Phylogeny

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Phylogeny

• - A brief introduction in 4 hours -

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Outline

• Introduction
• Practical approach
• Evolutionary models
• Distance-based methods / TP5_1
• Databases and software
• Sequence-based methods / TP5_2

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What is phylogeny?
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Phylogeny is the evolutionary history and
relationship of species.
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Why is phylogeny of interest in a proteomics
course?
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What data types can be used to infer phylogenies?

• Morphological characters
• Physiological characters
• Gene order (e.g. in mitochondria)
• Sequence data
• Nucleotide sequences
• Amino acid sequences
• Mixed characters
• .

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What is a phylogenetic tree?

• A phylogenetic tree is a model about the
  evolutionary relationship between species (OTUs)
  based on homologous characters
• But not all trees are phylogenetic trees
• Dendrogram general term for a branching diagram
• Cladogram branching diagram without branch
  length estimates
• Phylogenetic tree or Phylogram branching diagram
  with branch length estimates

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What is a phylogenetic tree?

• Rooted or unrooted
• bifurcating or multifurcating (solved or
  unsolved)

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Gene duplication

• Prokaryots at least 50
• Eukaryots gt90

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After gene duplication

• Coexistence (normally only for a short while)
• Mostly, only one copy is retained
• becomes nonfunctional (non-functionalization),
• becomes a pseudogene (pseudogenization)
• is lost
• Both copies are retained
• Distinct expression pattern
• Distinct subcellular location (rare)
• One copy keeps the original function, the other
  copy acquires a new function (neofunctionalization
  )
• Deleterious mutations in both entries
  (subfunctionalization)

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Relationships within homologs
Frog gene A
Orthologs
Human gene A
Mouse gene A
Gene duplication
Paralogs
Mouse gene B
Homologs
Ancestral gene
Human gene B
Orthologs
Frog gene B
Drosophila gene AB
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Homologs

• Homologs Genes of common origin
• Orthologs 1. Genes resulting from a speciation
  event, 2. Genes originating from an ancestral
  gene in the last common ancestor of the compared
  genomes
• Co-orthologs Orthologs that have undergone
  lineage-specific gene duplications subsequent to
  a particular speciation event
• Paralogs Genes resulting from gene duplication
• Inparalogs Paralogs resulting from
  lineage-specific duplication(s) subsequent to a
  particular speciation event
• Outparalogs Paralogs resulting from gene
  duplication(s) preceding a particular speciation
  event
• One-to-one (11) orthologs Orthologs with no
  (known) lineage-specific gene duplications
  subsequent to a particular speciation event
• One-to-many (1n) orthologs Orthologs of which
  at least one - and at most all but one - has
  undergone lineage-specific gene duplication
  subsequent to a particular speciation event
• Many-to-many (nn) orthologs Orthologs which
  have undergone lineage-specific gene duplications
  subsequent to a particular speciation event
• Xenologs Orthologs derived by horizontal gene
  transfer from another lineage

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Relationships between orthologs and paralogs
Frog gene A
Orthologs (Group 1)
Human gene A
Mouse gene A
Co-orthologs of Drosophila gene AB
Inparalogs of Group 2
Gene duplication
Orthologs (Group 2)
Mouse gene B
Ancestral gene
Human gene B
Outparalogs of Group 1
Frog gene B
Drosophila gene AB
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Practical approach I

• Actin-related protein 2 (first 60 columns of the
  alignment)
• ARP2_A MESAP---IVLDNGTGFVKVGYAKDNFPRFQFPSIVGR
  PILRAEEKTGNVQIKDVMVGDE
• ARP2_B MDSQGRKVIVVDNGTGFVKCGYAGTNFPAHIFPSMVGR
  PIVRSTQRVGNIEIKDLMVGEE
• ARP2_C MDSQGRKVVVCDNGTGFVKCGYAGSNFPEHIFPALVGR
  PIIRSTTKVGNIEIKDLMVGDE
• ARP2_D MDSQGRKVVVCDNGTGFVKCGYAGSNFPEHIFPALVGR
  PIIRSTTKVGNIEIKDLMVGDE
• ARP2_E MDSKGRNVIVCDNGTGFVKCGYAGSNFPTHIFPSMVGR
  PMIRAVNKIGDIEVKDLMVGDE
• .
  .
• Species are
• Caenorhabditis briggsae
• Drosophila melanogaster
• Homo sapiens
• Mus musculus
• Schizosaccharomyces pombe
• Can you build a dendrogram (tree) for the
  sequences of the alignment?
• Can you assign the species to the corresponding
  sequences of the alignment?

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Phylogenetic analysis

• Select Data
• Alignment
• Select a data model
• Select a substitution model
• Tree-building
• Distance matrix
• Tree-building
• Tree evaluation

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Select data

• To be considered
• Input data must be homolog!
• Number of character states
• Content of phylogenetic information
• Size of the dataset
• Automated cluster data from large datasets
• etc

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Alignment

• MSA methods
• ClustalW
• muscle
• MAFFT
• Probcons
• T-coffee
• See previous course

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Data model

• Characters selected for the analysis
• To be considered
• Each character should be homolog!
• Missing data (in some OTU)
• Number of characters
• etc

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Evolutionary models

• Phylogenetic tree-building presumes particular
  evolutionary models
• The model used influences the outcome of the
  analysis and should be considered in the
  interpretation of the analysis results
• Which aspects are to be considered?
• Frequencies of aa exchange
• Change of aa frequencies during evolution
• Between-site rate variation or Among-site
  substitution rate heterogenity
• Presence of invariable sites

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Evolutionary models

• Notation, e.g.
• JTT
• JTT F
• JTT F gamma (4 )
• JTT F gamma (8 ) I (under discussion)
• JTT F I
• It is not always the most complex model that
  produces the best result.
• The more complex the model, the more complex the
  explanation of the results.

