Computing the Tree of Life

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Computing the Tree of Life

• The University of Texas at Austin
• Department of Computer Sciences
• Tandy Warnow

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Phylogeny
From the Tree of the Life Website,University of
Arizona
Orangutan
Human
Gorilla
Chimpanzee
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DNA Sequence Evolution
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Molecular Phylogenetics
U
V
W
X
Y
TAGCCCA
TAGACTT
TGCACAA
TGCGCTT
AGGGCAT
X
U
Y
(Tree is unrooted)
V
W
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Phylogeny
From the Tree of the Life Website,University of
Arizona
Orangutan
Human
Gorilla
Chimpanzee
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Evolution informs about everything in biology

• Big genome sequencing projects just produce data
  -- so what?
• Evolutionary history relates all organisms and
  genes, and helps us understand and predict
• interactions between genes (genetic networks)
• drug design
• predicting functions of genes
• influenza vaccine development
• origins and spread of disease
• origins and migrations of humans

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Evolutionary trees and the pharmaceutical industry

• Big genome sequencing projects just produce data
  -- so what? Evolutionary history relates all
  organisms and genes, and evolutionary trees are
  used to make important biological discoveries.
• The pharmaceutical industry uses phylogenies for
  many applications, such as the development of
  influenza vaccine!
• Inaccuracies in the phylogenies lead to
  inaccurate predictions (e.g., vaccines that dont
  work, drugs that dont have the required
  properties). Current software isnt accurate
  enough, or fast enough!
• This means !

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NSFs program for Assembling the Tree of Life

• The Tree of Life has proven useful in many
  fields, such as
• choosing experimental systems for biological
  research,
• determining which genes are common to many kinds
  of organisms and which are unique,
• tracking the origin and spread of emerging
  diseases and their vectors,
• bio-prospecting for pharmaceutical and
  agrochemical products,
• Developing databases for genetic information, and
  evaluating risk factors for species conservation
  and ecosystem restoration.

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Computational challenges for Assembling the Tree
of Life (NSF)

• 8 million species for the Tree of Life -- cannot
  currently analyze more than a few hundred (and
  even this takes years)
• We need new methods for inferring large
  phylogenies - hard optimization problems!
• We need new software for visualizing large trees
• We need new database technology

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We are world leaders in research in Computational
Phylogenetics

• DCM-boosting for phylogeny reconstruction -
  improves accuracy and speeds up heuristics for
  NP-hard problems (Warnow, UT-Austin)
• GRAPPA -- software for whole genome phylogeny
  (Moret, UNM)
• Visualization of large trees, and sets of trees
  (Amenta, UC Davis)
• Phylogenetic databases (Miranker)

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DCM-boosting improves methodsNakhleh et al.
ISMB 2001

• Random trees
• K2PGamma model
• Sequence length1000
• Average branch length0.05

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(Figure)Nakhleh et al. ISMB 2001

• Random trees
• K2PGamma model
• Sequence length1000
• Average branch length0.05

DCM-NJ
0.8
NJ
DCM-NJ MP
HGT-FP
0.6
Avg. RF
0.4
0.2
0
0
400
800
1600
1200
No. Taxa
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DCM-boosting phylogenetic reconstruction
methodsNakhleh et al. ISMB 2001

• DCM-boosting makes fast methods more accurate
• DCM-boosting speeds-up heuristics for hard
  optimization problems

0.8
NJ
DCM-NJ
0.6
Error Rate
0.4
0.2
0
0
400
800
1600
1200
No. Taxa
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Whole-Genome Phylogenetics
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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
• 1997 BPAnalysis (Blanchette and Sankoff) 200
  years (est.)

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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
• 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)

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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
• 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
• Fastest and most accurate software for whole
  genome phylogeny worldwide

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Opportunities

• New phylogenetic reconstruction software can
  improve pharmaceutical RD (making more accurate
  solutions achievable in hours or days, rather
  than months or years)
• Software for researchers is available as free
  (open source), but users need the latest tools
  now, with proper interfaces -- business
  opportunity.

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Participants and Funding

• University of Texas Computer Scientists Warnow,
  Dhillon, Hunt, and Miranker
• University of Texas biologists Jansen,
  Linder, and Hillis
• Other institutions UNM, UC Davis, Central
  Washington, CUNY, JGI
• Funding Three NSF ITR grants, NSF Biocomplexity,
  David and Lucile Packard Foundation

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Phylolab, U. Texas
Please visit us at http//www.cs.utexas.edu/users/
phylo/