1. Presented By Dr. Shazzad Hosain Asst. Prof. EECS, NSU
Phylogeny
Presented By
Dr. Shazzad Hosain
Asst. Prof. EECS, NSU

2. What is phylogenetics?
Phylogenetics is the study of evolutionary relationships among and within species.
birds
snakes
rodents
primates
crocodiles
marsupials
lizards

3. This is an example of a phylogenetic tree.
What is phylogenetics?
crocodiles
birds
lizards
snakes
rodents
primates
marsupials
This is an example of a phylogenetic tree.

4. Applications of phylogenetics
• Forensics:
Did a patient’s HIV infection result from an invasive dental procedure performed by an HIV+ dentist?
• Conservation:
How much gene flow is there among local populations of island foxes off the coast of California?
Invasive: Marked by the tendency to spread, especially into healthy tissue
The prion diseases are a large group of related neurodegenerative conditions, which affect both animals and humans
Prion diseases are unique in that they can be inherited, they can occur sporadically, or they can be infectious.
• Medicine:
What are the evolutionary relationships among the various prion-related diseases?
To be continued…

5. Phylogenetic concepts: Interpreting a Phylogeny
Which sequence is most closely related to B?
Sequence A
Sequence B
Sequence C
Sequence D
Sequence E
A, because B diverged from A more recently than from any other sequence.
Physical position in tree is not meaningful! Only tree structure matters.
Time

6. Phylogenetic concepts: Rooted and Unrooted Trees
Time A B C D
Root = A B C D
Root X = ? A B C D X

7. Rooting and Tree Interpretation
bacteria
archaea
oak
fruit fly
chicken
human
bacteria
archaebacteria
oak
fruit fly
chicken
human
– bones
– cell nuclei
bacteria
archaebacteria
oak
fruit fly
chicken
human
The Archaea constitute a domain of single-celled microorganisms. These microbes have no cell nucleus or any other membrane-bound organelles within their
+ cell nuclei
+ bones

8. Rooting Methods Outgroup Rooting a network of relationships
Given an unrooted network of relationships among four species of Carnivora [left], outgroup rooting uses an additional taxon (the outgroup) known from independent evidence to be less closely related to any of the other species (the ingroup) than they are to each other. The root is then placed on the branch between the outgroup and the ingroup. In this case, Lynx is a feloid carnivore in a separate superfamily from the four canoid carnivores. Inclusion of Lynx in the network analysis places it on the internode.This method requires accurate information as to ingroup / outgroup relationships.  

9. How Many Trees? (assuming bifurcation only) Unrooted trees
# sequences
# pairwise distances
# trees
# branches /tree
# branches
/tree 3 4 5 6 10 30 N

10. How Many Trees? 2N - 2 (2N - 3)! 2N - 2 (N - 2)! 2N - 3 (2N - 5)!58
4.95  1038 57
8.69  1036
435 30 18
34,459,425 17
2,027,025 45 10
945 9
105 15 6 8 7 5 3 4 1
# branches
/tree
# trees
# branches /tree
# pairwise distances
# sequences
Rooted trees
Unrooted trees

11. All tips are an equal distance from the root.
Tree Properties
Root
Ultrametricity
All tips are an equal distance from the root. X Y a b c d e
a = b + c + d + e
Root
Additivity
Distance between any two tips equals the total branch length between them. X Y a b c d e
XY = a + b + c + d + e
A phylogram is a phylogenetic tree that has branch spans proportional to the amount of character change
In simple scenarios, evolutionary trees are ultrametric and phylograms are additive.

12. Terminology
External nodes: things under comparison; operational taxonomic units (OTUs)
Internal nodes: ancestral units; hypothetical; goal is to group current day units
Root: common ancestor of all OTUs under study. Path from root to node defines evolutionary path
Unrooted: specify relationship but not evolutionary path
If have an outgroup (external reason to believe certain OTU branched off first), then can root
Topology: branching pattern of a tree
Branch length: amount of difference that occurred along a branch
In cladistics or phylogenetics, an outgroup is a (monophyletic) group of organisms that serve as a reference group for determination of the evolutionary relationship among three or more monophyletic groups of organisms.

