computa(onal challenges in construcng the tree of lifeipdps.org/ipdps2017/warnow-ipdps2017.pdf ·...

Computa(onalChallengesinConstruc(ngtheTreeofLife

TandyWarnowFounderProfessorofEngineering

TheUniversityofIllinoisatUrbana-Champaignh?p://tandy.cs.illinois.edu

Orangutan Gorilla Chimpanzee Human

From the Tree of the Life Website, University of Arizona

Phylogeny(evoluEonarytree)

The “Tree of Life”

“ArchaeaTree”

courtesyofEMSL@PNNL

Phylogenies and Applications

BasicBiology:Howdidlifeevolve?

ApplicaEonsofphylogeniesto:proteinstructureandfuncEonpopulaEongeneEcshumanmigraEonsmetagenomics

“NothinginbiologymakessenseexceptinthelightofevoluEon”--Dobhzansky(1973)

Phylogenomics = Species trees from whole genomes

Phylogenomicpipeline

•  Selecttaxonsetandmarkers

•  Gatherandscreensequencedata,possiblyidenEfyorthologs

•  Foreachgene:

–  ComputemulEplesequencealignment

–  ConstructphylogeneEctree•  Computespeciestreeornetwork:

–  CombinetheesEmatedgenetrees,OR

–  EsEmateatreefromaconcatenaEonofthemulEplesequencealignments

•  Usespeciestreewithbranchsupportanddatestounderstandbiology

ButeverythingisNP-hard!



•  Foreachgene:






Avian Phylogenomics Project Erich Jarvis, HHMI

Guojie Zhang, BGI

•  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

MTP Gilbert, Copenhagen

Siavash Mirarab, Tandy Warnow, Texas Texas and UIUC

90. J. F. Storz, J. C. Opazo, F. G. Hoffmann, Mol. Phylogenet. Evol.66, 469–478 (2013).

91. F. G. Hoffmann, J. F. Storz, T. A. Gorr, J. C. Opazo, Mol. Biol.Evol. 27, 1126–1138 (2010).

ACKNOWLEDGMENTS

Genome assemblies and annotations of avian genomes in thisstudy are available on the avian phylogenomics website(http://phybirds.genomics.org.cn), GigaDB (http://dx.doi.org/10.5524/101000), National Center for Biotechnology Information(NCBI), and ENSEMBL (NCBI and Ensembl accession numbersare provided in table S2). The majority of this study wassupported by an internal funding from BGI. In addition, G.Z. wassupported by a Marie Curie International Incoming Fellowshipgrant (300837); M.T.P.G. was supported by a Danish NationalResearch Foundation grant (DNRF94) and a Lundbeck Foundationgrant (R52-A5062); C.L. and Q.L. were partially supported by aDanish Council for Independent Research Grant (10-081390);and E.D.J. was supported by the Howard Hughes Medical Instituteand NIH Directors Pioneer Award DP1OD000448.

The Avian Genome ConsortiumChen Ye,1 Shaoguang Liang,1 Zengli Yan,1 M. Lisandra Zepeda,2

Paula F. Campos,2 Amhed Missael Vargas Velazquez,2

José Alfredo Samaniego,2 María Avila-Arcos,2 Michael D. Martin,2

Ross Barnett,2 Angela M. Ribeiro,3 Claudio V. Mello,4 Peter V. Lovell,4

Daniela Almeida,3,5 Emanuel Maldonado,3 Joana Pereira,3

Kartik Sunagar,3,5 Siby Philip,3,5 Maria Gloria Dominguez-Bello,6

Michael Bunce,7 David Lambert,8 Robb T. Brumfield,9

Frederick H. Sheldon,9 Edward C. Holmes,10 Paul P. Gardner,11

Tammy E. Steeves,11 Peter F. Stadler,12 Sarah W. Burge,13

Eric Lyons,14 Jacqueline Smith,15 Fiona McCarthy,16

Frederique Pitel,17 Douglas Rhoads,18 David P. Froman19

1China National GeneBank, BGI-Shenzhen, Shenzhen 518083,China. 2Centre for GeoGenetics, Natural History Museum ofDenmark, University of Copenhagen, Øster Voldgade 5-7, 1350Copenhagen, Denmark. 3CIMAR/CIIMAR, Centro Interdisciplinar deInvestigação Marinha e Ambiental, Universidade do Porto, Ruados Bragas, 177, 4050-123 Porto, Portugal. 4Department ofBehavioral Neuroscience Oregon Health & Science UniversityPortland, OR 97239, USA. 5Departamento de Biologia, Faculdadede Ciências, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal. 6Department of Biology, University of PuertoRico, Av Ponce de Leon, Rio Piedras Campus, JGD 224, San Juan,PR 009431-3360, USA. 7Trace and Environmental DNA laboratory,Department of Environment and Agriculture, Curtin University, Perth,Western Australia 6102, Australia. 8Environmental Futures ResearchInstitute, Griffith University, Nathan, Queensland 4121, Australia.9Museum of Natural Science, Louisiana State University, BatonRouge, LA 70803, USA. 10Marie Bashir Institute for InfectiousDiseases and Biosecurity, Charles Perkins Centre, School ofBiological Sciences and Sydney Medical School, The University ofSydney, Sydney NSW 2006, Australia. 11School of BiologicalSciences, University of Canterbury, Christchurch 8140, New Zealand.12Bioinformatics Group, Department of Computer Science, andInterdisciplinary Center for Bioinformatics, University of Leipzig,Hrtelstrasse 16-18, D-04107 Leipzig, Germany. 13European MolecularBiology Laboratory, European Bioinformatics Institute, Hinxton,Cambridge CB10 1SD, UK. 14School of Plant Sciences, BIO5 Institute,University of Arizona, Tucson, AZ 85721, USA. 15Division of Geneticsand Genomics, The Roslin Institute and Royal (Dick) School ofVeterinary Studies, The Roslin Institute Building, University ofEdinburgh, Easter Bush Campus, Midlothian EH25 9RG, UK.16Department of Veterinary Science and Microbiology, University ofArizona, 1117 E Lowell Street, Post Office Box 210090-0090, Tucson,AZ 85721, USA. 17Laboratoire de Génétique Cellulaire, INRA Cheminde Borde-Rouge, Auzeville, BP 52627 , 31326 CASTANET-TOLOSANCEDEX, France. 18Department of Biological Sciences, Science andEngineering 601, University of Arkansas, Fayetteville, AR 72701, USA.19Department of Animal Sciences, Oregon State University, Corvallis,OR 97331, USA.

