CIPRES: Enabling Tree of Life Projects

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CIPRES Enabling Tree of Life Projects

• Tandy Warnow
• The University of Texas at Austin

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Phylogeny
From the Tree of the Life Website,University of
Arizona
Orangutan
Human
Gorilla
Chimpanzee
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Reconstructing the Tree of Life
Handling large datasets millions of species
The Tree of Life is not really a tree
reticulate evolution
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Cyber Infrastructure for Phylogenetic
Research Purpose to create a national
infrastructure of hardware, open source
software, database technology, etc., necessary to
infer the Tree of Life. Group 40 biologists,
computer scientists, and mathematicians from 13
institutions. Funding 11.6 M (large ITR grant
from NSF). URL http//www.phylo.org
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CIPRes Members
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CIPRES activity

• Databases - e.g. TreeBase II (Bill Piel and
  others)
• Simulations of large-scale complex genome-scale
  evolution (Junhyong Kim)
• Outreach (Michael Donoghue and Brent Mishler)
• Algorithms (Tandy Warnow)
• Open source software (Wayne Maddison, Dave
  Swofford, Mark Holder, and Bernard Moret)
• Computer cluster at SDSC (Fran Berman and Mark
  Miller) - available to ATOL projects and other
  groups with datasets above 1000 taxa

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Phylogeny Problem
U
V
W
X
Y
TAGCCCA
TAGACTT
TGCACAA
TGCGCTT
AGGGCAT
X
U
Y
V
W
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Steps in a phylogenetic analysis

• Gather data
• Align sequences
• Estimate phylogeny (or combine with previous
  step)
• Estimate the reliable aspects of the evolutionary
  history (using bootstrapping, consensus trees, or
  other methods)
• Perform post-tree analyses.

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CIPRES research in algorithms

• Multiple sequence alignment
• Genomic alignment
• Heuristics for Maximum Parsimony and Maximum
  Likelihood
• Bayesian MCMC methods
• Supertree methods
• Whole genome phylogeny reconstruction
• Reticulate evolution detection and reconstruction
  
• Data mining on sets of trees, and compact
  representations of these sets

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Software distributions

• The first distribution (in the next months) will
  focus on Rec-I-DCM3(PAUP) fast heuristic
  searches for maximum parsimony on large datasets
  for PAUP users
• All software will be open source
• Community contributions to software will be
  enabled

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Phylogenetic reconstruction methods

1. Heuristics for hard optimization criteria
   (Maximum Parsimony and Maximum Likelihood) - hard
   to solve on large datasets

1. Polynomial time distance-based methods Neighbor
   Joining, FastME, Weighbor, etc. - poor accuracy
   on datasets with large evolutionary distances

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DCMs Divide-and-conquer for improving phylogeny
reconstruction
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Boosting phylogeny reconstruction methods

• DCMs boost the performance of phylogeny
  reconstruction methods.

DCM
Base method M
DCM-M
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DCMs (Disk-Covering Methods)

• DCMs for polynomial time methods improve
  topological accuracy (empirical observation), and
  have provable theoretical guarantees under Markov
  models of evolution
• DCMs for hard optimization problems reduce
  running time needed to achieve good levels of
  accuracy (empirically observation)

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DCM1-boosting distance-based methodsNakhleh et
al. ISMB 2001

• DCM1-boosting makes distance-based methods more
  accurate
• Theoretical guarantees that DCM1-NJ converges to
  the true tree from polynomial length sequences

0.8
NJ
DCM1-NJ
0.6
Error Rate
0.4
0.2
0
0
400
800
1600
1200
No. Taxa
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Major challenge MP and ML

• Maximum Parsimony (MP) and Maximum Likelihood
  (ML) remain the methods of choice for most
  systematists
• The main challenge here is to make it possible to
  obtain good solutions to MP or ML in reasonable
  time periods on large datasets

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Solving NP-hard problems exactly is unlikely
leaves trees
4 3
5 15
6 105
7 945
8 10395
9 135135
10 2027025
20 2.2 x 1020
100 4.5 x 10190
1000 2.7 x 102900

• Number of (unrooted) binary trees on n leaves is
  (2n-5)!!
• If each tree on 1000 taxa could be analyzed in
  0.001 seconds, we would find the best tree in
• 2890 millennia

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How good an MP analysis do we need?

• Our research shows that we need to get within
  0.01 of optimal (or better even, on large
  datasets) to return reasonable estimates of the
  true trees topology

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Problems with current techniques for MP
Shown here is the performance of a heuristic
maximum parsimony analysis on a real dataset of
almost 14,000 sequences. (Optimal here means
best score to date, using any method for any
amount of time.) Acceptable error is below 0.01.
Performance of TNT with time
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Strict Consensus Merger (SCM)
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Observations

• The best MP heuristics cannot get acceptably good
  solutions within 24 hours on most of these large
  datasets.
• Datasets of these sizes may need months (or
  years) of further analysis to reach reasonable
  solutions.
• Apparent convergence can be misleading.

