under the mul9-species coalescent model. Theorem (Hudson 1983): There are no rooted 3-leaf AGTs. Theorem (Allman et al. 2011, Degnan 2013): There are no unrooted 4-leaf AGTs.

21 Summary Methods...

22 Summary Methods... Compu9ng rooted species tree from rooted gene trees: For every three species {a,b,c}, record most frequent rooted gene tree on {a,b,c} Combine rooted three-leaf gene trees into rooted tree if they are compa9ble Theorem: This algorithm is sta9s9cally consistent under the MSC and runs in polynomial 9me.

23 Summary Methods... Compu9ng unrooted species tree from unrooted gene trees: For every four species {a,b,c,d}, record most frequent unrooted gene tree on {a,b,c,d} Combine unrooted four-leaf gene trees into unrooted tree if they are compa9ble (recursive algorithm based on finding sibling pairs and removing one sibling) Theorem: This algorithm is sta9s9cally consistent under the MSC and runs in polynomial 9me.

24 Sta9s9cally consistent under ILS? Coalescent-based summary methods: MP-EST (Liu et al. 2010): maximum pseudo-likelihood es9ma9on of rooted species tree based on rooted triplet tree distribu9on YES BUCKy-pop (Ané and Larget 2010): quartet-based Bayesian species tree es9ma9on YES And many others (ASTRAL, ASTRID, NJst, GLASS, etc.) - YES Co-es-ma-on methods: *BEAST (Heled and Drummond 2009): Bayesian coes9ma9on of gene trees and species trees YES Co-es9ma9on methods are too slow to use on most datasets hence the debate is largely between concatena9on (tradi9onal approach) and summary methods. Single-site methods (SMRT, SVDquartets, METAL, SNAPP, and others) - YES CA-ML (Concatena9on using unpar99oned maximum likelihood) - NO MDC NO GC (Greedy Consensus) NO

25 Sta9s9cally consistent under ILS? Coalescent-based summary methods: MP-EST (Liu et al. 2010): maximum pseudo-likelihood es9ma9on of rooted species tree based on rooted triplet tree distribu9on YES BUCKy-pop (Ané and Larget 2010): quartet-based Bayesian species tree es9ma9on YES And many others (ASTRAL, ASTRID, NJst, GLASS, etc.) - YES Co-es-ma-on methods: *BEAST (Heled and Drummond 2009): Bayesian coes9ma9on of gene trees and species trees YES Co-es9ma9on methods are too slow to use on most datasets hence the debate is largely between concatena9on (tradi9onal approach) and summary methods. Single-site methods (SMRT, SVDquartets, METAL, SNAPP, and others) - YES CA-ML (Concatena9on using unpar99oned maximum likelihood) - NO MDC NO GC (Greedy Consensus) NO

26 Sta9s9cally consistent under ILS? Coalescent-based summary methods: MP-EST (Liu et al. 2010): maximum pseudo-likelihood es9ma9on of rooted species tree based on rooted triplet tree distribu9on YES BUCKy-pop (Ané and Larget 2010): quartet-based Bayesian species tree es9ma9on YES And many others (ASTRAL, ASTRID, NJst, GLASS, etc.) - YES Co-es-ma-on methods: *BEAST (Heled and Drummond 2009): Bayesian coes9ma9on of gene trees and species trees YES Co-es9ma9on methods are too slow to use on most datasets hence the debate is largely between concatena9on (tradi9onal approach) and summary methods. Single-site methods (SMRT, SVDquartets, METAL, SNAPP, and others) - YES CA-ML (Concatena9on using unpar99oned maximum likelihood) - NO MDC NO GC (Greedy Consensus) NO

27 Results on 11-taxon datasets with weak ILS Average FN rate *BEAST CA ML BUCKy con BUCKy pop MP EST Phylo exact MRP GC 0 5 genes 10 genes 25 genes 50 genes *BEAST more accurate than summary methods (MP-EST, BUCKy, etc) CA-ML (concatenated analysis) most accurate Datasets from Chung and Ané, 2011 Bayzid & Warnow, Bioinforma9cs 2013