SUPPLEMENTARY MATERIALS

www.sciencemag.org/content/346/6215/1311/suppl/DC1Supplementary TextFigs. S1 to S42Tables S1 to S51References (92–192)

27 January 2014; accepted 6 November 201410.1126/science.1251385

RESEARCH ARTICLE

Whole-genome analyses resolveearly branches in the tree of lifeof modern birdsErich 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 Zhang4,77†

To better determine the history of modern birds, we performed a genome-scale phylogeneticanalysis of 48 species representing all orders of Neoaves using phylogenomic methodscreated to handle genome-scale data. We recovered a highly resolved tree that confirmspreviously controversial sister or close relationships. We identified the first divergence inNeoaves, two groups we named Passerea and Columbea, representing independent lineagesof diverse and convergently evolved land and water bird species. Among Passerea, we inferthe common ancestor of core landbirds to have been an apex predator and confirm independentgains of vocal learning. Among Columbea, we identify pigeons and flamingoes as belonging tosister clades. Even with whole genomes, some of the earliest branches in Neoaves provedchallenging to resolve, which was best explained by massive protein-coding sequenceconvergence and high levels of incomplete lineage sorting that occurred during a rapidradiation after the Cretaceous-Paleogene mass extinction event about 66 million years ago.

The diversification of species is not alwaysgradual but can occur in rapid radiations,especially aftermajor environmental changes(1, 2). Paleobiological (3–7) and molecular (8)evidence suggests that such “big bang” radia-

tions occurred for neoavian birds (e.g., songbirds,parrots, pigeons, and others) and placental mam-mals, representing 95% of extant avian and mam-malian species, after the Cretaceous to Paleogene(K-Pg)mass extinction event about 66million yearsago (Ma). However, other nuclear (9–12) and mito-chondrial (13, 14) DNA studies propose an earlier,more gradual diversification, beginning withinthe Cretaceous 80 to 125 Ma. This debate is con-founded by findings that different data sets (15–19)and analytical methods (20, 21) often yield con-

trasting species trees. Resolving such timing andphylogenetic relationships is important for com-parative genomics,which can informabout humantraits and diseases (22).Recent avian studies based on fragments of 5

[~5000 base pairs (bp) (8)] and 19 [31,000 bp (17)]genes recovered some relationships inferred frommorphological data (15, 23) and DNA-DNA hy-bridization (24), postulated new relationships,and contradicted many others. Consistent withmost previous molecular and contemporary mor-phological studies (15), they divided modernbirds (Neornithes) into Palaeognathae (tinamousand flightless ratites), Galloanseres [Galliformes(landfowl) and Anseriformes (waterfowl)], andNeoaves (all other extant birds). Within Neoaves,

1320 12 DECEMBER 2014 • VOL 346 ISSUE 6215 sciencemag.org SCIENCE

A FLOCK OF GENOMES

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$binning”.$

$

$

$

$

Only48species,buttreeesEmaEontook~300CPUyearsonmulEplesupercomputersandused1Tbofmemory

90. J. F. Storz, J. C. Opazo, F. G. Hoffmann, Mol. Phylogenet. Evol.66, 469–478 (2013).

91. F. G. Hoffmann, J. F. Storz, T. A. Gorr, J. C. Opazo, Mol. Biol.Evol. 27, 1126–1138 (2010).

ACKNOWLEDGMENTS

Genome assemblies and annotations of avian genomes in thisstudy are available on the avian phylogenomics website(http://phybirds.genomics.org.cn), GigaDB (http://dx.doi.org/10.5524/101000), National Center for Biotechnology Information(NCBI), and ENSEMBL (NCBI and Ensembl accession numbersare provided in table S2). The majority of this study wassupported by an internal funding from BGI. In addition, G.Z. wassupported by a Marie Curie International Incoming Fellowshipgrant (300837); M.T.P.G. was supported by a Danish NationalResearch Foundation grant (DNRF94) and a Lundbeck Foundationgrant (R52-A5062); C.L. and Q.L. were partially supported by aDanish Council for Independent Research Grant (10-081390);and E.D.J. was supported by the Howard Hughes Medical Instituteand NIH Directors Pioneer Award DP1OD000448.

