1. - A brief introduction in 4 hours -
Phylogeny
- A brief introduction in 4 hours -

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

3. What is phylogeny?

4. Phylogeny is the evolutionary history and relationship of species.

5. Why is phylogeny of interest in a proteomics course?

6. 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 ….

7. 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

8. What is a phylogenetic tree?
Rooted or unrooted
bifurcating or multifurcating (solved or unsolved)

9. Gene duplication
Prokaryots: at least 50%
Eukaryots: >90%

10. 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)

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

12. 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 (1:1) orthologs = Orthologs with no (known) lineage-specific gene duplications subsequent to a particular speciation event
One-to-many (1:n) 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 (n:n) 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

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

14. Practical approach I
Actin-related protein 2 (first 60 columns of the alignment)
ARP2_A MESAP---IVLDNGTGFVKVGYAKDNFPRFQFPSIVGRPILRAEEKTGNVQIKDVMVGDE
ARP2_B MDSQGRKVIVVDNGTGFVKCGYAGTNFPAHIFPSMVGRPIVRSTQRVGNIEIKDLMVGEE
ARP2_C MDSQGRKVVVCDNGTGFVKCGYAGSNFPEHIFPALVGRPIIRSTTKVGNIEIKDLMVGDE
ARP2_D MDSQGRKVVVCDNGTGFVKCGYAGSNFPEHIFPALVGRPIIRSTTKVGNIEIKDLMVGDE
ARP2_E MDSKGRNVIVCDNGTGFVKCGYAGSNFPTHIFPSMVGRPMIRAVNKIGDIEVKDLMVGDE
*:* :* ******** *** *** . **::****::*: . *::::**:***:*
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?

15. Phylogenetic analysis
Select Data
Alignment
Select a data model
Select a substitution model
Tree-building
[Distance matrix]
Tree evaluation

16. 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

17. Alignment MSA methods See previous course … ClustalW muscle MAFFT
Probcons
T-coffee …
See previous course …

18. Data model = Characters selected for the analysis To be considered:
Each character should be homolog!
Missing data (in some OTU)
Number of characters
etc

19. 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

20. 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.

21. 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

22. Step 1: Compute distances
Estimate the number of amino acid substitutions between sequence pairs
p distance: p=nd/n
p = proportion (p distance)
nd= number of aa differences
n = number of aa used ^

23. Step 1: Compute distances
Nonlinear relationship of p with t (time)
Estimation of aa substitutions
Poisson correction
PC distance
Gamma correction
Gamma distance

24. Step 2: Tree-building Common distance methods Neighbor Joining (NJ)
UPGMA / WPGMA
Least Square (LS)
Minimal Evolution (ME)

25. 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

26. Neighbor Joining (NJ) Sum of branch lengths in the star tree
Calculate the sum of all branch lengths for all possible neighbors …

27. Neighbor Joining (NJ) Calculate Length X-Y
Calculate again sum of all branch length

28. Neighbor Joining (NJ)

29. Neighbor Joining (NJ) Advantage Disadvantage Very efficient
Also for large datasets
Disadvantage
Does not examine all possible topologies

30. 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
replicates
Bootstrap support values: (75%), 95%, 98%

31. TP5 - 1st part, Exercises 1-5

32. 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

33. Phylogenetic software
Software packages
Freely available
Phylip
BioNJ
PhyML
Tree Puzzle
MrBayes
Commercial
PAUP
MEGA

34. Phylogenetic servers http://www.phylogeny.fr/

35. Sequence methods Most common: Maximum Parsimony (MP)
Maximum Likelihood (ML)
Baysian Inference

36. 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

37. 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 m>20
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

38. 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

39. 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)

40. Number of possible trees
Unrooted bifurcating trees:
Rooted bifurcating trees:

41. Number of possible trees
Leaves
Rooted
Unrooted

42. Number of possible trees
Leaves Unrooted Rooted

43. Maximum Likelihood (ML)
Methods:
ProML (Phylip)
PhyML
RaxML …

44. Tree evaluation Topology Branch lengths Comparison with species tree
Robustness, e.g. bootstrap
Branch lengths

45. TP5 – 2nd part, Exercise 6