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Tree-building methods

• Distance (matrix) methods
• Calculate distances for all pairs of taxa based
  on the sequence alignment
• Construct a phylogenetic tree based on a distance
  matrix
• Character-based (Sequence) methods
• Constructs a phylogenetic tree based on the
  sequence alignment

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Step 1 Compute distances

• Estimate the number of amino acid substitutions
  between sequence pairs
• p distance pnd/n
• p proportion (p distance)
• nd number of aa differences
• n number of aa used

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Step 1 Compute distances

• Nonlinear relationship of p with t (time)
• Estimation of aa substitutions
• Poisson correction
• PC distance
• Gamma correction
• Gamma distance

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Step 2 Tree-building

• Common distance methods
• Neighbor Joining (NJ)
• UPGMA / WPGMA
• Least Square (LS)
• Minimal Evolution (ME)

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Neighbor Joining (NJ)

• Saitou, Nei (1987)
• Principle
• Clustering method
• Simplified minimal evolution principle
• Neighbors taxa connected by a single node in an
  unrooted tree
• Computational process Star tree, followed by a
  successive joining of neighbors and the creation
  of new pairs of neighbors
• Result
• A single final tree with branch length estimates
• unrooted tree

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Neighbor Joining (NJ)

• Sum of branch lengths in the star tree
• Calculate the sum of all branch lengths for all
  possible neighbors

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Neighbor Joining (NJ)

• Calculate Length X-Y
• Calculate again sum of all branch length

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Neighbor Joining (NJ)
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Neighbor Joining (NJ)

• Advantage
• Very efficient
• Also for large datasets
• Disadvantage
• Does not examine all possible topologies

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Bootstrap

• Used to test the robustness of a tree topology
• by Bradley Efron (1979)
• Felsenstein (1985)
• Principle new MSA datasets are created by
  choosing randomly N columns from the original
  MSA where N is the length of the original MSA
• 100-1000 replicates
• Bootstrap support values (75), 95, 98

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TP5 - 1st part, Exercises 1-5

• http//education.expasy.org/m07_phylo.html

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Ortholog databases phylogenetic databases

• Some databases providing orthologous groups and
  trees
• COG/KOG
• HOGENOM
• Ensembl
• OMA browser
• OrthoDB
• OrthoMCL

• Pfam
• PANDIT
• SYSTERS
• TreeBase
• Tree of Life

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Phylogenetic software

• Software packages
• Freely available
• Phylip
• BioNJ
• PhyML
• Tree Puzzle
• MrBayes
• Commercial
• PAUP
• MEGA

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Phylogenetic servers

• http//www.phylogeny.fr/
• http//bioweb.pasteur.fr/seqanal/phylogeny/intro-u
  k.html
• http//atgc.lirmm.fr/phyml/
• http//phylobench.vital-it.ch/raxml-bb/
• http//www.fbsc.ncifcrf.gov/app/htdocs/appdb/drawp
  age.php?appnamePAUP
• http//power.nhri.org.tw/power/home.htm

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Sequence methods

• Most common
• Maximum Parsimony (MP)
• Maximum Likelihood (ML)
• Baysian Inference

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Maximum Parsimony (MP)

• Originally developed for morphological characters
• Henning, 1966
• William of Ockham the best hypothesis is the one
  that requires the smallest number of assumptions

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Maximum Parsimony (MP)

• Principle
• Estimate the minimum number of substitutions for
  a given topology
• Parsimony-informative sites (exclude invariable
  sites and singletons)
• Searching MP trees
• Exhaustive search
• Branch-and-bound (Hendy-Penny, 1982)
• Good but time-consuming, if mgt20
• Heuristic search
• Result tree might not be the most parsimonious
  tree
• Result
• Multiple result trees are possible (strict
  consensus tree, majority-rule consensus tree)
• Most parsimonious tree vs true tree
• Unrooted result trees

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Maximum Parsimony (MP)

• Advantages
• Free from assumptions (model-free)
• Disadvantages
• Does not take into account homoplasy
• Long-branch attraction (LBA) creates wrong
  topologies, if the substitution rate varies
  extensively between lineages

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Maximum Likelihood (ML)

• Cavalli-Sforza, Edwards (1967), gene frequency
  data
• Felsenstein (1981), nucleotide sequences
• Kishino (1990), proteins
• Principle
• Maximizes the likelihood of observing the
  sequence data for a specific model of character
  state changes
• Likelihood of a site Sum of probabilities of
  every possible reconstruction of ancestral states
  at the internal nodes
• Likelyhood of the tree Product of the
  likelihoods for all sites (sum of log
  likelihoods)
• Result tree with the highest likelihood
• Maximized to estimate branch lengths, not
  topologies
• Search strategies rarely exhaustive, mostly
  heuristic
• NNI (Nearest neighbor interchanges)
• TBR (Tree bisection-reconnection)
• SPR (Subtree pruning and regrafting)

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Number of possible trees

• Unrooted bifurcating trees
• Rooted bifurcating trees

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Number of possible trees
Rooted
Unrooted
Leaves
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Number of possible trees
Leaves Unrooted Rooted 3 1 3 4
3 15 5 15 105 6 105 945
7 945 10395 8 10395 135135
9 135135 2027025 10 2027025 34459425
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Maximum Likelihood (ML)

• Methods
• ProML (Phylip)
• PhyML
• RaxML

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Tree evaluation

1. Topology
2. Comparison with species tree
3. Robustness, e.g. bootstrap
4. Branch lengths

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TP5 2nd part, Exercise 6

• http//education.expasy.org/m07_phylo.html