1. Advances in Ultra-large Phylogeny Estimation
Tandy Warnow
Department of Computer Science
University of Texas

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

3. How did life evolve on earth?
Courtesy of the Tree of Life project

4. Where did humans come from, and how did they move throughout the globe?
The 1000 Genome Project: using human genetic variation to better treat diseases

5. Metagenomics:
C. Venter et al., Exploring the Sargasso Sea:
Scientists Discover One Million New Genes in Ocean Microbes

6. Major Challenges
Current phylogenetic datasets contain hundreds to thousands of taxa, with multiple genes.
Future datasets will be substantially larger (e.g., iPlant plans to construct a tree on 500,000 plant species)
Current methods have poor accuracy or cannot run on large datasets.

7. Computational Phylogenetics
Current methods can use months to
estimate trees on 1000 DNA sequences
Our objective:
More accurate trees and alignments
on 500,000 sequences in under a week
We prove theorems using graph theory
and probability theory, and our
algorithms are studied on real and
simulated data.
Courtesy of the Tree of Life project

8. DNA Sequence Evolution
-3 mil yrs
-2 mil yrs
-1 mil yrs
today
AAGACTT
TGGACTT
AAGGCCT
AGGGCAT
TAGCCCT
AGCACTT
AGCGCTT
AGCACAA
TAGACTT
TAGCCCA
AAGACTT
TGGACTT
AAGGCCT
AGGGCAT
TAGCCCT
AGCACTT
AAGGCCT
TGGACTT
TAGCCCA
TAGACTT
AGCGCTT
AGCACAA
AGGGCAT
TAGCCCT
AGCACTT

9. …ACGGTGCAGTTACCA… …ACGGTGCAGTTACC-A… …AC----CAGTCACCTA… …ACCAGTCACCTA…
Deletion
Substitution
…ACGGTGCAGTTACCA…
Insertion
…ACGGTGCAGTTACC-A…
…AC----CAGTCACCTA…
…ACCAGTCACCTA…
The true multiple alignment
Reflects historical substitution, insertion, and deletion events
Defined using transitive closure of pairwise alignments computed on edges of the true tree
Homology = nucleotides lined up since they come from a common ancestor.
Indel = dash. 9

10. U V W X Y
AGGGCATGA
AGAT
TAGACTT
TGCACAA
TGCGCTT X U Y V W

11. Input: unaligned sequences
S1 = AGGCTATCACCTGACCTCCA
S2 = TAGCTATCACGACCGC
S3 = TAGCTGACCGC
S4 = TCACGACCGACA

12. Phase 1: Multiple Sequence Alignment
S1 = AGGCTATCACCTGACCTCCA
S2 = TAGCTATCACGACCGC
S3 = TAGCTGACCGC
S4 = TCACGACCGACA
S1 = -AGGCTATCACCTGACCTCCA
S2 = TAG-CTATCAC--GACCGC--
S3 = TAG-CT GACCGC--
S4 = TCAC--GACCGACA

13. Phase 2: Construct tree S1 = AGGCTATCACCTGACCTCCA
S2 = TAGCTATCACGACCGC
S3 = TAGCTGACCGC
S4 = TCACGACCGACA
S1 = -AGGCTATCACCTGACCTCCA
S2 = TAG-CTATCAC--GACCGC--
S3 = TAG-CT GACCGC--
S4 = TCAC--GACCGACA S1 S2 S4 S3

14. Simulation Studies Unaligned Sequences Compare True tree and alignment
S1 = AGGCTATCACCTGACCTCCA
S2 = TAGCTATCACGACCGC
S3 = TAGCTGACCGC
S4 = TCACGACCGACA
Unaligned Sequences
S1 = -AGGCTATCACCTGACCTCCA
S2 = TAG-CTATCAC--GACCGC--
S3 = TAG-CT GACCGC--
S4 = TCAC--GACCGACA
S1 = -AGGCTATCACCTGACCTCCA
S2 = TAG-CTATCAC--GACCGC--
S3 = TAG-C--T-----GACCGC--
S4 = T---C-A-CGACCGA----CA S1 S2 S3 S4 S1 S4 S3 S2
Compare
True tree and alignment
Estimated tree and alignment