The Avian Genome ConsortiumChen Ye,1 Shaoguang Liang,1 Zengli Yan,1 M. Lisandra Zepeda,2

Paula F. Campos,2 Amhed Missael Vargas Velazquez,2

José Alfredo Samaniego,2 María Avila-Arcos,2 Michael D. Martin,2

Ross Barnett,2 Angela M. Ribeiro,3 Claudio V. Mello,4 Peter V. Lovell,4

Daniela Almeida,3,5 Emanuel Maldonado,3 Joana Pereira,3

Kartik Sunagar,3,5 Siby Philip,3,5 Maria Gloria Dominguez-Bello,6

Michael Bunce,7 David Lambert,8 Robb T. Brumfield,9

Frederick H. Sheldon,9 Edward C. Holmes,10 Paul P. Gardner,11

Tammy E. Steeves,11 Peter F. Stadler,12 Sarah W. Burge,13

Eric Lyons,14 Jacqueline Smith,15 Fiona McCarthy,16

Frederique Pitel,17 Douglas Rhoads,18 David P. Froman19

1China National GeneBank, BGI-Shenzhen, Shenzhen 518083,China. 2Centre for GeoGenetics, Natural History Museum ofDenmark, University of Copenhagen, Øster Voldgade 5-7, 1350Copenhagen, Denmark. 3CIMAR/CIIMAR, Centro Interdisciplinar deInvestigação Marinha e Ambiental, Universidade do Porto, Ruados Bragas, 177, 4050-123 Porto, Portugal. 4Department ofBehavioral Neuroscience Oregon Health & Science UniversityPortland, OR 97239, USA. 5Departamento de Biologia, Faculdadede Ciências, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal. 6Department of Biology, University of PuertoRico, Av Ponce de Leon, Rio Piedras Campus, JGD 224, San Juan,PR 009431-3360, USA. 7Trace and Environmental DNA laboratory,Department of Environment and Agriculture, Curtin University, Perth,Western Australia 6102, Australia. 8Environmental Futures ResearchInstitute, Griffith University, Nathan, Queensland 4121, Australia.9Museum of Natural Science, Louisiana State University, BatonRouge, LA 70803, USA. 10Marie Bashir Institute for InfectiousDiseases and Biosecurity, Charles Perkins Centre, School ofBiological Sciences and Sydney Medical School, The University ofSydney, Sydney NSW 2006, Australia. 11School of BiologicalSciences, University of Canterbury, Christchurch 8140, New Zealand.12Bioinformatics Group, Department of Computer Science, andInterdisciplinary Center for Bioinformatics, University of Leipzig,Hrtelstrasse 16-18, D-04107 Leipzig, Germany. 13European MolecularBiology Laboratory, European Bioinformatics Institute, Hinxton,Cambridge CB10 1SD, UK. 14School of Plant Sciences, BIO5 Institute,University of Arizona, Tucson, AZ 85721, USA. 15Division of Geneticsand Genomics, The Roslin Institute and Royal (Dick) School ofVeterinary Studies, The Roslin Institute Building, University ofEdinburgh, Easter Bush Campus, Midlothian EH25 9RG, UK.16Department of Veterinary Science and Microbiology, University ofArizona, 1117 E Lowell Street, Post Office Box 210090-0090, Tucson,AZ 85721, USA. 17Laboratoire de Génétique Cellulaire, INRA Cheminde Borde-Rouge, Auzeville, BP 52627 , 31326 CASTANET-TOLOSANCEDEX, France. 18Department of Biological Sciences, Science andEngineering 601, University of Arkansas, Fayetteville, AR 72701, USA.19Department of Animal Sciences, Oregon State University, Corvallis,OR 97331, USA.

SUPPLEMENTARY MATERIALS

www.sciencemag.org/content/346/6215/1311/suppl/DC1Supplementary TextFigs. S1 to S42Tables S1 to S51References (92–192)

27 January 2014; accepted 6 November 201410.1126/science.1251385

RESEARCH ARTICLE

Whole-genome analyses resolveearly branches in the tree of lifeof modern birdsErich 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 Zhang4,77†

To better determine the history of modern birds, we performed a genome-scale phylogeneticanalysis of 48 species representing all orders of Neoaves using phylogenomic methodscreated to handle genome-scale data. We recovered a highly resolved tree that confirmspreviously controversial sister or close relationships. We identified the first divergence inNeoaves, two groups we named Passerea and Columbea, representing independent lineagesof diverse and convergently evolved land and water bird species. Among Passerea, we inferthe common ancestor of core landbirds to have been an apex predator and confirm independentgains of vocal learning. Among Columbea, we identify pigeons and flamingoes as belonging tosister clades. Even with whole genomes, some of the earliest branches in Neoaves provedchallenging to resolve, which was best explained by massive protein-coding sequenceconvergence and high levels of incomplete lineage sorting that occurred during a rapidradiation after the Cretaceous-Paleogene mass extinction event about 66 million years ago.

