Phylogenetic Tree Construction Methods and Programs

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Chapter 11 Phylogenetic Tree Construction
Methods and Programs
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• Character based methods
• Molecular sequences from individual taxa
• Characters at corresponding positions in
  alignments are homologous
• Characters of common ancestor can be traced
• Each character evolved independently
• Distance based methods
• Dissimilarity between sequence pairs, based on
  alignment
• All sequences involved are homologous
• Tree branches are additive

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Distance methods Clustering-based methods

• Unweighted Pair Group Method Using Arithmetic
  Average (UPGMA)
• Given distance matrix
• Group two taxa with smallest pairwise distance
• Generate new matrix with combined taxa as a
  single group
• Repeat grouping and reduce matrix again
• Continue iteration until all taxa are grouped
• Last taxon added considered outgroup and tree
  root

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Distance methods Clustering-based methods
Neighbour Joining Similar to UPGMA, but does not
assume constant evolutionary rate between
taxa Makes use of distance matrix, but corrects
for unequal evolutionary rates by calculating
r-distances and transformed r-distances dAB
dAB -1/2(rArB) dAB is the converted distance
between A and B and dAB is the actual
evolutionary distance between A and B rA (or rB)
is sum of distances of A (or B) to all other
taxa ri ?dij Where i and j are two different
taxa r value is needed to create a modified
distance matrix Transformed r ri
ri/(n-2) Used to determine distance of
individual taxon to nearest node
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• Generalized neighbour joining
• Disadvantage of NJ is that one tree is generated
• Depending on choice of two equally close starting
  taxa, sub-optimal tree may be calculated
• Generalized NJ multiple NJ runs with different
  starting taxa
• Select tree from all calculated trees that best
  fit actual evolutionary distances

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• Optimally based methods Fitch-Margoliash
• Selects best tree based on minimal deviation
  between calculated distance and original distance
• Randomly selects two taxa, and calculated branch
  lengths
• Examines all tree topologies and chooses one with
  minimum deviation
• dij pairwise distance
• pij corresponding tree branch length

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Minimum evolution (ME) Similar to FM, but uses
different optimality criterion to find tree with
minimum branch length S ?bi bi is ith branch
length ME slightly outperforms FM
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Character based Methods Maximum Parsimony
(thriftiness) Based on character differences in
sequence alignments Number of differences
counted, i.e., ancestral sequences can be
inferred Tree with fewest evolutionary changes
(Occams Razor) Occams Razor (14th-century
English logician and Franciscan friar, William of
Ockham.) "entia non sunt multiplicanda praeter
necessitatem", roughly translated as "entities
must not be multiplied beyond necessity, i.e.,
"All other things being equal, the simplest
solution is the best."
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William of Ockham - Sketch labelled "frater
Occham iste", from a manuscript of Ockham's Summa
Logicae, 1341
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• Maximum Parsimony
• Search all possible tree topologies
• Reconstruct ancestral tree that require minimum
  number of character changes
• Use only sites with rich phylogenetic information
  to save time
• Informative sites two different kinds of
  characters occurring at least twice

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Find tree with minimum number of character changes
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• Weighted parsimony
• Weighing scheme takes into account functionally
  important sites
• Transitions versus transversion

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• Tree searching methods
• Only works for lt 10 taxa
• Need simplification steps for more taxa branch
  and bound

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• Branch and Bound
• Upper limit for number of allowed sequence
  variations
• Build tree for all taxa involved using UPGMA or
  NJ
• Compute minimum substitutions for such a tree
• MP must be equal or better than UPGMA or NJ

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• When number of taxa gt 20
• Branch and bound computationally too intensive
• Need heuristic approach
• Cut of branch and re-graft
• Recalculate branch lengths
• Continue iteration until no shorter branch
  arrangements are found
• Can get stuck in local minima
• Global sub-pruning option in some programs

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• Long Branch Attraction
• Rapidly evolving taxa with long branches are
  places together in a tree
• Assumption that all taxa evolve at the same rate
• All mutations (transitions and transversions)
  contribute equally to branch lengths
• Weighted parsimony should solve problem
• Increase taxon sampling size

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• Maximum likelihood method
• The probability that a nucleotide changes over
  time t is calculated from
• Pt 1/4 1/4e-?t
• The probabilities are multiplied from root to tip
  for all branches
• All possible tree topologies are examined, and
  probabilities calculated
• The tree with the highest probability is chosen

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• Quartet Puzzling
• Make many subsets composed of four taxa
• Combine all quartets into single tree
• Significantly reduces computing time
• ML tree not guaranteed

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• Neighbour Joining Maximum Likelihood
• Construct initial tree with NJ
• Poor branches (low bootstrap support),
  collapsed
• Resolve by ML
• 10X faster than neat ML method

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• Genetic Algorithm
• Generate random population of trees with
  arbitrary branch lengths
• Select tree with highest likelihood score
• Allow this tree to mutate
• Screen mutated offspring, and select highest
  likelihood trees
• Repeat process until no higher scoring trees are
  produced

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Bayesian Analysis Based on posterior
probability Probably the fastest and most
accurate phylogenetic analysis available to date
Posterior probability
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• Phylogenetic Tree Evaluation
• Bootstrapping
• Recalculate trees with randomly perturbed
  datasets
• If the original tree topology is repeatedly
  found, it is most likely correct
• Parametric
• New datasets are generated based on sequence
  distribution
• Based opn sequence substitution model
  (Jukes-Cantor or Kimura)
• Non-parametric bootstrapping
• Random replacement of sites
• Jack knifing
• Randomly delete half of the sites in a dataset

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• Bayesian simulation
• Most efficient in terms of statistical evaluation
• Does not require bootstrapping because MCMC
  procedure millions of re-sampling steps
• Posterior probabilities assigned as statistical
  support
• Makes statistical valuation of ML trees more
  feasible

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• Kishino-Hasegawa Test
• Test whether two competing tree topologies are
  statistically significantly different
• Difference in branch length at each informative
  site is calculated
• Standard deviation of the difference values is
  them calculated
• Allowing derivation of t-value

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Shimodaira-Hasegawa Test Frequently used for
ML Based on ?2 test Converted to p-value
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Phylogenetic Programs PAUP http//paup.csit.fsu.
edu/ Phylip http//evolution.genetics.washington.e
du/phylip.html TREE-PUZZLE http//www.tree-puzzle.
de PHYML http//atgc.lirmm.fr/phyml/ MrBayes
http//morphbank.ebc.uu.se/mrbayes
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