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Our objective speed up the best MP heuristics
Fake study
Performance of hill-climbing heuristic
MP score of best trees
Desired Performance
Time
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DCM3 decomposition

• DCM3 decompositions
• can be obtained in O(n) time
• (2) yield small subproblems
• (3) can be used iteratively
• (4) can be applied recursively

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Iterative-DCM3
T
DCM3
Base method
T
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New DCMs

• DCM3
• Compute subproblems using DCM3 decomposition
• Apply base method to each subproblem to yield
  subtrees
• Merge subtrees using the Strict Consensus Merger
  technique
• Randomly refine to make it binary
• Recursive-DCM3
• Iterative DCM3
• Compute a DCM3 tree
• Perform local search and go to step 1
• Recursive-Iterative DCM3

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Rec-I-DCM3 significantly improves performance
Current best techniques
DCM boosted version of best techniques
Comparison of TNT to Rec-I-DCM3(TNT) on one large
dataset
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Datasets
Obtained from various researchers and online
databases

• 1322 lsu rRNA of all organisms
• 2000 Eukaryotic rRNA
• 2594 rbcL DNA
• 4583 Actinobacteria 16s rRNA
• 6590 ssu rRNA of all Eukaryotes
• 7180 three-domain rRNA
• 7322 Firmicutes bacteria 16s rRNA
• 8506 three-domain2org rRNA
• 11361 ssu rRNA of all Bacteria
• 13921 Proteobacteria 16s rRNA

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Rec-I-DCM3(TNT) vs. TNT(Comparison of scores at
24 hours)
Base method is the default TNT technique, the
current best method for MP. Rec-I-DCM3
significantly improves upon the unboosted TNT by
returning trees which are at most 0.01 above
optimal on most datasets.
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Observations

• Rec-I-DCM3 improves upon the best performing
  heuristics for MP.
• The improvement increases with the difficulty of
  the dataset.

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DCMs

• DCM for NJ and other distance methods produces
  absolute fast converging (afc) methods
• DCMs for MP heuristics
• DCMs for use with the GRAPPA software for whole
  genome phylogenetic analysis these have been
  shown to let GRAPPA scale from its maximum of
  about 15-20 genomes to 1000 genomes.
• Current projects DCM development for maximum
  likelihood and multiple sequence alignment.

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Part II Whole-Genome Phylogenetics
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Genomes Evolve by Rearrangements
1 2 3 4 5 6 7 8 9 10

• Inversion (Reversal)

• Transposition

• Inverted Transposition

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Genome Rearrangement Has A Huge State Space

• DNA sequences 4 states per site
• Signed circular genomes with n genes
  states, 1
  site
• Circular genomes (1 site)
• with 37 genes
  states
• with 120 genes
  states

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Why use gene orders?

• Rare genomic changes huge state space and
  relative infrequency of events (compared to site
  substitutions) could make the inference of deep
  evolution easier, or more accurate.
• Our research shows this is true, but accurate
  analysis of gene order data is computationally
  very intensive!

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Maximum Parsimony on Rearranged Genomes (MPRG)

• The leaves are rearranged genomes.
• Find the tree that minimizes the total number of
  rearrangement events (NP-hard)

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Solving the inversion phylogeny

• Usual issue of getting stuck in local optima,
  since the optimization problems are NP-hard
• Additional problem finding the best trees is
  enormously hard, since even the point
  estimation problem is hard (worse than
  estimating branch lengths in ML).

Local optimum
MP score
Global optimum
Phylogenetic trees
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Benchmark gene order dataset Campanulaceae

• 12 genomes 1 outgroup (Tobacco), 105 gene
  segments
• NP-hard optimization problems breakpoint and
  inversion phylogenies (techniques score every
  tree)
• Joint work with Bob Jansen, Linda Raubeson, Jijun
  Tang, and Li-San Wang
• 1997 BPAnalysis (Blanchette and Sankoff) 200
  years (est.)
• 2000 Using GRAPPA v1.1 on the 512-processor Los
  Lobos Supercluster machine 2 minutes
  (200,000-fold speedup per processor)
• 2003 Using latest version of GRAPPA 2 minutes
  on a single processor (1-billion-fold speedup per
  processor)

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GRAPPA (Genome Rearrangement Analysis under
Parsimony and other Phylogenetic Algorithms)

• http//www.cs.unm.edu/moret/GRAPPA/
• Heuristics for NP-hard optimization problems
• Fast polynomial time distance-based methods
• Contributors U. New Mexico, U. Texas at Austin,
  Universitá di Bologna, Italy
• Freely available in source code at this site.
• Project leader Bernard Moret (UNM)
  (moret_at_cs.unm.edu)

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Limitations and ongoing research

• Current methods are mostly limited to single
  chromosomes with equal gene content (or very
  small amounts of deletions and duplications).
• We have made some progress on developing a
  reliable distance-based method for chromosomes
  with unequal gene content (tests on real and
  simulated data show high accuracy)
• Handling the multiple chromosome case is harder

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Acknowledgements

• NSF
• The David and Lucile Packard Foundation
• The Program in Evolutionary Dynamics at Harvard
• The Institute for Cellular and Molecular Biology
  at UT-Austin
• See http//www.phylo.org and http//www.cs.utexas.
  edu/tandy for more info