15. The neighbor joining method has high error rates on large trees
Simulation study based upon fixed edge lengths, K2P model of evolution, sequence lengths fixed to 1000 nucleotides.
Error rates reflect proportion of incorrect edges in inferred trees.
[Nakhleh et al. ISMB 2001]
0.8 NJ
0.6
Error Rate
0.4
0.2
400
800
1200
1600
No. Taxa

16. 1000 taxon models, ordered by difficulty (Liu et al., 2009)
1. 2 classes of MC: easy, moderate-to-difficult
2. true alignment
3. 2 classes: ClustalW, everything else
Alignment error, measured this way, isn't a perfect predictor of tree error, measured this way.
1000 taxon models, ordered by difficulty (Liu et al., 2009) 16

17. Problems
Large datasets with high rates of evolution are hard to align accurately, and phylogeny estimation methods produce poor trees when alignments are poor.
Many phylogeny estimation methods have poor accuracy on large datasets (even if given correct alignments)
Potentially useful genes are often discarded if they are difficult to align.
These issues seriously impact large-scale phylogeny estimation (and Tree of Life projects)

18. Major Challenges
Current phylogenetic datasets contain hundreds to thousands of taxa, with multiple genes.
Future datasets will be substantially larger (e.g., iPlant plans to construct a tree on 500,000 plant species)
Current methods have poor accuracy or cannot run on large datasets.

19. Phylogenetic “boosters” (meta-methods)
Goal: improve accuracy, speed, robustness, or theoretical guarantees of base methods
Examples:
DCM-boosting for distance-based methods (1999)
DCM-boosting for heuristics for NP-hard problems (1999)
SATé-boosting for alignment methods (2009)
SuperFine-boosting for supertree methods (2011)
DACTAL-boosting for all phylogeny estimation methods (2011)
SEPP-boosting for metagenomic analyses (2011)

20. Disk-Covering Methods (DCMs) (starting in 1998)

21. DCMs “boost” the performance of phylogeny reconstruction methods.
Base method M
DCM-M

22. The neighbor joining method has high error rates on large trees
Simulation study based upon fixed edge lengths, K2P model of evolution, sequence lengths fixed to 1000 nucleotides.
Error rates reflect proportion of incorrect edges in inferred trees.
[Nakhleh et al. ISMB 2001]
0.8 NJ
0.6
Error Rate
0.4
0.2
400
800
1200
1600
No. Taxa

23. DCM1-boosting distance-based methods [Nakhleh et al. ISMB 2001]
Theorem: DCM1-NJ converges to the true tree from polynomial length sequences
0.8 NJ
DCM1-NJ
0.6
Error Rate
0.4
0.2
400
800
1200
1600
No. Taxa

24. Today’s Talk
SATé: Simultaneous Alignment and Tree Estimation (Liu et al., Science 2009, and Liu et al. Systematic Biology, in press)
DACTAL: Divide-and-Conquer Trees without alignments (Nelesen et al., submitted)

25. Part 1: SATé
Liu, Nelesen, Raghavan, Linder, and Warnow, Science, 19 June 2009, pp
Liu et al., Systematic Biology (in press)
Public software distribution (open source) through the University of Kansas, in use, world-wide

26. SATé Algorithm
Tree
Obtain initial alignment and estimated ML tree

27. SATé Algorithm Obtain initial alignment and estimated ML tree Tree
Use tree to compute new alignment
Alignment

28. SATé Algorithm Obtain initial alignment and estimated ML tree Tree
Estimate ML tree on new alignment
Use tree to compute new alignment
Alignment

29. SATé Algorithm Obtain initial alignment and estimated ML tree Tree
Estimate ML tree on new alignment
Use tree to compute new alignment
Alignment
If new alignment/tree pair has worse ML score, realign using a different decomposition
Repeat until termination condition (typically, 24 hours)

30. One SATé iteration (really 32 subsets)D C
Merge subproblems
Estimate ML tree on merged alignment
Decompose based on input tree
Align subproblems
ABCD e

