Molecular Phylogenetics

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Molecular Phylogenetics
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Trees

• Diagram consisting of branches and nodes
• Species tree (how are my species related?)
• contains only one representative from each
  species
• when did speciation take place?
• all nodes indicate speciation events
• Gene tree (how are my genes related?)
• normally contains a number of genes from a single
  species
• nodes relate either to speciation or gene
  duplication events

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The purpose of a phylogenetic tree is to
illustrate how a group of objects (usually genes
or organisms) are related to one another
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Phylogenetic trees are about visualising
evolutionary relationships
Nothing in Biology Makes Sense Except in the
Light of Evolution Theodosius
Dobzhansky (1900-1975)
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Terms

• Phylogeny (phylo tribe genesis)
• Homologue
• Orthologue
• Paralogue
• Tree topology
• Cladogram
• Phenogram

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Cladogram
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Phenogram
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Clade A set of species which includes all of
the species derived from a single common ancestor
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Molecular Evolution - Li
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Cladistics and Phenetics

• Cladistic approach Trees are drawn based on the
  conserved characters
• Phenetic approach Trees are based on some
  measure of distance between the leaves
• Molecular phylogenies are inferred from molecular
  (usually sequence) data
• either cladistic (e.g. gene order) or phenetic

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Classes of algorithm used to infer phylogeny from
sequence

• Distance methods
• Parsimony
• Likelihood
• Probabilistic methods
• Phylogentic invariants

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Distance methods

• Calculate the distance CORRECTING FOR MULTIPLE
  HITS
• The Distance Matrix
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• Rat 0.0000 0.0646 0.1434 0.1456
  0.3213 0.3213 0.7018
• Mouse 0.0646 0.0000 0.1716 0.1743
  0.3253 0.3743 0.7673
• Rabbit 0.1434 0.1716 0.0000 0.0649
  0.3582 0.3385 0.7522
• Human 0.1456 0.1743 0.0649 0.0000
  0.3299 0.2915 0.7116
• Oppossum 0.3213 0.3253 0.3582 0.3299
  0.0000 0.3279 0.6653
• Chicken 0.3213 0.3743 0.3385 0.2915
  0.3279 0.0000 0.5721
• Frog 0.7018 0.7673 0.7522 0.7116
  0.6653 0.5721 0.0000

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Distance methods

• Normally fast and simple
• e.g. UPGMA, Neighbour Joining, Minimum Evolution,
  Fitch-Margoliash

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Correction for multiple hits

• Only differences can be observed directly not
  distances
• All distance methods rely (crucially) on this
• A great many models used for nucleotide sequences
  (e.g. JC, K2P, HKY, Rev, Maximum Likelihood)
• aa sequences are infinitely more complicated!
• Accuracy falls off drastically for highly
  divergent sequences

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Minimum Evolution

• The total length of all branches in the tree
  should be a minimum
• It has been shown that the minimum evolution tree
  is expected to be the true tree provided branch
  lengths corrected for multiple hits

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Neighbour Joining
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Neighbour joining is an approximation to minimum
evolution
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Maximum Parsimony

• Occams Razor
• Entia non sunt multiplicanda praeter
  necessitatem.
• William of Occam (1300-1349)

The best tree is the one which requires the least
number of substitutions
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• Check each topology
• Count the minimum number of changes required to
  explain the data
• Choose the tree with the smallest number of
  changes
• Usually performs well with closely related
  sequences but often performs badly with very
  distantly related sequences
• With distantly related sequences homoplasy
  becomes a major problem

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Maximum Likelihood

• Require a model of evolution
• Each substitution has an associated likelihood
  given a branch of a certain length
• A function is derived to represent the likelihood
  of the data given the tree, branch-lengths and
  additional parameters
• Function is minimized

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Models can be made more parameter rich to
increase their realism

• The most common additional parameters are
• A correction to allow different substitution
  rates for each type of nucleotide change
• A correction for the proportion of sites which
  are unable to change
• A correction for variable site rates at those
  sites which can change
• The values of the additional parameters will be
  estimated in the process (e.g. PAUP)

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A gamma distribution can be used to model site
rate heterogeneity
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Long Branches Attract

• In a set of sequences evolving at different
  rates the sequences evolving rapidly are drawn
  together

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Comparison of methods

• Inconsistency
• Neighbour Joining (NJ) is very fast but depends
  on accurate estimates of distance. This is more
  difficult with very divergent data
• Parsimony suffers from Long Branch Attraction.
  This may be a particular problem for very
  divergent data
• NJ can suffer from Long Branch Attraction
• Parsimony is also computationally intensive
• Codon usage bias can be a problem for MP and NJ
• Maximum Likelihood is the most reliable but
  depends on the choice of model and is very slow
• Methods may be combined

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The Molecular Clock

• For a given protein the rate of sequence
  evolution is approximately constant across
  lineages
• Zuckerkandl and Pauling (1965)

This would allow speciation and duplication
events to be dated accurately based on molecular
data
Local and approximate molecular clocks more
reasonable
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Relative Rate Test

• Test whether sets of sequences are evolving at
  equal rates (local molecular clock hypothesis)

e.g. RRTree, Robinson-Rechavi http//pbil.univ-lyo
n1.fr/software/rrtree.html
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Rooting the Tree

• In an unrooted tree the direction of evolution is
  unknown
• The root is the hypothesized ancestor of the
  sequences in the tree
• The root can either be placed on a branch or at a
  node
• You should start by viewing an unrooted tree

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Automatic rooting

• Many software packages will root trees
  automatically (e.g. mid-point rooting in NJPlot)
• This must involve assumptions BEWARE!