The diversification of species is not alwaysgradual but can occur in rapid radiations,especially aftermajor environmental changes(1, 2). Paleobiological (3–7) and molecular (8)evidence suggests that such “big bang” radia-

tions occurred for neoavian birds (e.g., songbirds,parrots, pigeons, and others) and placental mam-mals, representing 95% of extant avian and mam-malian species, after the Cretaceous to Paleogene(K-Pg)mass extinction event about 66million yearsago (Ma). However, other nuclear (9–12) and mito-chondrial (13, 14) DNA studies propose an earlier,more gradual diversification, beginning withinthe Cretaceous 80 to 125 Ma. This debate is con-founded by findings that different data sets (15–19)and analytical methods (20, 21) often yield con-

trasting species trees. Resolving such timing andphylogenetic relationships is important for com-parative genomics,which can informabout humantraits and diseases (22).Recent avian studies based on fragments of 5

[~5000 base pairs (bp) (8)] and 19 [31,000 bp (17)]genes recovered some relationships inferred frommorphological data (15, 23) and DNA-DNA hy-bridization (24), postulated new relationships,and contradicted many others. Consistent withmost previous molecular and contemporary mor-phological studies (15), they divided modernbirds (Neornithes) into Palaeognathae (tinamousand flightless ratites), Galloanseres [Galliformes(landfowl) and Anseriformes (waterfowl)], andNeoaves (all other extant birds). Within Neoaves,

1320 12 DECEMBER 2014 • VOL 346 ISSUE 6215 sciencemag.org SCIENCE

A FLOCK OF GENOMES

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$binning”.$

$

$

$

$

ThesecondtreewascomputedbycombiningesEmatedgenetrees,andusedonly5CPUyears(serialEme),andwasembarrassinglyparallel.

12 DECEMBER 2014 • VOL 346 ISSUE 6215 1337SCIENCE sciencemag.org

INTRODUCTION: Reconstructing species

trees for rapid radiations, as in the early

diversification of birds, is complicated by

biological processes such as incomplete

lineage sorting (ILS)

that can cause differ-

ent parts of the ge-

nome to have different

evolutionary histories.

Statistical methods,

based on the multispe-

cies 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 genome-

scale species tree estimation with MP-EST,

a popular coalescent-based species tree

estimation method.

Statistical binning enables an

accurate coalescent-based estimation

of the avian tree

AVIAN GENOMICS

Siavash Mirarab, Md. Shamsuzzoha Bayzid, Bastien Boussau, Tandy Warnow*

RESEARCH ARTICLE SUMMARY

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.

Statistical binning technique

Statistical binning pipeline

Traditional pipeline (unbinned)

Sequence data

Incompatibility graph

Gene alignments

Binned supergene alignments

Estimated gene trees

Supergene trees

Species tree

Species tree

RATIONALE: In statistical binning, phy-

logenetic trees on different genes are es-

timated 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 esti-

mated 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 us-

ing 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

when the phylogenetic signal in gene se-

quence alignments was low. Species trees

estimated using MP-EST with statisti-

cal 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 re-

duces 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 sup-

port, which was the case for the avian

phylogenomics project. Because using

unbinned gene trees can result in over-

estimation 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. E-mail: [email protected] this article as S. Mirarab et al., Science 346, 1250463 (2014). DOI: 10.1126/science.1250463

Read the full article

at http://dx.doi

.org/10.1126/

science.1250463

ON OUR WEB SITE

Published by AAAS

on

Janu

ary

7, 2

015

ww

w.s

cien

cem

ag.o

rgD

ownl

oade

d fro

m

on

Janu

ary

7, 2

015

ww

w.s

cien

cem

ag.o

rgD

ownl

oade

d fro

m

on

Janu

ary

7, 2

015

ww

w.s

cien

cem

ag.o

rgD

ownl

oade

d fro

m

on

Janu

ary

7, 2

015

ww

w.s

cien

cem

ag.o

rgD

ownl

oade

d fro

m

on

Janu

ary

7, 2

015

ww

w.s

cien

cem

ag.o

rgD

ownl

oade

d fro

m

on

Janu

ary

7, 2

015

ww

w.s

cien

cem

ag.o

rgD

ownl

oade

d fro

m

on

Janu

ary

7, 2

015

ww

w.s

cien

cem

ag.o

rgD

ownl

oade

d fro

m

on

Janu

ary

7, 2

015

ww

w.s

cien

cem

ag.o

rgD

ownl

oade

d fro

m

on

Janu

ary

7, 2

015

ww

w.s

cien

cem

ag.o

rgD

ownl

oade

d fro

m

on

Janu

ary

7, 2

015

ww

w.s

cien

cem

ag.o

rgD

ownl

oade

d fro

m

We$used$100$CPU$$

years$(mostly$on$$

TACC)$to$develop$$

and$test$this$$

method.$

1kp:ThousandTranscriptomeProject

l  Firststudy(Wicke?,Mirarab,etal.,PNAS2014)had~100speciesand~800genes,genetreesandalignmentsesEmatedusingSATé,andacoalescent-basedspeciestreeesEmatedusingASTRAL

l  Secondstudy:PlantTreeofLifebasedontranscriptomesof~1200species,andmorethan13,000genefamilies(mostnotsinglecopy)

GeneTreeIncongruence

G. Ka-Shu Wong U Alberta

N. Wickett Northwestern

J. Leebens-Mack U Georgia

N. Matasci iPlant

T. Warnow, S. Mirarab, N. Nguyen, UIUC UT-Austin UT-Austin

Challenges: Species tree estimation from conflicting gene trees Gene tree estimation of datasets with > 100,000 sequences

Plus many many other people…

Hard Computational Problems

NP-hardproblemsLargedatasets

100,000+sequencesthousandsofgenes

“Bigdata”complexity:

heterogeneitymodelmisspecificaEonfragmentarysequenceserrorsininputdatastreamingdata

Twodimensions

•  Numberofgenes(ortotalnumberofsites)–  ThousandsofgenesformulE-geneanalyses–  Thousandsofsitesforsinglegenes,millionsformulE-geneanalyses

–  Sometypesofanalysescanbeparallelized•  Numberofspecies

– Manydatasetshavethousandsofspecies–  TheTreeofLifewillhavemillions– Numberoftreesonnleavesis(2n-5)!!–  Parallelismismuchmorecomplicated

Divide-and-conquer

•  Million-sequencemulEplesequencealignments•  Genome-scalephylogenyesEmaEonwithupto1,000speciesand1,000genes

•  DACTAL(almostalignment-freetreeesEmaEon)•  DCM-NJ(boosEngdistance-basedmethods)

Divide-and-conquerkeytotheimprovementsinscalabilityandaccuracy,andproducesembarrassinglyparallelalgorithms.

