1. Molecular Evolution Revised 29/12/06
Bioinformatics ICES 2006
Molecular Evolution
Revised 29/12/06

2. Phylogeny is the inference of evolutionary relationships
All forms of life share a common origin.
deduce the correct trees for all species of life
to estimate the time of divergence between organisms since the time they last shared a common ancestor

3. Terminology
Phylogenetic trees that are used to assess the relationships of homologous proteins (or nucleotide sequences) in a family
Clade
Bifurcating node
Branch
OUT or external node
Internal node
Phylogram

4. Terminology

5. Terminology Species tree versus gene tree
In a species tree an internal node represents a speciation event
In a gene tree an internal node represents the divergence of an ancestral gene into two new genes with distinct sequences
Species tree <> Gene tree
horizontal gene transfer
gene duplications

6. Species tree versus gene tree
Gray et al.

7. Phylogenetic inference
Selection of sequences for analysis
Multiple sequence alignment
Tree building
Tree evaluation

8. Phylogenetic inference
selection of sequences for analysis
DNA:
Higher phylogenetic signal:
Synonymous vs nonsynonymous substitutions (detect negative and positive selection)
Protein:
Phylogenetic signal less predominant than in DNA
Better to construct a tree for evolutionary distant species or genes
RNA: rRNA often used for constructing species trees

9. Phylogenetic inference
2. multiple sequence alignment
This is a critical step in the analysis as in many cases the alignment of amino acids or nucleotides in a column implies that they share a common ancestor
If you misalign a group of sequences you will still be able to produce a tree. However, it is not likely to be biologically meaningful.
Crap in is crap out!
Inspect the alignment to be sure that all sequences are homologous
Some times with ClustalW distantly related sequences are not well aligned. Try different gap and extension parameters to improve the alignment
Only use these columns of the multiple alignment for which you have data for all organisms or sequences. Delete the columns for which this is not the case.
Delete columns with gaps

10. Phylogenetic inference
3. Tree building
Character-based methods
Non-character based methods
Methods based on an explicit
model of evolution
Maximum Likelihood Methods/Bayesian Phylogeny
Pairwise distance methods
Methods not based on an explicit
Maximum Parsimony Methods

11. Distance based methods
calculate the distances between molecular sequences using some distance metric
A clustering method (UPGMA, neighbour joining) is used to infer the tree from the pairwise distance matrix
treat the sequence from a horizontal perspective, by calculating a single distance between entire sequences
Advantage:
Fast
Allow using evolutionary models
Disadvantage:
sequences reduced to one number

12. Character based methods
treat the sequences from a vertical perspective
they search for each column of the alignment, the simplest explanation for how the characters evolved.
For instance, MP involves a search for a tree with the fewest number of amino acid (or nucleotide character changes that account for the observed differences between the protein (gene) sequences.

14. Phylogenetic inference
4. Tree evaluation: bootstrapping
sampling technique for estimating the statistical error in situations where the underlying sampling distribution is unknown
evaluating the reliability of the inferred tree - or better the reliability of specific branches
How to proceed:
From the original alignment, columns in the sequence alignment are chosen at random ‘sampling with replacement’
a new alignment is constructed with the same size as the original one
a tree is constructed
This process is repeated 100 of times

15. Phylogenetic inference
Show bootstrap values on phylogenetic trees
majority-rule consensus tree
map bootstrap values on the original tree

17. Maximum parsimony Principle
Select that tree that minimizes the total tree length = being the number of nucleic acid substitutions or amino acid replacements required to explain a given set of data.
Method
a particular topology is considered
for this topology, the ancestral sequences at each branching point are reconstructed
the minimum number of events to explain the sequence differences over the whole tree is computed: the minimum number of substitutions is computed for each nucleotide (or amino acid) site, and the numbers for all sites are added.
another tree topology is chosen

18. Maximum parsimony

19. Maximum parsimony OTU's rooted tree topologies
unrooted tree topologies 3 1 4 15 5
105 6
954 7
10395 8
135135 9
equation
Exhaustive search impossible
Heuristics needed

20. Maximum parsimony
Find different tree topologies that are 'equally parsimonious‘
Represent results as a consensus tree.
'strict' consensus tree
'majority-rule' consensus tree

21. Maximum parsimony
Only informative sites of the alignment are used in the construction of the tree: when there are at least two different kinds of characters, each represented at least two times

22. Maximum parsimony
Parsimony trees are usually only represented as a tree topology (cladogram): sometimes, the parsimony program cannot decide in which branches the substitutions have been taken place. It can not calculate branch lengths.

23. Maximum parsimony Assumptions Equal rate of evolution in all branches
Advantages
sequence information is not reduced to one number (such as for example in pairwise distance methods)
Disadvantages of maximum parsimony methods
can be slow for very large datasets
no correction for multiple mutations, i.e. no substitution model can be applied (see further)
sensitive to unequal rates of evolution in different lineages (see further) =>long branch attraction (voorbeeld hiervan?)

24. Pairwise distance methods
Distance calculation
Inferring the tree topology

25. Pairwise distance methods
Distance calculation
Approach:
align pairs of sequences and count the number of differences (Hamming distance).
For an alignment of length N with n sites at which there are differences: D= (n/N*100).
Problem:
observed differences <> actual genetic distances between the sequences.
=> dissimilarity is an underestimation of the true evolutionary distance, because of the fact that some of the sequence positions are the result of multiple events
Solution:
Use an evolutionary model that corrects for multiple mutations

26. Pairwise distance methods
Distance calculation

27. Pairwise distance methods
Distance calculation

28. Pairwise distance methods
Distance calculation
Other evolutionary models

29. Pairwise distance methods
Tree inference: UPGMA
Ultrametric trees are rooted trees, in which all the endnodes are equidistant from the root of the tree,
Assuming a molecular clock: i.e, that all sequences evolve at a similar rate

30. Pairwise distance methods
Tree inference: UPGMA
when two OTUs are grouped, we treat them as a new single OTU
when OTUs A, B (which have been grouped before) and C are grouped into a new node ‘u’, then the distance from node ‘u’ to any other node ‘k’ (e.g. grouping D and E) is simply computed as follows:

31. Pairwise distance methods
Tree inference: UPGMA

32. Pairwise distance methods
Tree inference: UPGMA
Advantages:
Fast
Allows incorporation of evolutionary models
Disadvantages:
Assumption of a molecular clock

33. Pairwise distance methods
Tree inference: neighbor joining
Additive distances can be fitted to an unrooted tree such that the evolutionary distance between a pair of OTUs equals the sum of the lengths of the branches connecting them, rather than being an average as in the case of cluster analysis
Tree construction methods: minimum evolution, the tree that minimizes the sum of the lengths of the branches is regarded the best estimate of the phylogeny
Drawback for the ME method: is that in principle all different tree topologies have to be investigated in order to find the ‘minimum’ tree.
The neighbour joining (NJ) method, developed by Saitou and Nei (1987) offers a heuristic approach to solve this problem

34. Pairwise distance methods
Tree inference: neighbor joining

35. Pairwise distance methods
Tree inference: neighbor joining

36. Pairwise distance methods
Tree inference: neighbor joining

37. Pairwise distance methods
Tree inference: neighbor joining
Advantages:
Fast
Allows incorporation of evolutionary models
No assumption of a molecular clock