31. 1000 taxon models, ordered by difficulty
Say the only thing changing here is the alignment method. 31

32. 1000 taxon models, ordered by difficulty
For moderate-to-difficult datasets, SATe gets better trees and alignments than all other estimated methods. Close to what you might get if you had access to true alignment.
Opens up a new realm of possibility: Datasets currently considered “unalignable” can in fact be aligned reasonably well. This opens up the feasibility of accurate estimations of deep evolutionary histories using a wider range of markers.
TRANSITION: can we do better? What about smaller simulated datasets? And what about biological datasets?
24 hour SATé analysis, on desktop machines
(Similar improvements for biological datasets)

33. 1000 taxon models ranked by difficulty

34. Limitations of SATé-I and -IIB D C A B
Decompose dataset C D
Align subproblems A B C D
Estimate ML tree on merged alignment
ABCD
Merge sub-alignments

35. Part II: DACTAL (Divide-And-Conquer Trees (Almost) without alignments)
Input: set S of unaligned sequences
Output: tree on S (but no alignment)
(Nelesen, Liu, Wang, Linder, and Warnow, submitted)

36. DACTAL BLAST-based Existing Method: RAxML(MAFFT) pRecDCM3
Unaligned Sequences
Overlapping
subsets
pRecDCM3
A tree for each subset
New supertree method: SuperFine
A tree for the entire dataset

37. Average of 3 Largest CRW Datasets
CRW: Comparative RNA database,
Three 16S datasets with 6,323 to 27,643 sequences
Reference alignments based on secondary structure
Reference trees are 75% RAxML bootstrap trees
DACTAL (shown in red) run for 5 iterations starting from FT(Part)
FastTree (FT) and RAxML are ML methods

38. Observations
DACTAL gives more accurate trees than all other methods on the largest datasets
DACTAL is much faster than SATé
DACTAL is robust to starting trees and other algorithmic parameters

39. Summary
Standard alignment and phylogeny estimation methods do not provide adequate accuracy on large datasets, and NGS data present novel challenges
When markers tend to yield poor alignments and trees, develop better methods - don’t throw out the data.

40. Current Research Projects
Method development:
Large-scale multiple sequence alignment and phylogeny estimation
Metagenomics
Comparative genomics
Estimating species trees from gene trees
Supertree methods
Phylogenetic estimation under statistical models
Dataset analyses (multi-institutional collaborations):
Avian Phylogeny (and brain evolution)
Human Microbiome
Thousand Transcriptome (1KP) Project
Conifer evolution

41. Acknowledgments
Guggenheim Foundation Fellowship, Microsoft Research New England, National Science Foundation: Assembling the Tree of Life (ATOL), ITR, and IGERT grants, and David Bruton Jr. Professorship
Collaborators:
SATé: Kevin Liu, Serita Nelesen, Sindhu Raghavan, and Randy Linder
DACTAL: Serita Nelesen, Li-San Wang, and Randy Linder

42. Part III: SEPP
SEPP: SATé-enabled Phylogenetic Placement, by Mirarab, Nguyen, and Warnow
To appear, Pacific Symposium on Biocomputing, 2012 (special session on the Human Microbiome)

43. Metagenomic data analysis
NGS data produce fragmentary sequence data
Metagenomic analyses include unknown species
Taxon identification: given short sequences, identify the species for each fragment
Applications: Human Microbiome
Issues: accuracy and speed

44. Metagenomics Input: set of sequences
Output: a tree on the set of sequences, indicating the species identification of each sequence
Issue: the sequences are not globally alignable, and there are often thousands (or more) of the sequences