Today’sTalk

•  Briefoverviewofphylogenomicpipeline.•  ParallelalgorithmsinphylogeneEcs:

– Ultra-largeMulEpleSequenceAlignment

•  Themes•  Outstandingproblems

PhylogenomicPipeline



•  Foreachgene:






DNA Sequence Evolution

AAGACTT

TGGACTT AAGGCCT

-3 mil yrs

-2 mil yrs

-1 mil yrs

today

AGGGCAT TAGCCCT AGCACTT

AAGGCCT TGGACTT

TAGCCCA TAGACTT AGCGCTT AGCACAA AGGGCAT


AAGACTT

TGGACTT AAGGCCT


AAGGCCT TGGACTT

AGCGCTT AGCACAA TAGACTT TAGCCCA AGGGCAT

The Classical Phylogeny Problem

TAGCCCA TAGACTT TGCACAA TGCGCTT AGGGCAT

U V W X Y

U

V W

X

Y

AGAT TAGACTT TGCA TGCGCTT AGGGCATGA

U V W X Y

U

V W

X

Y

However…

…ACGGTGCAGTTACCA…

Mutation Deletion

…ACCAGTCACCA…

Indels (insertions and deletions)

…ACGGTGCAGTTACC-A…

…AC----CAGTCACCTA…

Thetruemul0plealignment–  Reflects historical substitution, insertion, and deletion

events –  Defined using transitive closure of pairwise alignments

computed on edges of the true tree

…ACGGTGCAGTTACCA…

Substitution Deletion

…ACCAGTCACCTA…

Insertion

Input: unaligned sequences

S1 = AGGCTATCACCTGACCTCCA S2 = TAGCTATCACGACCGC S3 = TAGCTGACCGC S4 = TCACGACCGACA

Phase 1: Alignment

S1 = -AGGCTATCACCTGACCTCCA S2 = TAG-CTATCAC--GACCGC-- S3 = TAG-CT-------GACCGC-- S4 = -------TCAC--GACCGACA


Phase 2: Construct tree



S1

S4

S2

S3

Phylogenomicpipeline



•  Foreachgene:






First Align, then Compute the Tree



S1

S4

S2

S3

Simulation Studies

S1 S2

S3 S4



S1 = -AGGCTATCACCTGACCTCCA S2 = TAG-CTATCAC--GACCGC-- S3 = TAG-C--T-----GACCGC-- S4 = T---C-A-CGACCGA----CA

Compare

True tree and alignment

S1 S4

S3 S2

Estimated tree and alignment

Unaligned Sequences

Quantifying Error

FN: false negative (missing edge) FP: false positive (incorrect edge) 50% error rate

FN

FP

Two-phaseesEmaEonAlignmentmethods•  Clustal•  POY(andPOY*)•  Probcons(andProbtree)•  Probalign•  MAFFT•  Muscle•  Di-align•  T-Coffee•  Prank(PNAS2005,Science2008)•  Opal(ISMBandBioinf.2007)•  FSA(PLoSComp.Bio.2009)•  Infernal(Bioinf.2009)•  Etc.

Phylogenymethods•  BayesianMCMC•  Maximumparsimony•  Maximumlikelihood•  Neighborjoining•  FastME•  UPGMA•  Quartetpuzzling•  Etc.

RAxML:heuris(cforlarge-scaleMLop(miza(on

1000-taxonmodels,orderedbydifficulty(Liuetal.,2009)

Large-scale Alignment Estimation

•  ManygenesareconsideredunalignableduetohighratesofevoluEon

•  Onlyafewmethodscananalyzelargedatasets

•  AlignmenterrorincreaseswithrateofevoluEon,andresultsintreeesEmaEonerror

Multiple Sequence Alignment (MSA): a scientific grand challenge1

S1 = -AGGCTATCACCTGACCTCCA S2 = TAG-CTATCAC--GACCGC-- S3 = TAG-CT-------GACCGC-- … Sn = -------TCAC--GACCGACA

S1 = AGGCTATCACCTGACCTCCA S2 = TAGCTATCACGACCGC S3 = TAGCTGACCGC … Sn = TCACGACCGACA

Novel techniques needed for scalability and accuracy NP-hard problems and large datasets Current methods do not provide good accuracy Few methods can analyze even moderately large datasets Many important applications besides phylogenetic estimation

1 Frontiers in Massive Data Analysis, National Academies Press, 2013

Large-scale Alignment Estimation

•  ManygenesareconsideredunalignableduetohighratesofevoluEon

•  Onlyafewmethodscananalyzelargedatasets

•  AlignmenterrorincreaseswithrateofevoluEon,andresultsintreeesEmaEonerror

1000-taxonmodels,orderedbydifficulty(Liuetal.,2009)

Re-aligning on a tree A

B D

C

Merge sub-alignments

Estimate ML tree on merged

alignment

Decompose dataset

A B

C D

Align subsets

A B

C D

ABCD

SATéandPASTAAlgorithms

Estimate ML tree on new alignment

Tree

Obtain initial alignment and estimated ML tree

Use tree to compute new alignment

Alignment

RepeatunElterminaEoncondiEon,and

returnthealignment/treepairwiththebestMLscore

SATé-1:Liuetal.,Science2009;SATé-2,Liuetal.,SystemaEcBiology2012;PASTA:Mirarabetal.,RECOMB2014andJ.ComputaEonalBiology2014

1000-taxonmodels,rateofevoluEongenerallyincreasesfromlertoright

SATé-1isbasedonMAFFTtoalignsubsets.Resultsshownarefora24-houranalysis,ondesktopmachines.