28 Results on 11-taxon datasets with weak ILS 0.25 Average FN rate *BEAST CA ML BUCKy con BUCKy pop MP EST Phylo exact MRP GC *BEAST MORE ACCURATE than summary methods, because *BEAST gets more accurate gene trees! genes 10 genes 25 genes 50 genes *BEAST more accurate than summary methods (MP-EST, BUCKy, etc) CA-ML (concatenated analysis) most accurate Datasets from Chung and Ané, 2011 Bayzid & Warnow, Bioinforma9cs 2013

29 Results on 11-taxon datasets with weak ILS 0.25 Average FN rate *BEAST CA ML BUCKy con BUCKy pop MP EST Phylo exact MRP GC Summary methods (BUCKy-pop, MP-EST) are both sta9s9cally consistent under the MSC but are impacted by gene tree es9ma9on error genes 10 genes 25 genes 50 genes *BEAST more accurate than summary methods (MP-EST, BUCKy, etc) CA-ML (concatenated analysis) most accurate Datasets from Chung and Ané, 2011 Bayzid & Warnow, Bioinforma9cs 2013

30 Results on 11-taxon datasets with weak ILS 0.25 Average FN rate *BEAST CA ML BUCKy con BUCKy pop MP EST Phylo exact MRP GC Concatena9on (RAxML) best of all methods on these data! (However, for high enough ILS, concatena9on is not as accurate as the best summary methods.) 0 5 genes 10 genes 25 genes 50 genes *BEAST more accurate than summary methods (MP-EST, BUCKy, etc) CA-ML (concatenated analysis) most accurate Datasets from Chung and Ané, 2011 Bayzid & Warnow, Bioinforma9cs 2013

31 Impact of Gene Tree Es9ma9on Error on MP-EST Average FN rate true estimated MP EST MP-EST has no error on true gene trees, but MP-EST has 9% error on es-mated gene trees Datasets: 11-taxon strongils condi9ons with 50 genes Similar results for other summary methods (MDC, Greedy, etc.)

32 TYPICAL PHYLOGENOMICS PROBLEM: many poor gene trees Summary methods combine es9mated gene trees, not true gene trees. Mul9ple studies show that summary methods can be less accurate than concatena9on in the presence of high gene tree es9ma9on error. Genome-scale data includes a range of markers, not all of which have substan9al signal. Furthermore, removing sites due to model viola9ons reduces signal. Some researchers also argue that gene trees should be based on very short alignments, to avoid intra-locus recombina9on.

33 Gene tree es9ma9on error: key issue in the debate Summary methods combine es9mated gene trees, not true gene trees. Mul9ple studies show that summary methods can be less accurate than concatena9on in the presence of high gene tree es9ma9on error. Genome-scale data includes a range of markers, not all of which have substan9al signal. Furthermore, removing sites due to model viola9ons reduces signal. Some researchers also argue that gene trees should be based on very short alignments, to avoid intra-locus recombina9on.

34 Avian Phylogenomics Project Erich Jarvis, HHMI MTP Gilbert, Copenhagen Guojie Zhang, BGI Siavash Mirarab, Tandy Warnow, Texas Texas and UIUC Approx. 50 species, whole genomes 14,000 loci Multi-national team (100+ investigators) 8 papers published in special issue of Science 2014 Biggest computational challenges: 1. Multi-million site maximum likelihood analysis (~300 CPU years, and 1Tb of distributed memory, at supercomputers around world) 2. Constructing coalescent-based species tree from 14,000 different gene trees

