TreeBuilding

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Tree-Building
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Methods in Tree Building
Phylogenetic trees can be constructed by
clustering method optimality method
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Clustering Method

• follows a set of steps (an algorithm) and arrives
  at a tree.
• easy to implement and resulting in very fast
  computer programmes.
• always produces a single tree.

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Clustering Method
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Clustering Method

• limitations
• the result obtained from simple clustering
  algorithms often depends on the order in which
  the taxa added in a growing tree.
• do not allow us to evaluate competing hypotheses
  (they merely produce a tree).

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Optimality Method

• Chooses among the set of all possible trees.
• each tree is assigned a score or rank which is
  function of the relationship between tree and
  data.

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Optimality Method
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Optimality Method

• Advantages
• requires an explicit function that relates data
  and tree.
• allows to evaluate the quality of any tree.
• Disadvantages
• time consuming (infeasible for a tree with more
  than 20 taxa). Overcome by heuristic approach.

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Heuristic Approach

• to explore some subset of all the possible trees,
  in the hope that the subset will contain the
  optimal tree.
• to start with a tree and rearrange it, keeping
  any rearrangement that produces a better tree
  hill-climbing.
• if a set of possible trees contains more than one
  island, the heuristic search may land on a
  suboptimal island, and the optimal island goes
  undiscovered.

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Heuristic Approach
Island B
Island A
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Comparing Tree-Building Methods
Type of data
Nucleotide sites
Distance
UPGMA Neighbour-Joining
Clustering algorithm
Tree building
Maximum Parsimony Maximum Likelihood
Minimum evolution
Optimality criterion