Similarimprovementsseenforbiologicaldatasets.

SATé-1cananalyzeuptoabout8,000sequences.

SATé-1(Science2009)performance

SATé-I decomposition (clades) A

B D

C



alignment

Decompose dataset

A B

C D

Align subsets

A B

C D

ABCD

SATé-II:centroidedgedecomposiEon

A

B

C

D

E

ABCDE

ABC

AB

A B

C

DE

D E

SATé-IImakesallsubsetssmall(userparameter),andcananalyze50Ksequences.

(RecallthattheSATé-IdecomposiEonproducedcladesand

hadbiggersubsets;limitedto8Ksequences.)

1000-taxonmodelsrankedbydifficulty

SATé-1andSATé-2(Systema0cBiology,2012)

SATé-1:upto8KSATé-2:upto~50K

SATé-II running time profiling ��

�

��

��

�

��

��

��

��

��

� � ��

��

SATé-II running time profiling

�

�

�

�

�

� � � � � ��

��

��

��

�

��

��

�

��

��

��

��

��

� � ��

��

PASTA: SATé-II with a new merger technique

A

B D

C



alignment

Decompose dataset

A B

C D

Align subsets

A B

C D

ABCD

SATé:mergerstrategy

A

B

C

D

E

ABCDE

ABC

AB

A B

C

DE

D E

BothSATé’susethesamehierarchicalmergerstrategy.Onlarge(50K)datasets,thelastpairwisemergercanuse

morethan70%oftherunningEme

A

B

C

D

E

PASTAmerging:Step1

D

C

EBA

ComputeaspanningtreeconnecEngalignmentsubsets

A

B

C

D

E

PASTAmerging:Step2

D

C

EBA

AB

BD

CD

DE

ABBD

CD

DE

UseOpal(orMuscle)tomergeadjacentsubsetalignmentsinthespanningtree

PASTAmerging:Step3

D

C

EBA

UsetransiEvitytomergeallpairwise-mergedalignmentsfromStep2intofinalanalignmentonenEredataset

AB+BD=ABDABD+CD=ABCDABCD+DE=ABCDE

ABBD

CD

DE

SATé-II running time profiling

�

�

�

�

�

� � � � � ��

��

��

��

�

��

��

�

��

��

��

��

��

� � ��

��

1

2

4

6

8

1 2 4 6 8 10 12Number of Threads

Speedup

SATe2

PASTAvs.SATé-IIprofilingandscaling10 PASTA: ultra-large multiple sequence alignment

(a)

●

●

●

●

●

●

●

●

0

250

500

750

1000

1250

10,000 50,000 100,000 200,000Number of Sequences

Run

ning

tim

e (m

inut

es)

(b)

●●

●●

●

●

●

●

●

●

●

●

●

●

1

2

4

6

8


Spee

dup

●

●

PASTASATe2

(c)

Fig. 5. Running time comparison of PASTA and SATe. (a) Running time pro-filing on one iteration for RNASim datasets with 10K and 50K sequences (the dottedregion indicates the last pairwise merge). (b) Running time for one iteration of PASTAwith 12 CPUs as a function of the number of sequences (the solid line is fitted to firsttwo points). (c) Scalability for PASTA and SATe with increased number of CPUs.

reason SATe uses so much time is that all mergers are done hierarchically usingeither Opal (for small datasets) or Muscle (on larger datasets), and both arecomputationally expensive with increased number of sequences. For example,the last pairwise merge within SATe, shown by the dotted area in Figure 5a,is entirely serial and takes up a large chunk of the total time. PASTA solvesthis problem by using transitivity for all but the initial pairwise mergers, andtherefore scales well with increased dataset size, as shown in Figure 5b (thesub-linear scaling is due to a better use of parallelism with increased number ofsequences). Finally, Figure 5c shows that PASTA is highly parallelizable, andhas a much better speed-up with increasing number of threads than SATe does.While PASTA has a much improved parallelization, it does not quite scale uplinearly, because FastTree-2 does not scale up well with increased thread count.

Divide-and-Conquer strategy: impact of guide tree. We also investigated theimpact of the use of the guide tree for computing the subset decomposition,and hence defining the Type 1 sub-alignments. We compared results obtainedusing three di↵erent decompositions: the decomposition computed by PASTAon the HMM-based starting tree, the decomposition computed by PASTA onthe true (model) tree, and a random decomposition into subsets of size 200,all on the RNASim 10k dataset. PASTA alignments and trees had roughly thesame accuracy when the guide tree was either the true tree or the HMM-basedstarting tree (Table 3). However, when based on a random decomposition, treeerror increased dramatically from 10.5% to 52.3%, and alignment scores alsodropped substantially. Thus, the guide-tree based dataset decomposition usedby PASTA provides substantial improvements over random decompositions, andthe default technique for getting the starting tree works quite well.