13. Phylogeny Applications
Tree of Life: Analyzing changes that have occurred in evolution of different organisms
Phylogenetic relationships among genes can help predict which ones might have similar functions (e.g., ortholog detection)
Follow changes occurring in rapidly changing species (e.g., HIV virus)
Homolog : A gene related to a second gene by descent from a common ancestral DNA sequence. The term, homolog, may apply to the relationship between genes separated by the event of speciation (see ortholog) or to the relationship between genes separated by the event of genetic duplication (see paralog).
Ortholog : Orthologs are genes in different species that evolved from a common ancestral gene by speciation. Normally, orthologs retain the same function in the course of evolution. Identification of orthologs is critical for reliable prediction of gene function in newly sequenced genomes. (See also Paralogs.).

14. Phylogeny Packages PHYLIP, Phylogenetic inference package
evolution.genetics.washington.edu/phylip.html
Felsenstein
Free!
PAUP, phylogenetic analysis using parsimony
paup.csit.fsu.edu
Swofford

15. Similarity vs. Homology
sequences resemble one another
Homolog
sequences derived from common ancestor
Ortholog
homologous sequences within a species
Paralog
homologous sequences between species
Paralog : Paralogs are genes related by duplication within a genome. Orthologs retain the same function in the course of evolution, whereas paralogs evolve new functions, even if these are related to the original one.

16. Ortholog vs. Paralog Ortholog Paralog
genomic variation occurs after speciation
hence can be used for phylogeny of organism
Paralog
genetic duplication occurs before speciation
hence not suitable for phylogeny of organism

17. Homoplasy Sequence similarity NOT due to common ancestry
May arise due to parallelism or convergent evolution
Parallelism or parallel evolution
the development of a similar trait in related, but distinct, species descending from the same ancestor, but from different clades
Convergent evolution

18. Parallel evolution
Parallel evolution occurs when two species that have descended from the same ancestor remain similar over long periods of time because they independently acquire the same evolutionary adaptations. Parallel evolution occurs because genetically related species adapt to similar environmental changes in similar ways. After many years, the organisms may still resemble each other, even though they speciated in the distant past.

19. Convergent evolution
when species from different ancestors colonize the same environment, they may independently acquire the same adaptations. The evolution of species descended from different ancestors to become superficially similar because they are adapting to the same environment is called convergent evolution

20. Divergent Evolution

21. Phylogeny of what? Organisms Strains (closely related microbes)
Whole genome phylogeny
Ribosomal RNA (surrogate for whole genome)
Strains (closely related microbes)
Individual genes (or gene families)
Repetitive DNA sequences
Metabolic pathways
Secondary Structures
Any discrete character(s)
Human languages
Microbial communities
A microorganism (from the Greek: μικρός, mikrós, "small" and ὀργανισμός, organismós, "organism") or microbe is a microscopic organism, which may be a single cell[1] or multicellular organism.

22. Why compute phylogenetic trees?
Understand evolutionary history
Map pathogen strain diversity for vaccines
Assist in epidemiology
Of infectious diseases
Of genetic defects
Aid in prediction of function of novel genes
Biodiversity studies
Understanding microbial ecologies

23. Tree Building Exercises

24. Computational Approaches to Phylogenetic Tree Computation
Distance Based Methods
UPGMA
Neighbor joining
Character State Methods
Maximum Parsimony Method
Maximum Likelihood Methods
Tree merging
Consensus trees, super-trees

25. What data is used to build trees?
Traditionally: morphological features (e.g., number of legs, beak shape, etc.)
Today: Mostly molecular data (e.g., DNA and protein sequences)

26. Data for Phylogeny Can be classified into two categories:
Numerical data
Distance between objects
e.g., distance(man, mouse)=500,
distance(man, chimp)=100
Usually derived from sequence data
Discrete characters
Each character has finite number of states
e.g., number of legs = 1, 2, 4
DNA = {A, C, T, G}