45. Phylogenetic Placement
Input: Backbone alignment and tree on full- length sequences, and a set of query sequences (short fragments)
Output: Placement of query sequences on backbone tree
Phylogenetic placement can be used for taxon identification, but it has general applications for phylogenetic analyses of NGS data.
The two basic steps in phylogenetic placement is similar to the two phase methods we typically use, align and then place the fragment.
The differences are we align each fragment independently into the backbone tree. We then take each extended alignment and place each fragment independently into the backbone tree.
Two methods to align the query sequences are HMMALIGN, which estimates a hidden markov model profile for the backbone alignment, and then aligns the query sequence to the model, and PaPaRa, which estimates ancestoral parsimony state vectors for each each in the reference tree and aligns the query sequence against each state vector. It selects the best scoring alignment.
Two methods that place the query sequence try to optimize the same criterion: find the ML placement given the extended alignment

46. Phylogenetic Placement
Align each query sequence to backbone alignment
Place each query sequence into backbone tree, using extended alignment
The two basic steps in phylogenetic placement is similar to the two phase methods we typically use, align and then place the fragment.
The differences are we align each fragment independently into the backbone tree. We then take each extended alignment and place each fragment independently into the backbone tree.
Two methods to align the query sequences are HMMALIGN, which estimates a hidden markov model on backbone alignemnt, then aligns query sequence. PaPaRa, which estimates ancestoral parsimony state vectors for each each in the reference tree and aligns the query sequence against each state vector. It selects the best scoring alignment.
Two methods that place the query sequence try to optimize the same criterion: find the ML placement given the extended alignment

47. Align Sequence S1 S2 S3 S4 S1 = -AGGCTATCACCTGACCTCCA-AA
S2 = TAG-CTATCAC--GACCGC--GCA
S3 = TAG-CT GACCGC--GCT
S4 = TAC----TCAC--GACCGACAGCT
Q1 = TAAAAC S1 S2 S3 S4

48. Align Sequence S1 S2 S3 S4 S1 = -AGGCTATCACCTGACCTCCA-AA
S2 = TAG-CTATCAC--GACCGC--GCA
S3 = TAG-CT GACCGC--GCT
S4 = TAC----TCAC--GACCGACAGCT
Q1 = T-A--AAAC S1 S2 S3 S4

49. Place Sequence S1 S2 S3 S4 S1 = -AGGCTATCACCTGACCTCCA-AA
S2 = TAG-CTATCAC--GACCGC--GCA
S3 = TAG-CT GACCGC--GCT
S4 = TAC----TCAC--GACCGACAGCT
Q1 = T-A--AAAC S1 S2 S3 S4 Q1

50. Phylogenetic Placement
Align each query sequence to backbone alignment
HMMALIGN (Eddy, Bioinformatics 1998)
PaPaRa (Berger and Stamatakis, Bioinformatics 2011)
Place each query sequence into backbone tree
Pplacer (Matsen et al., BMC Bioinformatics, 2011)
EPA (Berger and Stamatakis, Systematic Biology 2011)
Note: pplacer and EPA use maximum likelihood

51. HMMER vs. PaPaRa Alignments
0.0
Increasing rate of evolution

52. Insights from SATé

53. Insights from SATé

54. Insights from SATé

55. Insights from SATé

56. Insights from SATé

57. SEPP Parameter Exploration
Alignment subset size and placement subset size impact the accuracy, running time, and memory of SEPP
10% rule (subset sizes 10% of backbone) had best overall performance

58. SEPP (10%-rule) on simulated data
0.0
0.0
Increasing rate of evolution

59. SEPP (10%) on Biological Data
16S.B.ALL dataset, 13k curated backbone tree, 13k total fragments

60. SEPP (10%) on Biological Data
16S.B.ALL dataset, 13k curated backbone tree, 13k total fragments
For 1 million fragments:
PaPaRa+pplacer: ~133 days
HMMALIGN+pplacer: ~30 days
SEPP 1000/1000: ~6 days

61. Three “Boosters” SATé: co-estimation of alignments and trees
DACTAL: large trees without full alignments
SEPP: phylogenetic analysis of fragmentary data
Algorithmic strategies: divide-and-conquer and iteration to improve the accuracy and scalability of a base method

62. Red gene tree ≠ species tree (green gene tree okay)

63. Multi-marker species tree estimation
Species phylogenies are estimated using multiple gene trees. Most methods assume that all gene trees are identical to the species tree.
This is known to be unrealistic in some situations, due to processes such as
Deep Coalescence
Gene duplication and loss
Horizontal gene transfer
MDC problem: Given set of gene trees, find a species tree that minimizes the total number of “deep coalescences”.