10000 50000

0

50

100

0

1000

2000

3000

Running

Time(minutes)

MergeSubsetAlignment Tree

PASTA SATe-IIPASTA SATe-II

PASTA Running Time and Scalability

10 PASTA: ultra-large multiple sequence alignment

(a)

●

●

●

●

●

●

●

●

0

250

500

750

1000

1250

10,000 50,000 100,000 200,000Number of Sequences

Run

ning

tim

e (m

inut

es)

(b)

●●

●●

●

●

●

●

●

●

●

●

●

●

1

2

4

6

8


Spee

dup

●

●

PASTASATe2

(c)

Fig. 5. Running time comparison of PASTA and SATe. (a) Running time pro-filing on one iteration for RNASim datasets with 10K and 50K sequences (the dottedregion indicates the last pairwise merge). (b) Running time for one iteration of PASTAwith 12 CPUs as a function of the number of sequences (the solid line is fitted to firsttwo points). (c) Scalability for PASTA and SATe with increased number of CPUs.

reason SATe uses so much time is that all mergers are done hierarchically usingeither Opal (for small datasets) or Muscle (on larger datasets), and both arecomputationally expensive with increased number of sequences. For example,the last pairwise merge within SATe, shown by the dotted area in Figure 5a,is entirely serial and takes up a large chunk of the total time. PASTA solvesthis problem by using transitivity for all but the initial pairwise mergers, andtherefore scales well with increased dataset size, as shown in Figure 5b (thesub-linear scaling is due to a better use of parallelism with increased number ofsequences). Finally, Figure 5c shows that PASTA is highly parallelizable, andhas a much better speed-up with increasing number of threads than SATe does.While PASTA has a much improved parallelization, it does not quite scale uplinearly, because FastTree-2 does not scale up well with increased thread count.

Divide-and-Conquer strategy: impact of guide tree. We also investigated theimpact of the use of the guide tree for computing the subset decomposition,and hence defining the Type 1 sub-alignments. We compared results obtainedusing three di↵erent decompositions: the decomposition computed by PASTAon the HMM-based starting tree, the decomposition computed by PASTA onthe true (model) tree, and a random decomposition into subsets of size 200,all on the RNASim 10k dataset. PASTA alignments and trees had roughly thesame accuracy when the guide tree was either the true tree or the HMM-basedstarting tree (Table 3). However, when based on a random decomposition, treeerror increased dramatically from 10.5% to 52.3%, and alignment scores alsodropped substantially. Thus, the guide-tree based dataset decomposition usedby PASTA provides substantial improvements over random decompositions, andthe default technique for getting the starting tree works quite well.

•  One iteration

•  Using •  12 cpus •  1 node on Lonestar TACC •  Maximum 24 GB memory

•  Showing wall clock running time •  ~ 1 hour for 10k taxa •  ~ 17 hours for 200k taxa

MassiveParallelisminPASTA•  Divisionstep:veryfast(notworsethanO(n2))•  So1,000,000-sequencedatasetbecomes:

–  ~5000subsetsof200sequenceseach–  Eachanalyzedindependently–  Cantailorsubsetanalysistofeaturesofthedata

•  Mergingstep:veryfastandalsomassivelyparallel(independentpairwisemergers,thentransiEvity)

TheonlypartofPASTAthatisn’twellparallelizedisthetreeesEmaEonstepineachiteraEon!

RNASim

0.00

0.05

0.10

0.15

0.20

10000 50000 100000 200000

Tree

Erro

r (FN

Rat

e) Clustal−OmegaMuscleMafftStarting TreeSATe2PASTAReference Alignment

PASTA:Mirarab,Nguyen,andWarnow,JComp.Biol.2015–  SimulatedRNASimdatasetsfrom10Kto200Ktaxa–  Limitedto24hoursusing12CPUs–  Notallmethodscouldrun(missingbarscouldnotfinish)

PASTA:evenbe?erthanSATé-2

RNASimMillionSequences:treeerror

Using 12 TACC processors: •  UPP(Fast,NoDecomp)

took 2.2 days,

•  UPP(Fast) took 11.9 days, and

•  PASTA took 10.3 days

UPP:Nguyenetal.RECOMB2015andGenomeBiology2015(alsousesdivide-and-conquerandishighlyparallelizable.)

PASTAandSATé-II:MSA“boosters”

•  PASTAandSATé-IIaretechniquesforimprovingthescalabilityofMSAmethodstolargedatasets.

•  WeshowedresultshereusingMAFFT-l-ins-itoalignsmallsubsetswith200sequences.

GoldStandard:StaEsEcalco-esEmaEon

•  Improvedaccuracycanbeobtainedthroughco-esEmaEonofalignmentsandtrees.

•  BAli-Phy(RedelingsandSuchard,2005),aBayesianmethod,istheleadingco-esEmaEonmethod.

•  However,BAli-Phyislimitedtosmalldatasets(atmost100sequences),andeventheseanalysescantakeweeks(duetoconvergenceissues).

•  WeintegratedBAli-PhyintoPASTA(replacingMAFFT),withdecomposiEonstoatmost100sequences.

