Molecular phylogenetics 1

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Molecular phylogenetics 1

• Level 3 Molecular Evolution and Bioinformatics
• Jim Provan

Page and Holmes Sections 6.1-2
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Distances vs. discrete characters

• This division is based on how the data are
  treated
• Distance methods first convert aligned sequences
  into a pairwise distance matrix, then input that
  matrix into a tree building method
• Discrete methods consider each nucleotide site
  (or function of each site) separately

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Distances vs. discrete characters
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Distances vs. discrete characters
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Distances vs. discrete characters

• Trees obtained by parsimony (a discrete method)
  and minimum evolution (a distance method) are
  identical in topology and branch lengths
• Parsimony analysis identifies seven substitutions
  and places them on the five branches of the tree
• Distance tree apportions observed distances
  between sequences over branches of the tree
• Under parsimony each site requires one change,
  which gives a total of seven changes
• Summing the branch lengths of the distance tree
  gives the same value 2 1 2 1 1 7
• Parsimony tree gives additional information
  which site contributes to which branch plus
  ancestral states

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Clustering methods vs. search methods

• Cluster methods follow a set of steps (an
  algorithm) and arrive at a tree
• Advantages
• Easy to implement, resulting in very fast
  computer programs
• Always produce a single tree
• Disadvantages
• Results obtained from simple clustering
  algorithms often depend on the order in which
  sequences are added to the growing tree
• Do not allow evaluation of competing hypotheses
  two different trees could explain data equally
  well but no way of measuring fit between tree and
  data

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A clustering method
Round 1
Round 2
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Search methods

• Tree-building methods in this class use
  optimality criteria to choose among the set of
  all possible trees
• Criterion is used to assign a score or rank
  to each tree which is a function of the
  relationship between the tree and the data
• Require an explicit function relating tree and
  data (e.g. a model of how sequences evolve)
• Allow comparison of how well competing hypotheses
  of evolutionary relationships fit the data
• Major disadvantage is that optimality methods are
  computationally very expensive
• For a given data set and tree, what is the
  optimality value?
• Which of all possible trees has the maximum
  optimality value?

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An optimality method
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Non-deterministic polynomial- completeness
problems

• Non-deterministic polynomial-completeness
  problems represent a set of problems with no
  efficient algorithm for their solution known to
  exist
• Problem of finding the optimal evolutionary tree
  for a variety of criteria (e.g. minimum
  evolution, maximum parsimony) is NP-complete
• For even a reasonable number of sequences (e.g.
  20) it is impossible to guarantee that the
  optimal tree has been found
• In such cases, we must rely on heuristics to find
  something approaching the best tree, but this may
  be far from optimal
• Human mitochondrial DNA - different researchers
  obtained quite different trees using different
  heuristic searches

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An heuristic method
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Subtree methods

• The effectiveness of an heuristic search depends
  in part on the number of trees examined, which
  can be computationally demanding
• An alternative approach is to divide the set of
  sequences into smaller sets and find optimal
  trees for these subsets
• Smallest unrooted tree is a quartet
• Each quartet has three possible unrooted trees
• Quartet puzzling follows these two steps
• For each quartet, identify the optimal tree
• Take all four-sequence trees from step 1 and
  assemble them into a tree
• Due to homoplasy, the best tree will usually be
  the one which contains most quartets (but this is
  an NP-complete problem as well)

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Comparing tree-building methods
UPGMA
Neighbour joining
Maximum parsimony
Minimum evolution
Maximum likelihood
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Comparing tree-building methods

• Efficiency
• Effectively the time in which a computer program
  can find a tree
• Since virtually all optimality methods are
  NP-complete, efficient tree searching algorithms
  that guarantee the best tree are unlikely
• Some optimality criteria can be evaluated quicker
  than others heuristic searches using parsimony
  can explore a much larger number of trees than a
  search using likelihood
• Power
• Measure of how much data are needed before we can
  be reasonably sure of arriving at the correct
  result
• A method may be theoretically appealing, but if
  it requires huge numbers of sites it is not
  practical

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Comparing tree-building methods

• Consistency
• Will the method converge on the true tree as data
  are added?
• Inconsistent methods will fail even if data are
  continually added
• Robustness
• All tree-building methods make (implicit or
  explicit) assumptions about evolutionary
  processes
• Sensitivity to violations of the underlying model
  which return poor estimates of phylogeny e.g.
  assumption of a molecular clock
• Falsifiability
• The ability to tell whether these assumptions
  have been violated i.e. that we should not be
  using the method at all!