35 Only 48 species, but heuris9c ML took ~300 CPU years on mul9ple supercomputers and used 1Tb of memory Jarvis,$Mirarab,$et$al.,$examined$48$ bird$species$using$14,000$loci$from$ whole$genomes.$two$trees$were$ presented.$ $ 1.$A$single$dataset$maximum$ likelihood$concatena,on$analysis$ used$~300$cpu$years$and$1tb$of$ distributed$memory,$using$tacc$and$ other$supercomputers$around$the$ world.$$ $ 2.$However,$every%locus%had%a% different%%tree$ $sugges,ve$of$ incomplete$lineage$sor,ng $ $and$ the$noisy$genomehscale$data$required$ the$development$of$a$new$method,$ sta,s,cal$binning.$ $ $ $ $ RESEARCH ARTICLE Whole-genome analyses resolve early branches in the tree of life of modern birds Erich D. Jarvis, 1 * Siavash Mirarab, 2 * Andre J. Aberer, 3 Bo Li, 4,5,6 Peter Houde, 7 Cai Li, 4,6 Simon Y. W. Ho, 8 Brant C. Faircloth, 9,10 Benoit Nabholz, 11 Jason T. Howard, 1 Alexander Suh, 12 Claudia C. Weber, 12 Rute R. da Fonseca, 6 Jianwen Li, 4 Fang Zhang, 4 Hui Li, 4 Long Zhou, 4 Nitish Narula, 7,13 Liang Liu, 14 Ganesh Ganapathy, 1 Bastien Boussau, 15 Md. Shamsuzzoha Bayzid, 2 Volodymyr Zavidovych, 1 Sankar Subramanian, 16 Toni Gabaldón, 17,18,19 Salvador Capella-Gutiérrez, 17,18 Jaime Huerta-Cepas, 17,18 Bhanu Rekepalli, 20 Kasper Munch, 21 Mikkel Schierup, 21 Bent Lindow, 6 Wesley C. Warren, 22 David Ray, 23,24,25 Richard E. Green, 26 Michael W. Bruford, 27 Xiangjiang Zhan, 27,28 Andrew Dixon, 29 Shengbin Li, 30 Ning Li, 31 Yinhua Huang, 31 Elizabeth P. Derryberry, 32,33 Mads Frost Bertelsen, 34 Frederick H. Sheldon, 33 Robb T. Brumfield, 33 Claudio V. Mello, 35,36 Peter V. Lovell, 35 Morgan Wirthlin, 35 Maria Paula Cruz Schneider, 36,37 Francisco Prosdocimi, 36,38 José Alfredo Samaniego, 6 Amhed Missael Vargas Velazquez, 6 Alonzo Alfaro-Núñez, 6 Paula F. Campos, 6 Bent Petersen, 39 Thomas Sicheritz-Ponten, 39 An Pas, 40 Tom Bailey, 41 Paul Scofield, 42 Michael Bunce, 43 David M. Lambert, 16 Qi Zhou, 44 Polina Perelman, 45,46 Amy C. Driskell, 47 Beth Shapiro, 26 Zijun Xiong, 4 Yongli Zeng, 4 Shiping Liu, 4 Zhenyu Li, 4 Binghang Liu, 4 Kui Wu, 4 Jin Xiao, 4 Xiong Yinqi, 4 Qiuemei Zheng, 4 Yong Zhang, 4 Huanming Yang, 48 Jian Wang, 48 Linnea Smeds, 12 Frank E. Rheindt, 49 Michael Braun, 50 Jon Fjeldsa, 51 Ludovic Orlando, 6 F. Keith Barker, 52 Knud Andreas Jønsson, 51,53,54 Warren Johnson, 55 Klaus-Peter Koepfli, 56 Stephen O Brien, 57,58 David Haussler, 59 Oliver A. Ryder, 60 Carsten Rahbek, 51,54 Eske Willerslev, 6 Gary R. Graves, 51,61 Travis C. Glenn, 62 John McCormack, 63 Dave Burt, 64 Hans Ellegren, 12 Per Alström, 65,66 Scott V. Edwards, 67 Alexandros Stamatakis, 3,68 David P. Mindell, 69 Joel Cracraft, 70 Edward L. Braun, 71 Tandy Warnow, 2,72 Wang Jun, 48,73,74,75,76 M. Thomas P. Gilbert, 6,43 Guojie Zhang 4,77 To better determine the history of modern birds, we performed a genome-scale phylogenetic analysis of 48 species representing all orders of Neoaves using phylogenomic methods created to handle genome-scale data. We recovered a highly resolved tree that confirms previously controversial sister or close relationships. We identified the first divergence in