DecomposiEonto100-sequencesubsets,oneiteraEonofPASTA+BAli-Phy

ComparingdefaultPASTAtoPASTA+BAli-Phyonsimulateddatasetswith1000sequences

PASTA+BAli−Phy Better

PASTA Better

0.0

0.1

0.2

0.3

0.4

0.0 0.1 0.2 0.3 0.4PASTA

PAST

A+BA

li−Ph

y

Total Column Score

dataIndelible M2RNAsimRose L1Rose M1Rose S1


PASTA Better

0.6

0.7

0.8

0.9

1.0

0.6 0.7 0.8 0.9 1.0PASTA

PAST

A+BA

li−Ph

y

Recall (SP−Score)


PASTA Better

0.00

0.05

0.10

0.15

0.00 0.05 0.10 0.15PASTA+BAli−Phy

PAST

A

Tree Error: Delta RF (RAxML)

SATéandPASTAAlgorithms

Estimate ML tree on new alignment

Tree

Obtain initial alignment and estimated ML tree

Use tree to compute new alignment

Alignment

RepeatunElterminaEoncondiEon,and

returnthealignment/treepairwiththebestMLscore

MajorOpenProblem

•  ScalablemaximumlikelihoodtreeesEmaEon–  Input:MulEplesequencealignment(andspecifiedmodel)

– Output:Treeandnumericparameterstomaximizeprobabilityofthesequencesunderthemodel

Numberofsites(sequencelength)

Num

bero

fspe

cies

MaximumLikelihoodHeurisEcs

MaximumLikelihood(NP-hard):InputisamulEplesequencealignment,OutputisatreemaximizingtheprobabilityofgeneraEngtheinputsequences.


Num

bero

fspe

cies

Lotsofmethods




Num

bero

fspe

cies

LotsofmethodsParallelismworks(e.g.,ExaML,Stamatakisetal.)




Num

bero

fspe

cies


FastheurisEcs(e.g.,FastTree-2,Priceetal.)




Num

bero

fspe

cies


FastheurisEcs(e.g.,FastTree-2,Priceetal.)


?MaximumLikelihood(NP-hard):

InputisamulEplesequencealignment,OutputisatreemaximizingtheprobabilityofgeneraEngtheinputsequences.

MajorOpenProblems

•  ScalablemaximumlikelihoodtreeesEmaEon–  Input:MulEplesequencealignment(andspecifiedmodel)

– Output:Treeandnumericparameterstomaximizeprobabilityofthesequencesunderthemodel

– Comments:RAxMLcannotanalyzelargenumbersofsequencesefficiently;FastTreecannotanalyzelongalignments(andhaspoorparallelism)

SecondMajorOpenProblemSupertreeesEmaEon•  Input:SetTofunrootedtreesonsubsetsofS•  Output:TreeTthatminimizestotaldistancetotreesinTComments:•  BasicprobleminphylogeneEcs.•  Keyalgorithmicstepindivide-and-conquertreeesEmaEon

methods.•  ThebestcurrentmethodsrelyonheurisEcsforNP-hard

opEmizaEonproblems,andsocannotscaletolargedatasets(foreitherdimensionoflarge!).

DACTAL

Supertree Estimation

Compute trees on each subset

Decompose

BLAST-based

Overlapping subsets

A tree for each subset

Unaligned Sequences

A tree for the entire dataset

DACTALcomparedtoSATéandstandardmethods

16S.Tdataset7350sequencesfromtheCRW(ComparaEveRibosomalDatabase)

ObservaEons•  HighlyaccuratestaEsEcalmethods(especiallymaximumlikelihoodandBayesianmethods)havebeendevelopedformanyproblems.

•  However,thesemethodsweretypicallydesignedforsmalldatasets,andeitherdonotrunonlargedatasets,taketoolong,orhavepooraccuracy.

•  RelaEveperformanceofmethodscanchangewithdatasetsizeandheterogeneity!

•  Butappropriatedivide-and-conquerstrategiescanmakethemscaletolargedatasets,andbemassivelyparallel.

Summary

•  Divide-and-conquerinphylogeneEcsandmulEplesequencealignmentisverypowerful,andcanleadtoimprovedaccuracyandscalability.

•  Theingredientsofthesestrategiesare:–  ExisEngtree-basedapproachesforthedecomposiEonstep–  ExisEngstaEsEcalmethodsforanalyzingsubsets(orencomputaEonallyintensive)

–  CombiningsoluEonsfromsubsetsiswheretheresearchisneeded!

•  DistributedcompuEngisnecessaryforsomedatasets

Scientific challenges: •  Ultra-large multiple-sequence alignment •  Gene tree estimation •  Metagenomic classification •  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

Techniques: applied probability theory, graph theory, supercomputing, and heuristics Testing: simulations and real data

The Tree of Life: Multiple Challenges

Acknowledgments

PASTA:NamNguyen(nowpostdocatUIUC)andSiavashMirarab(nowfacultyatUCSD),undergrad:KeerthanaKumar(atUT-AusEn)PASTA+BAli-Phy:MikeNute(PhDstudentatUIUC)DACTAL:SeritaNelesen(nowprofessoratCalvinCollege)CurrentNSFgrants:ABI-1458652(mulEplesequencealignment)GraingerFounda0on(atUIUC),andUIUCTACC,UTCS,BlueWaters,andUIUCcampusclusterPASTAisavailableongithubath?ps://github.com/smirarab/pasta;seealsoPASTA+BAli-Phyath?p://github.com/MGNute/pasta

computa(onal challenges in construcng the tree of lifeipdps.org/ipdps2017/warnow-ipdps2017.pdf ·...

Documents