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Untangling Molecular Evolution

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13 years, finished 2 year ahead of schedule. $3 billion, cost $2.7 ... Amniotic Egg. 330 million years. The true tree is unknown ... – PowerPoint PPT presentation

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Title: Untangling Molecular Evolution


1
Untangling Molecular Evolution
  • Andrew Meade
  • A.Meade_at_Reading.ac.uk

2
Molecular Data
  • Human Genome
  • Finished 2003
  • 13 years, finished 2 year ahead of schedule.
  • 3 billion, cost 2.7
  • 483 completely sequenced genomes (2006)
  • X Prize 100 human genomes 10 days 10 million.

3
Molecular Data Pancreatic Ribonuclease
4
What is a phylogeny?
  • Representation of evolution
  • Inferred from data via a model.
  • Data is normally a genetic element (such as a
    gene), taken from a number of species.
  • Allows us to infer the past processes of
    evolution without observing it.

5
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6
Uses of phylogeny
  • Spread of diseases, H5N1, HIV.
  • Protein-Protein Interaction
  • Predicting changes in Protein structure.
  • Information about molecular evolution

7
Human Influenza (Flu) Virus
1997
10
1984
8
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9
The Chicken And The Egg
80 million years
Amniotic Egg 330 million years
10
The true tree is unknown
  • Data is only available for living species
  • Evolution has been going on for a long time (4
    billion years)
  • Evolution is very complex

11
There are lots of trees
Number of Possible Phylogenetic Trees
Species Number of Trees
Species 50 275292135328356515452597297515244306393
00973035816196098326553772152587890625
12
MCMC
  • Sample of trees used.
  • Trees are sampled in proportion to there
    probability.
  • Not looking for the best / most probable tree.

13
Where
Is the probability of the sequence given Treei
Is a vector of branch lengths
Is a vector of parameters lengths
Is the prior probability of t
Is the prior probability of m
14
MCMC properties
  • Guaranteed to sample all trees in the search
    space.

Only as time goes to 8
Guaranteed to sample trees in proportion to there
probability.
Only at convergence
15
MCMC Sampling
16
Iteration
Convergence Sampling from the stationary
distribution
Log Likelihood
Burn-in
17
Postior distribution of likelihoods
18
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19
Computational Time
20
Parallel algorithm
Node 1
Node 3
Node 2
21
Algorithm Scaling
1 Processor 130 Days 60 Processors 4 Days
22
Estimating dinosaur genome properties
In Genome size (pg)
ln Osteocyte cell size (µm3)
23
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24
The effect of speciation on molecular evolution
each speciation event makes some contribution to
path length
path length accumulates as a function of time
25
How many data sets show evidence of a
punctuational effect?
35 of the 100 data sets showed significant
punctuational effects
significantly more common in plants and fungi
than animals
10,000 molecule studied
26
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27
Protein Networks
Genes in the human genome
1999 100,000
2002 65,000 75,000
2007 20,000 25,00 19,599 protein-coding genes
confirmed
28
Eukaryote protein-interaction network
animals
yeast protein-interaction network (MIPS)
fungal pathogens
yeast
29
Changes in Gene networks
yeast
fungal pathogens
animals
retained link
acquired link
30
Areas of computer science interest
  • Search / Optimisation
  • Distributed computation / parallelisation
  • Visualisation / user interfaces
  • Data mining

31
Acknowledgments
  • Mark Pagel, Chris Venditti and Daniel Barker -
    Computation Biology
  • Vassil Alexandrov, Christian Weihrauch and Ashish
    Thandavan - ACET
  • Chris Organ, Andrew Shedlock, Scott Edwards -
    Harvard University

32
Convergence of a Markov chainsampling
phylogenetic tree of n500 tips using
an alignment of n4400 nucleotides
log-likelihood
Iteration number
NB 99 of increase in likelihood in first 2.8
of run. 0.07 change in final 2 million
iterations
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