36 We$used$100$CPU$$ years$(mostly$on$$ TACC)$to$develop$$ and$test$this$$ method.$ RESEARCH ARTICLE SUMMARY AVIAN GENOMICS Statistical binning enables an accurate coalescent-based estimation of the avian tree Siavash Mirarab, Md. Shamsuzzoha Bayzid, Bastien Boussau, Tandy Warnow* INTRODUCTION: Reconstructing species trees for rapid radiations, as in the early diversification of birds, is complicated by biological processes such as incomplete ON OUR WEB SITE Read the full article at science lineage sorting (ILS) that can cause different parts of the genome to have different evolutionary histories. Statistical methods, based on the multispecies coalescent model and that combine gene trees, can be highly accurate even in the presence of massive ILS; however, these methods can produce species trees that are topologically far from the species tree when estimated gene trees have error. We have developed a statistical binning technique to address gene tree estimation error and have explored its use in genomescale species tree estimation with MP-EST, a popular coalescent-based species tree estimation method. Statistical binning technique RATIONALE: In statistical binning, phylogenetic trees on different genes are estimated and then placed into bins, so that the differences between trees in the same bin can be explained by estimation error (see the figure). A new tree is then estimated for each bin by applying maximum likelihood to a concatenated alignment of the multiple sequence alignments of its genes, and a species tree is estimated using a coalescent-based species tree method from these supergene trees. RESULTS: Under realistic conditions in our simulation study, statistical binning reduced the topological error of species trees estimated using MP-EST and enabled a coalescent-based analysis that was more accurate than concatenation even when gene tree estimation error was relatively high. Statistical binning also reduced the error in gene tree topology and species tree branch length estimation, especially Traditional pipeline (unbinned) when the phylogenetic signal in gene sequence alignments was low. Species trees estimated using MP-EST with statistical binning on four biological data sets showed increased concordance with the biological literature. When MP-EST was used to analyze 14,446 gene trees in the avian phylogenomics project, it produced a species tree that was discordant with the concatenation analysis and conflicted with prior literature. However, the statistical binning analysis produced a tree that was highly congruent with the concatenation analysis and was consistent with the prior scientific literature. CONCLUSIONS: Statistical binning reduces the error in species tree topology and branch length estimation because it reduces gene tree estimation error. These improvements are greatest when gene trees have reduced bootstrap support, which was the case for the avian phylogenomics project. Because using unbinned gene trees can result in overestimation of ILS, statistical binning may be helpful in providing more accurate estimations of ILS levels in biological data sets. Thus, statistical binning enables highly accurate species tree estimations, even on genome-scale data sets. The list of author affiliations is available in the full article online. *Corresponding author. warnow@illinois.edu Cite this article as S. Mirarab et al., Science 346, (2014). DOI: /science Downloaded from on January 7, 2015 Sequence data Gene alignments Estimated gene trees Species tree Statistical binning pipeline Incompatibility graph Binned supergene alignments Supergene trees Species tree The statistical binning pipeline for estimating species trees from gene trees. Loci are grouped into bins based on a statistical test for combinabilty, before estimating gene trees. SCIENCE sciencemag.org 12 DECEMBER 2014 VOL 346 ISSUE Published by AAAS

37 Ideas behind sta9s9cal binning Gene tree error tends to decrease with the number of sites in the alignment Number of sites in an alignment Concatena9on (even if not sta9s9cally consistent) tends to be reasonably accurate when there is not too much gene tree heterogeneity

38 Statistical binning technique a coalescent-based analysis that was more accurate than concatenation even when gene tree estimation error was relatively high. Statistical binning also reduced the error in gene tree topology and species tree branch length estimation, especially Traditional pipeline (unbinned) The list of author affiliations is available in the full article online. *Corresponding author. warnow@illinois.edu Cite this article as S. Mirarab et al., Science 346, (2014). DOI: /science Downloaded from Sequence data Gene alignments Estimated gene trees Species tree Statistical binning pipeline Incompatibility graph Binned supergene alignments Supergene trees Species tree The statistical binning pipeline for estimating species trees from gene trees. Loci are grouped into bins based on a statistical test for combinabilty, before estimating gene trees. Published by AAAS Note: Supergene trees computed using fully par99oned maximum likelihood Vertex-coloring graph with balanced color classes is NP-hard; we used heuris9c.

39 Sta9s9cal binning vs. unbinned Average FN rate Unbinned Statistical MP EST MDC*(75) MRP MRL GC Datasets: 11-taxon strongils datasets with 50 genes from Chung and Ané, Systema9c Biology Binning produces bins with approximate 5 to 7 genes each

40 Theorem 3 (PLOS One, Bayzid et al. 2015): Unweighted sta9s9cal binning pipelines are not sta9s9cally consistent under GTR+MSC As the number of sites per locus increase: All es9mated gene trees converge to the true gene tree and have bootstrap support that converges to 1 (Steel 2014) For each bin, with probability converging to 1, the genes in the bin have the same tree topology (but can have different numeric parameters), and there is only one bin for any given tree topology For each bin, a fully par99oned maximum likelihood (ML) analysis of its supergene alignment converges to a tree with the common gene tree topology. As the number of loci increase: every gene tree topology appears with probability converging to 1. Hence as both the number of loci and number of sites per locus increase, with probability converging to 1, every gene tree topology appears exactly once in the set of supergene trees. It is impossible to infer the species tree from the flat distribu9on of gene trees!

