lect2

Tree-thinking (cont.)Introduction to parsimony: 

Tree-thinking (cont.) Introduction to parsimony

The most important feature of a phylogenetic trees is its topology (the order of branching): 

A B C D E† F G The most important feature of a phylogenetic trees is its topology (the order of branching) F G C D E† A B Draw this topology with the taxa in the order: E-G-F-C-D-A-B

Which of the following has a different topology?: 

Which of the following has a different topology? A B C D E A B C D E B A C E D A B E D C

Various types of trees you will see: 

Various types of trees you will see R R R

Slide5: 

Which topology is different? A B C D E E D C A B F R A B C D E F R R F E D C A B R F

Evolutionary relatedness: 

Evolutionary relatedness Evolutionary relatedness = recency of common ancestry Topology contains the information needed to assess relative degree of relatedness

Slide7: 

Fish Newt Lizard Mouse Human Is a newt more closely related to a fish or a human?

Why do people go wrong?Looking “along” the top: 

Fish Newt Lizard Mouse Human Is a newt more closely related to a fish or a human? Why do people go wrong? Looking “along” the top

Slide9: 

This is not how evolution happened All these species are alive today: A living fish is not an ancestor of a newt The order “along the top” can change without changing the content of the tree Fish Newt Lizard Mouse Human

Now, is a newt more closely related to a fish or a human?: 

Fish Newt Lizard Mouse Human Now, is a newt more closely related to a fish or a human?

The tree has the same topology: 

The tree has the same topology

Trees depict descent not similarity: 

Is a crocodile more closely related to a lizard or an sparrow? Trees depict descent not similarity Turtle Lizard Crocodile Sparrow

Don’t be distracted by similarity: 

Turtle Lizard Crocodile Sparrow It doesn’t matter how many changes occurred here, the tree shape remains the same Don’t be distracted by similarity

Slide14: 

Fish Newt Lizard Mouse Human Is a newt more closely related to a lizard or a human?

The principle of phylogenetic inference: 

The principle of phylogenetic inference

General procedure: 

General procedure We score tips for some variable characters We have a model of how evolution might have given rise to the states we see We identify the tree (etc.) that is most compatible with our data

A hypothetical example: 

A hypothetical example AGTTGTACGTATGCCGA

Slide18: 

AGTTGTAGGTATGCCGA AGTAGTACGTATGCCGA AGTAGTACGTATGCCTA AGTAGCACGTATGACTA O A B C

Typical experimental strategy: 

Typical experimental strategy Extract DNA PCR with gene-specific probes Sequence PCR product Align DNA sequences from different species (data matrix)

Data matrix: 

Data matrix O A B C Taxa

Typical experimental strategy: 

Typical experimental strategy Extract DNA PCR with gene-specific probes Sequence PCR product Align DNA sequences from different species (data matrix) Phylogeny

Matrix -> Tree: 

Matrix -> Tree Use an algorithm Apply an optimality criterion

The algorithmic approach: 

The algorithmic approach Make assumptions about how evolution works Identify properties of the true tree under these assumptions Develop an algorithm for finding the tree with these properties (can be very fast) Two main ones: UPGMA - Assumes ultrametricity Neigbor-joining - Assumes additivity

The problem with algorithms: 

The problem with algorithms Even if the real world matches our model there is an element of chance in evolution The true tree may not be the one found We have no way of evaluating the degree of support for the algorithmic tree relative to other possible trees

Optimality criteria: 

Optimality criteria Make assumptions about how evolution works Identify properties that will tend to be maximized or minimized on true trees Score that property for all possible trees Trees with better scores will be more likely to be true (if the model is correct) Trees can be compared based on their score

Example of an optimality criterion: Parsimony: 

Example of an optimality criterion: Parsimony Favor the tree that can explain the distribution of character states with the minimum number of character-state changes

A hypothetical example: 

A hypothetical example AGTTGTACGTATGCCGA AGTAGTACGTATGCCGA AGTTGTAGGTATGCCGA AGTAGTACGTATGCCGA AGTAGTACGTATGCCTA AGTAGCACGTATGACTA AGTAGTACGT -ATGCCTA

Slide28: 

Data matrix AGTAGTACGTATGCCGA AGTAGTACGTATGCCTA AGTAGCACGTATGACTA O A B C AGTTGTAGGTATGCCGA 1111111 12345678901234567

Remove invariant characters: 