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Rooting Using an Outgroup

• 1. The outgroup should be a sequence (or set of
  sequences) known to be less closely related to
  the rest of the sequences than they are to each
  other
• 2. It should ideally be as closely related as
  possible to the rest of the sequences while still
  satisfying condition 1
• The root must be somewhere between the outgroup
  and the rest (either on the node or in a branch)

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Sometimes two trees may look very different
but, in fact, differ only in the position of the
root
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What sequences should I use for organism
phylogenies?

• Slowly evolving / Fast evolving
• rRNA
• mitochondrion
• other

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How confident am I that my tree is correct?

• Bootstrap values
• Bootstrapping is a statistical technique that
  can use random resampling of data to determine
  sampling error for tree topologies

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Bootstrapping phylogenies

• Characters are resampled with replacement to
  create many bootstrap replicate data sets
• Each bootstrap replicate data set is analysed
  (e.g. with parsimony, distance, ML etc.)
• Agreement among the resulting trees is summarized
  with a majority-rule consensus tree
• Frequencies of occurrence of groups, bootstrap
  proportions (BPs), are a measure of support for
  those groups

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Bootstrapping - an example
Ciliate SSUrDNA - parsimony bootstrap
Ochromonas (1)
Symbiodinium (2)
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Prorocentrum (3)
Euplotes (8)
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Tetrahymena (9)
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Loxodes (4)
100
Tracheloraphis (5)
100
Spirostomum (6)
100
Gruberia (7)
Majority-rule consensus
Wim de Grave et al. Fiocruz bioinformatics
training course
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Bootstrapping
Majority-rule consensus (with minority components)
Wim de Grave et al. Fiocruz bioinformatics
training course
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Bootstrap - interpretation

• Bootstrapping is a very valuable and widely used
  technique (it is demanded by some journals)
• BPs give an idea of how likely a given branch
  would be to be unaffected if additional data,
  with the same distribution, became available
• BPs are not the same as confidence intervals.
  There is no simple mapping between bootstrap
  values and confidence intervals. There is no
  agreement about what constitutes a good
  bootstrap value (gt 70, gt 80, gt 85 ????)
• Some theoretical work indicates that BPs can be a
  conservative estimate of confidence intervals
• If the estimated tree is inconsistent all the
  bootstraps in the world wont help you..

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Jack-knifing

• Jack-knifing is very similar to bootstrapping and
  differs only in the character resampling strategy
• Jack-knifing is not as widely available or widely
  used as bootstrapping
• Tends to produce broadly similar results

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Likelihood-based tests of topologies

• Kishino-Hasegawa test
• Trees specified apriori
• KH can be used to test whether two competing
  hypotheses have significantly different
  likelihood
• NB should not be used to test trees that have
  been chosen on the basis of the data!
• Shimodaira-Hasegawa test
• Can be used to test confidence of ML tree
  compared to related trees (e.g. second most
  likely tree from the data)
• Andrew Rambaut http//evolve.zoo.ox.ac.uk/software
  /shtests

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Inferring Sequences at Ancestral Nodes

• Maximum likelihood estimates of tree topologies
  also provide inferred sequences at ancestral
  nodes
• Analysis of sequences at ancestral nodes and
  sequence changes at ancestral branches can
  provide information about the timing of the
  acquiring of a novel trait or mutation
• PAML (Phylogenetic Analysis using Maximum
  Likelihood)
• Confidence intervals provided
• Selection can be inferred

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Coalescent models

• Consider genetic lineages going back in time
• Make inferences from patterns of coalescent
  events (e.g. effective population size,
  migrations etc.)
• Improved efficiency for sequence simulations
• Great number of software packages including
  LAMARK (Kuhner and Felsenstein)
• Has generated enormous interest and body of
  literature (for review see Rosenberg and
  Nordborg, Nature 2002

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• For an excellent review of recent progress in
  phylogenetics see
• Molecular phylogenetics state-of-the-art methods
  for looking into the past
• Whelan et al, Trends in Genetics, 2001

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Inferring Function from Sequence Homology
High throughput Genome Annotation
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Basic Methods
Taken from JA Eisen, Genome Research, 1998

• Highest BLAST hit
• uncharacterised gene is assigned the function of
  the best BLAST hit
• An additional cut-off value may be used
• Still very frequently used sometimes referred
  to as first-pass annotation
• Top Hits
• Examine a set of top BLAST hits and consider the
  consensus function
• COGs (Clusters of Orthologous Genes)
• Clustering of genes into orthologous groups based
  on similarity scores

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Phylogenomics
Taken from JA Eisen, Genome Research, 1998

• Choose genes of interest
• Identify homologues
• Align sequences
• Calculate gene tree
• Overlay known functions onto tree
• Infer likely function of genes of interest
• Note Only proteins with confirmed functions
  should be used (to avoid error propagation)

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Increased power over similarity methods However
increased power comes with increased costs in
terms of labour
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Duplication and Speciation

• Gene duplication may accelerate the rate of
  evolution and the rate of functional divergence
• Orthologues may provide better information about
  function (for a given level of divergence) than
  paralogues
• Software is available for automatic inference of
  gene duplication events on a phylogenetic tree
  (e.g. SDI Seán Eddy). This can be used for
  improved automation of phylogenomics (e.g. RIO
  Eddy et al.)
• ref Zmasek and Eddy BMC Bioinformatics 2002

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Functional Genomics

• e.g. Xun Gu Molecular Biology and Evolution 1999
• Examine functional divergence after duplication
• Type I (rates change)
• Type II (replacement at conserved site)
• Diverge program