41

42 Theorem 2 (PLOS One, Bayzid et al. 2015): WSB pipelines are sta9s9cally consistent under GTR+MSC Easy proof: As the number of sites per locus increase All es9mated gene trees converge to the true gene tree and have bootstrap support that converges to 1 (Steel 2014) For every bin, with probability converging to 1, the genes in the bin have the same tree topology Fully par99oned GTR ML analysis of each bin converges to a tree with the common topology of the genes in the bin Hence as the number of sites per locus and number of loci both increase, WSB followed by a sta9s9cally consistent summary method will converge in probability to the true species tree. Q.E.D.

43 Weighted Sta9s9cal Binning: empirical WSB generally benign to highly beneficial: Improves accuracy of gene tree topology Improves accuracy of species tree topology Improves accuracy of species tree branch length Reduces incidence of highly supported false posi9ve branches

44 Sta-s-cal binning vs. Unbinned and Concatena-on (a) MP-EST on varying gene sequence length (b) ASTRAL on varying gene sequence length Species tree es9ma9on error for MP-EST and ASTRAL, and also concatena9on using ML, on avian simulated datasets: 48 taxa, moderately high ILS (AD=47%), 1000 genes, and varying gene sequence length. Bayzid et al., (2015). PLoS ONE 10(6): e

45 Comparing Binned and Un-binned MP-EST on the Avian Dataset Conflict with other lines of strong evidence 97/97 100/99 Australaves 91/87 88/90 99/99 Cursores Otidimorphae 80/79 Columbea 9 7/94 Calypte anna Chaetura pelagica Antrostomus carolinensis Passeriformes Psittaciformes Falco peregrinus Cariama cristata Coraciimorphae Accipitriformes Tyto alba 59/57 Pelecanus crispus 87 Egrett agarzetta Nipponia nippon Phalacrocorax carbo Procellariimorphae Gavia stellata Gavia stellata 94 50/48 Phaethon lepturus Phaethon lepturus 68 58/56 100/99 100/99 Eurypyga helias Balearica regulorum Charadrius vociferus Opisthocomus hoazin Tauraco erythrolophus Chlamydotis macqueenii Cuculus canorus Phoenicopterus ruber Podiceps cristatus Columbal ivia Pterocles gutturalis Mesitornis unicolor Meleagris gallopavo Gallus gallus Anas platyrhynchos Tinamus guttatus Struthio camelus Binned MP-EST (unweighted/weighted) Calypte anna Chaetura pelagica Antrostomus carolinensis Passeriformes Psittaciformes Falco peregrinus Coraciimorphae Cariama cristata Accipitriformes Tyto alba Pelecanus crispus Egrett agarzetta Nipponia nippon Phalacrocorax carbo Procellariimorphae Eurypyga helias Balearica regulorum Charadrius vociferus Opisthocomus hoazin Phoenicopterus ruber Podiceps cristatus Tauraco erythrolophus Chlamydotis macqueenii Cuculus canorus Columbal ivia Pterocles gutturalis Mesitornis unicolor Meleagris gallopavo Gallus gallus Anas platyrhynchos Tinamus guttatus Struthio camelus Unbinned MP-EST Unbinned MP-EST strongly rejects Columbea, a major finding by Jarvis, Mirarab,et al. Binned MP-EST is largely consistent with the ML concatena9on analysis. The trees presented in Science 2014 were the ML concatena9on and Binned MP-EST

46 Running Time Comparison Concatena9on analysis of the Avian dataset: ~250 CPU years and 1Tb memory Sta9s9cal binning analysis: ~5 CPU years, almost all of which was compu9ng maximum likelihood gene trees, much less memory usage Species tree es9ma9on using tradi9onal approaches is more computa9onally expensive, and not as accurate as coalescentbased methods!

47 Summary (so far) Sta9s9cal binning (weighted or unweighted): improves gene trees, and leads to improved species trees in the presence of ILS compared to unbinned analyses. Sta9s9cal binning pipelines are also more accurate than concatena9on under high ILS. Pipelines using weighted version are sta9s9cally consistent under the mul9-species coalescent model. Sta9s9cal binning pipelines are much faster than concatena9on analyses (e.g. 5 years vs. 250 years for avian dataset).