Remove invariant characters O A B C A T C C G A T C C T A C C A T T T G C G 1111111 12345678901234567

There are three possible arrangements that we need to consider:: 

There are three possible arrangements that we need to consider: C B A O Tree 1 Tree 2 Tree 3

These trees can be drawn without the root: 

These trees can be drawn without the root R R R

These trees can be drawn without the root: 

These trees can be drawn without the root

Map the characters onto tree 1: 

Map the characters onto tree 1 C B A O A B C O 1 2 3 4 5 A A A T T G C G T T G C C C C C A G T T Total cost (length) = steps

Actually there are two ways to map character 5: 

Actually there are two ways to map character 5 C B A O A B C O 3 G G T T C B A O Either way the character contributes __ steps to the overall cost

Slide35: 

Map the characters onto tree 2 A B C O A B C O 1 2 3 4 5 Total cost = A A A T T G C G T T G C C C C C A G T T

Map the characters onto tree 3: 

B C A O A B C O 1 2 3 4 5 Total cost = steps Map the characters onto tree 3 A A A T T G C G T T G C C C C C A G T T

What was the cost of each tree?: 

What was the cost of each tree?

The difference in tree length is all due to character 5: 

The difference in tree length is all due to character 5 A B C O 1 2 3 4 5 Parsimony informative Parsimony uninformative A A A T T G C G T T G C C C C C A G T T

Parsimony informative characters: 

Parsimony informative characters At least two states that occur in at least two taxa A C G T T T A T C G A T T A G G G T T A G G G A A A T ? C A T G ? C G

Redraw tree 2 with root in place: 

Redraw tree 2 with root in place R R This is the correct tree

Which rooted tree is correct?: 

Which rooted tree is correct? A B E O F G H C D A B E O F G H C D O F E D C A B G H O A D E H G F B C B A C A B C

Many issues glossed over: 

Many issues glossed over What if characters disagree? How is the tree score determined? How can we root the trees? How do we find the optimal tree? How can we evaluate the robustness of our conclusions?

How does character conflict arise? : 

How does character conflict arise? The tree is not divergent (ignore) A particular character changes more than once (Homoplasy) A B C D E F G A->G G->A Reversal

How can characters conflict arise? : 

How can characters conflict arise? The tree is not divergent (ignore) A particular character changes more than once (Homoplasy) A B C D E F G G->A G->A Parallelism/ Convergence

Parsimony can still work: 

Parsimony can still work If characters are independent (a key assumption) homoplasy will be randomly distributed Homoplasies will tend to cancel each other out Non-homoplastic changes will tend to agree Therefore,with enough characters the shortest tree is a good estimate of the true tree

The justification of parsimony: 

The justification of parsimony Good characters - mark real clades Bad characters - the rest Only bad characters contradict each other

Many issues glossed over: 

Many issues glossed over What if characters disagree? How is the tree score determined? How can we root the trees? How do we find the optimal tree? How can we evaluate the robustness of our conclusions?

Tree score calculation: 

Tree score calculation Ltot = ∑ Ln Wn I=n I=1 The tree score is the sum of the minimum number of weighted steps (Ln) for each character multiplied by the weight of that character (Wn)

How is the minimum number of steps calculated?: 

How is the minimum number of steps calculated? Postorder traversal algorithm: The tree is arbitrarily rooted Each internal node is inspected to see if there is an intersection in the possible states of its descendant nodes if not tree length is increased It is not necessary to identify all ancestral state reconstructions (this requires a preorder traversal)

Why weight characters?: 

Why weight characters? If we think some characters are less prone to homoplasy, we can upweight them Character weights are multiplied by the character length

We can also weight character state transitions: 

We can also weight character state transitions Common examples: Ordered character states (morphology) From state To state Step matrix

We can also weight character state transitions: 

We can also weight character state transitions Common examples: Transitions vs. transversions From state To state Step matrix

We can also weight character state transitions: 

We can also weight character state transitions Common examples: Gains less likely than loss (restriction sites) From state To state Step matrix (Asymmetric)

The weighting game: 

The weighting game When should you weight characters/character-states? If you think that they differ in evidential power How much should you modify weights? There is no simple formula It is probably better to err on the side of less extreme weights Often sensible to try a range of weights