Bioinformatics ICES 2006 Molecular Evolution Revised 29/12/06

BioinformaticsICES 2006

Molecular Evolution

Revised 29/12/06

• 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

Terminology

• Phylogenetic trees that are used to assess the relationships of homologous proteins (or nucleotide sequences) in a family

OUT or external node

Internal node

Branch

Bifurcating node

Clade

Phylogram

Terminology

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

Species tree versus gene tree

Gray et al.

Phylogenetic inference

1. Selection of sequences for analysis

2. Multiple sequence alignment

3. Tree building

4. Tree evaluation

1. 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



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


3. Tree building

Character-based methods

Non-character based methods

Methods based on an explicitmodel of evolution

Maximum Likelihood Methods/Bayesian Phylogeny

Pairwise distance methods

Methods not based on an explicitmodel of evolution

Maximum Parsimony Methods

Distance based methods

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

Character based methods

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.


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


Show bootstrap values on phylogenetic trees

• majority-rule consensus tree

• map bootstrap values on the original tree

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

Maximum parsimony

Maximum parsimony

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nN

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n

nN

nU

OTU's rooted tree topologies unrooted tree topologies

3 3 1

4 15 3

5 105 15

6 954 105

7 10395 954

8 135135 10395

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• Exhaustive search impossible

• Heuristics needed

Maximum parsimony

• Find different tree topologies that are 'equally parsimonious‘

• Represent results as a consensus tree.

– 'strict' consensus tree

– 'majority-rule' consensus tree

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

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.

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?)


• Distance calculation

• Inferring the tree topology


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

Distance calculation






Other evolutionary models



• 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

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:





Advantages:

• Fast

• Allows incorporation of evolutionary models

Disadvantages:

• Assumption of a molecular clock



• 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

Tree inference: neighbor joining








Advantages:

• Fast

• Allows incorporation of evolutionary models

• No assumption of a molecular clock


Documents

Bioinformatics ICES 2006 Molecular Evolution Revised 29/12/06