1. Phylogenetic basis of systematics
Linnaeus:
Ordering principle is God.
Darwin:
Ordering principle is shared descent from common ancestors.
Today, systematics is explicitly based on phylogeny.

2. Goals of Phylogenetic Analysis
Given a multiple sequence alignment, determine the ancestral relationships among the species.
We assume that residues in a column are homologous, and that all columns have the same history.
Time
Hu Ch Go Gi

3. Types of Phylogenic Trees:
1. Cladogram:
show the relationships between different organisms
branch lengths are arbitary
2. Phylogram:
branches that represent evolutionary time and amount of change.

5. Data
Biomolecular sequences: DNA, RNA, amino acid, in a multiple alignment
Molecular markers (e.g., SNPs, etc.)
Morphology
Gene order and content
These are “character data”: each character is a function mapping the set of taxa to distinct states (equivalence classes), with evolution modelled as a process that changes the state of a character

6. DNA Sequence Evolution
-3 mil yrs
-2 mil yrs
-1 mil yrs
today
AAGACTT
TGGACTT
AAGGCCT
AGGGCAT
TAGCCCT
AGCACTT
AGCGCTT
AGCACAA
TAGACTT
TAGCCCA
AAGACTT
TGGACTT
AAGGCCT
AGGGCAT
TAGCCCT
AGCACTT
AAGGCCT
TGGACTT
TAGCCCA
TAGACTT
AGCGCTT
AGCACAA
AGGGCAT
TAGCCCT
AGCACTT

7. Phylogenetic Analyses
Step 1: Gather sequence data, and estimate the multiple alignment of the sequences.
Step 2: Reconstruct trees on the data. (This can result in many trees.)
Step 3: Apply consensus methods to the set of trees to figure out what is reliable.

8. Phylogeny Problem AGGGCAT TAGCCCA TAGACTT TGCACAA TGCGCTT U V W X Y X

9. Types of Phylogenetic Methods
Character-based
Parsimony
Likelihood
Distance-based
Neighbor joining (NJ)
UPGMA
Involve optimizing a criterion based on fit of the residues to the tree.
Involve optimizing a criterion based on fit of a matrix of pairwise distances to the tree

10. Select the tree that best recreates the observed pairwise distances.
Parsimony
Select the tree that explains the data with the fewest number of substitutions.
Likelihood
Select the tree that has the highest probability of producing the observed data
Distance
Select the tree that best recreates the observed pairwise distances.

11. Phylogenetic Tree Building
Two basic types:
Gene/protein tree: represents evolutionary history of genes/proteins
Species tree: represents the evolutionary history of species based on characters
(like protein sequences)
Rooted, binary
tree
Unrooted, binary
tree

12. Phylogenetic Tree Building
Two basic types:
Gene/protein tree: represents evolutionary history of genes/proteins
Species tree: represents the evolutionary history of species based on characters
(like protein sequences)
Rooted, binary
tree
Unrooted, binary
tree
* Can root a tree using an outgoup: known distant relative

13. (modern observations)
Branch lengths (“distance”) ~ time
Root
(ancestral species)
Edges
Nodes
(common ancestor)
Leaves
(modern observations)

14. (modern observations)
Branch lengths (“distance”) ~ time
Root
(ancestral species)
Why is the structure
of the tree important?
Edges
Nodes
(common ancestor)
Leaves
(modern observations)

15. (modern observations)
Branch lengths (“distance”) ~ time
Root
(ancestral species)
Why is the structure
of the tree important?
Branching represents
speciation into
two new species
Edges
Nodes
(common ancestor)
Leaves
(modern observations)

16. Branch lengths (“distance”) ~ time8 7
Root
(ancestral species) 6 5 4 3 2 1
This tree can also be denoted in text format

17. Branch lengths (“distance”) ~ time8 7
Root
(ancestral species) 6 5 4 3 2 1
This tree can also be denoted in text format
( ( ( (3,4) , (5,6) ), 7 ), (1,2) ), 8

18. Building phylogenetic trees
Distance based methods
a. Calculate evolutionary distances between sequences
b. Build a tree based on those distances
Maximum Parsimony (character based method)
a. Find the simplest tree that explains the data with the fewest # of substitutions
Maximum Likelihood (probabilistic method based on explicit model)
a. Find the tree that is most likely, given an evolutionary model

19. Building phylogenetic trees
Distance based methods
Maximum Parsimony (character based method)
Search all possible trees and find the one requiring the fewest substitutions
A A G a
G G A b
A A A c
A G A d

20. Building phylogenetic trees
Distance based methods
Maximum Parsimony (character based method)
Search all possible trees and find the one requiring the fewest substitutions
A A G a
G G A b
A A A c
A G A d

21. Building phylogenetic trees
Distance based methods
Maximum Parsimony (character based method)
Search all possible trees and find the one requiring the fewest substitutions
A A G a
A A A c
G G A b
A G A d
What are the ancestral sequences at each node?
How many base changes are required for this tree?

22. Building phylogenetic trees
Distance based methods
Maximum Parsimony (character based method)
Search all possible trees and find the one requiring the fewest substitutions
A A A
A A G a
A A A c
G G A b
A G A d
A A A or
A G A
A G A
What are the ancestral sequences at each node?
How many base changes are required for this tree?
3 changes are required.

23. Building phylogenetic trees
Distance based methods
Maximum Parsimony (character based method)
Search all possible trees and find the one requiring the fewest substitutions
A A A
A A G a
A A A c
G G A b
A G A d
A A A or
A G A
A G A
The score of the tree is the number of character changes.
MP aims to minimize the score of tree.

24. How can you tell if your tree is significant?
Bootstrapping: how dependent is the tree on the dataset
1. Randomly choose n objects from your dataset of n, with replacement
2. Rebuild the tree based on the subset of the data
3. Repeat 1,000 – 10,000 times
4. How often are the same children joined?
If a given node is represented in <x trials, collapse the node for a ‘consensus’ tree
Jackknifing: how dependent is the tree on the dataset
1. Randomly choose k objects from your dataset of n, without replacement
2. Rebuild the tree based on the subset of the data
3. Repeat 1,000 – 10,000 times
4. How often are the same children joined?

25. How can you tell if your tree is significant?70
100 80 95
100

27. Maximum Likelihood tree showing Bayesian Inference/Maximum Parsimony/Maximum Likelihood support value at each node