64. Yu, Warnow and Nakhleh, 2011
Previous software for MDC assumed all gene trees are correct, completely resolved, and rooted.
Our methods allow for error in estimated gene trees.
We provide exact algorithms and heuristics to find an optimal species tree with respect to a given set of partially resolved, unrooted gene trees, minimizing the total number of deep coalescences.
Software at
To appear, RECOMB 2011 and J. Computational Biology, special issue for RECOMB 2011.
Talk about this topic today at 2 PM in OEB.

65. Markov Model of Site Evolution
Simplest (Jukes-Cantor):
The model tree T is binary and has substitution probabilities p(e) on each edge e.
The state at the root is randomly drawn from {A,C,T,G} (nucleotides)
If a site (position) changes on an edge, it changes with equal probability to each of the remaining states.
The evolutionary process is Markovian.
More complex models (such as the General Markov model) are also considered, often with little change to the theory.

66. SATé-I vs. SATé-II SATé-II Faster and more accurate than SATé-I
Longer analyses or use of ML to select tree/alignment pair slightly better results

67. Divergence & Information Content
Average Pairwise Sequence Divergence
Percent Informative Sites
100 50 25 75
0.00
0.05
0.10
0.15
0.20
0.25
0.30
Exons
UTRs
Introns
The rate of evolution is important, because as sequence divergence increases, so does the amount of phylogenetic information the sequences can contain. Protein-coding exons, which rarely reach average pairwise sequence divergence above 10% in our data, generally have between 15 and 30% of sites that are phylogenetically informative. For introns, with 10-30% sequence divergence, the percentage of informative sites is much higher. The UTRs we have looked at tend to fall in between.
Analysis and figure provided by Mike Braun
Smithsonian Institution 67

68. Reticulate evolution Not all evolution is tree-like:
Horizontal gene transfer
Hybrid speciation
How can we detect reticulate evolution?

70. U V W X Y
AGGGCAT
TAGCCCA
TAGACTT
TGCACAA
TGCGCTT X U Y V W

71. Two-phase estimation Phylogeny methods Bayesian MCMC Maximum parsimony
Maximum likelihood
Neighbor joining
FastME
UPGMA
Quartet puzzling
Etc.
Alignment methods
Clustal
POY (and POY*)
Probcons (and Probtree)
Probalign
MAFFT
Muscle
Di-align
T-Coffee
Prank (PNAS 2005, Science 2008)
Opal (ISMB and Bioinf. 2007)
FSA (PLoS Comp. Bio. 2009)
Infernal (Bioinf. 2009)
Etc.
RAxML: heuristic for large-scale ML optimization 71

72. Software In use by research groups around the world
Kansas SATé software developers: Mark Holder, Jiaye Yu, and Jeet Sukumaran
Downloadable software for various platforms
Easy-to-use GUI

73. Quantifying Error FN: false negative (missing edge) FP: false positive
(incorrect edge)
50% error rate FP

74. Understanding SATé
Observations: (1) subsets of taxa that are small enough, closely related, and densely sampled are aligned more accurately than others.
SATé-1 produces subsets that are closely related and densely sampled, but not small enough.
SATé-2 (“next SATé”) changes the design to produce smaller subproblems.
The next iteration starts with a more accurate tree. This leads to a better alignment, and a better tree.

75. Biology: 21st Century Science!
“When the human genome was sequenced seven years ago, scientists knew that most of the major scientific discoveries of the 21st century would be in biology.”
January 1, 2008, guardian.co.uk

76. Genome Sequencing Projects:
Started with the Human Genome Project

77. Whole Genome Sequencing:
Graph Algorithms and Combinatorial Optimization!

78. Other Genome Projects! (Neandertals, Wooly Mammoths, and more ordinary creatures…)

79. Phylogeny (evolutionary tree)
Orangutan
Gorilla
Chimpanzee
Human
From the Tree of the Life Website, University of Arizona