48 1KP: Thousand Transcriptome Project G. Ka-Shu Wong U Alberta J. Leebens-Mack U Georgia N. Wickett Northwestern N. Matasci iplant T. Warnow, S. Mirarab, N. Nguyen UT-Austin UT-Austin UT-Austin l 103 plant transcriptomes, single copy genes l Wickeh, Mirarab et al., PNAS 2014 l Next phase will be much bigger (~1000 species and ~1000 genes) Challenges: Massive gene tree heterogeneity consistent with ILS Could not use exis9ng coalescent methods due to missing data (many gene trees could not be rooted) and large number of species

49 1KP: Thousand Transcriptome Project G. Ka-Shu Wong U Alberta J. Leebens-Mack U Georgia N. Wickett Northwestern N. Matasci iplant T. Warnow, S. Mirarab, N. Nguyen UT-Austin UT-Austin UT-Austin l 103 plant transcriptomes, single copy genes l Wickeh, Mirarab et al., PNAS 2014 l Next phase will be much bigger (~1000 species and ~1000 genes) Solu9on: New coalescent-based method ASTRAL (Mirarab et al., ECCB/ Bioinforma-cs 2014, Mirarab et al., ISMB/Bioinforma-cs 2015) ASTRAL is sta9s9cally consistent, polynomial 9me, and uses unrooted gene trees.

50 ASTRAL [Mirarab, et al., ECCB/Bioinformatics, 2014] Optimization Problem (NP-Hard): Find the species tree with the maximum number of induced quartet trees shared with the collection of input gene trees Score(T )= X t2t Q(T ) \ Q(t) Set of quartet trees induced by T a gene tree all input gene trees Theorem: Statistically consistent under the multispecies coalescent model when solved exactly 15

51 Constrained Maximum Quartet Support Tree Input: Set T = {t 1,t 2,,t k } of unrooted gene trees, with each tree on set S with n species, and set X of allowed bipar99ons Output: Unrooted tree T on leafset S, maximizing the total quartet tree similarity to T, subject to T drawing its bipar99ons from X. Theorems (Mirarab et al., 2014): If X contains the bipar99ons from the input gene trees (and perhaps others), then an exact solu9on to this problem is sta9s9cally consistent under the MSC. The constrained MQST problem can be solved in O( X 2 nk) 9me. (We use dynamic programming, and build the unrooted tree from the bohom-up, based on allowed clades halves of the allowed bipar99ons.)

52 Constrained Maximum Quartet Support Tree Input: Set T = {t 1,t 2,,t k } of unrooted gene trees, with each tree on set S with n species, and set X of allowed bipar99ons Output: Unrooted tree T on leafset S, maximizing the total quartet tree similarity to T, subject to T drawing its bipar99ons from X. Theorems (Mirarab et al., 2014): If X contains the bipar99ons from the input gene trees (and perhaps others), then an exact solu9on to this problem is sta9s9cally consistent under the MSC. The constrained MQST problem can be solved in O( X 2 nk) 9me. (We use dynamic programming, and build the unrooted tree from the bohom-up, based on allowed clades halves of the allowed bipar99ons.)

53 Constrained Maximum Quartet Support Tree Input: Set T = {t 1,t 2,,t k } of unrooted gene trees, with each tree on set S with n species, and set X of allowed bipar99ons Output: Unrooted tree T on leafset S, maximizing the total quartet tree similarity to T, subject to T drawing its bipar99ons from X. Theorems (Mirarab et al., 2014): If X contains the bipar99ons from the input gene trees (and perhaps others), then an exact solu9on to this problem is sta9s9cally consistent under the MSC. The constrained MQST problem can be solved in O( X 2 nk) 9me. (We use dynamic programming, and build the unrooted tree from the bohom-up, based on allowed clades halves of the allowed bipar99ons.)

54 Simulation study Variable parameters: Number of species: True (model) species tree True gene trees Sequence data Number of genes: Finch Falcon Owl Eagle Pigeon Amount of ILS: low, medium, high Deep versus recent speciation Finch Owl Falcon Eagle Pigeon Es mated species tree Es mated gene trees 11 model conditions (50 replicas each) with heterogenous gene tree error Compare to NJst, MP-EST, concatenation (CA-ML) Evaluate accuracy using FN rate: the percentage of branches in the true tree that are missing from the estimated tree Used SimPhy, Mallo and Posada,

55 Tree accuracy when varying the number of species Species tree topological error (FN) 16% 12% 8% 4% ASTRAL II MP EST genes, medium levels of recent ILS 16

56 Tree accuracy when varying the number of species Species tree topological error (FN) 16% 12% 8% 4% ASTRAL II MP EST number of species 1000 genes, medium levels of recent ILS 16

57

58 Accuracy in the presence of HGT + ILS 200 Estimated Gene Trees Data: Fixed, moderate ILS rate, 50 replicates per HGT rates (1)-(6), 1 model species tree per replicate on 51 taxa, 1000 true gene trees, simulated 1000 bp gene sequences using INDELible 8, 1000 gene trees estimated from GTR simulated sequences using FastTree Price, Dehal, Arkin Fletcher, Yang Davidson et al., RECOMB-CG, BMC Genomics 2015

59

60

61 Summary ASTRAL is a summary methods that is sta9s9cally consistent in the presence of ILS, and that run in polynomial 9me. ASTRAL can analyze very large datasets (1000 species and 1000 genes or more) with high accuracy. Coalescent-based summary methods are much faster than tradi9onal concatena9on approaches, and they can provide improved accuracy in the presence of gene tree heterogeneity. Gene tree es9ma9on error impacts accuracy of species trees but sta9s9cal binning can reduce gene tree es9ma9on error, and lead to improved species tree es9ma9ons (topology, branch lengths, and incidence of false posi9ves).

62 What is the impact of gene tree es9ma9on error on species tree es9ma9on? Ques9on: Do any summary methods converge to the species tree as the number of loci increase, but where each locus has only a constant number of sites? Answers: Roch & Warnow, Syst Biol, March 2015: Strict molecular clock: Yes for some new methods, even for a single site per locus No clock: Unknown for all methods, including MP-EST, ASTRAL, etc. S. Roch and T. Warnow. "On the robustness to gene tree es9ma9on error (or lack thereof) of coalescent-based species tree methods", Systema9c Biology, 64(4): , 2015, (PDF)

63 Future Direc9ons Beher coalescent-based summary methods (that are more robust to gene tree es9ma9on error) Beher techniques for es9ma9ng gene trees given mul9-locus data, or for co-es9ma9ng gene trees and species trees Beher theory about robustness to gene tree es9ma9on error (or lack thereof) for coalescentbased summary methods Beher single site methods (see SMRT, SVDquartets, METAL, and SNAPP)

64 The Tree of Life: Mul$ple Challenges Scien9fic challenges: Ultra-large mul9ple-sequence alignment Alignment-free phylogeny es9ma9on Supertree es9ma9on Es9ma9ng species trees from many gene trees Genome rearrangement phylogeny Re9culate evolu9on Visualiza9on of large trees and alignments Data mining techniques to explore mul9ple op9ma Theore9cal guarantees under Markov models of evolu9on Applica9ons: metagenomics protein structure and func9on predic9on trait evolu9on detec9on of co-evolu9on systems biology Techniques: Graph theory (especially chordal graphs) Probability theory and sta9s9cs Hidden Markov models Combinatorial op9miza9on Heuris9cs Supercompu9ng

65 Big Phylogenomic Data Thousands to millions of sequences and species, millions of sites per species Big phylogenomic data are not the same as the usual data Rela9ve performance of methods change with dataset size and heterogeneity Even moderate-sized inputs can create huge outputs We need new methods, new op$miza$on problems, new sta$s$cal models, new sta$s$cal theory, compression methods, visualiza$on methods,.

66 Acknowledgments NSF grant DBI (joint with Noah Rosenberg at Stanford and Luay Nakhleh at Rice): hhp://tandy.cs.illinois.edu/phylogenomicsproject.html Papers available at hhp://tandy.cs.illinois.edu/papers.html SoSware ASTRAL and sta-s-cal binning: Available at hhps://github.com/smirarab Others at hhp://tandy.cs.illinois.edu/so ware.html Other Funding: David Bruton Jr. Centennial Professorship, TACC (Texas Advanced Compu9ng Center), Grainger Founda9on, and HHMI (to SM)

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