28. UPGMA

29. UPGMA

31. 2. Determine the evolutionary distances and build distance matrix - A simple example
AGGCCATGAATTAAGAATAA
AGCCCATGGATAAAGAGTAA
AGGACATGAATTAAGAATAA
AAGCCAAGAATTACGAATAA
Distance Matrix
In this example the evolutionary distance is expressed as the number of nucleotide differences for each sequence pair. For example, sequences 1 and 2 are nucleotides in length and have four differences, corresponding to an evolutionary difference of 4/ = 0.2. 1 2 3 4 -
0.2
0.05
0.15
0.25
0.4
Given 4 DNA sequences, look for differences
Symmetrical matrix, no values on the diagonal
Usually more complex to generate, I’m not going to go into detail here
laboratory methods for examining sequences can be imprecise and often lead to incomplete distance matrices

32. 3. Phylogenetic Tree Construction example (UPGMA algorithm)
UPMGA (Michener & Sokal 1957)
Bear Raccoon
Dij
Bear
Raccoon
Weasel
Seal -
0.26
0.34
0.29
0.42
0.44
UPMGA is a so called distance based method and needs complete distance matrices
Simple sequential clustering algorithm, generally the algorithm joins the two nearest clusters (species) until only one cluster is left.
1. Pick smallest entry Dij
2. Join the two intersecting species and assign branch lengths Dij/2 to each of the nodes

33. 3. Phylogenetic Tree Construction example (UPGMA algorithm)
Dij
Bear
Raccoon
Weasel
Seal -
0.26
0.34
0.29
0.42
0.44
Bear Raccoon
3. Compute new distances to the other species using arithmetic means
Repeat steps 1 to 3

34. 3. Phylogenetic Tree Construction example (UPGMA algorithm)
Dij BR
Weasel
Seal -
0.38
0.365
0.44
Bear Raccoon Seal
0.13
New matrix
1. Pick smallest entry Dij
2. Join the two intersecting species and assign branch lengths Dij/2 to each of the nodes

35. 3. Phylogenetic Tree Construction example (UPGMA algorithm)
Dij BR
Weasel
Seal -
0.38
0.365
0.44
Bear Raccoon Seal
0.13
Repeat steps 1 to 3
Compute new distances to the other species using arithmetic means

36. 3. Phylogenetic Tree Construction example (UPGMA algorithm)
Dij
BRS
Weasel -
0.4
Bear Raccoon Seal Weasel
Simple algorithm
Generally satisfactory results
Next talk more sophisticated algorithms
Pick smallest entry Dij.
Join the two intersecting species and assign branch lengths Dij/2 to each of the nodes.
Done!

37. Downside of UPGMA
Assume molecular clock (assuming the evolutionary rate is approximately constant)
Generates only rooted tree
Trees are ultrametric
Doesn’t work the following case: 37

38. Computational Approaches to Phylogenetic Tree Computation
Distance Based Methods
UPGMA
Neighbor joining
Character State Methods
Maximum Parsimony Method
Maximum Likelihood Methods
Tree merging
Consensus trees, super-trees

39. Neighbor-joining method
Developed in 1987 by Saitou and Nei
Works in a similar fashion to UPGMA
Still fast – works great for large dataset
Doesn’t require the data to be ultrametric
Great for largely varying evolutionary rates

40. How to construct a tree with Neighbor-joining method?
Step 1:
Calculate sum all distance from x and divide by (leaves – 2)
Sx = (sum all Dx) / (leaves - 2)
Step 2:
Calculate pair with smallest M
Mij = Distance ij – Si – Sj
Step 3:
Create a node U that joins pair with lowest Mij
S1U = (Dij / 2) + (Si – Sj) / 2

41. How to construct a tree with Neighbor-joining method?
Step 4:
Join I and j according to S and make all other taxa in form of a star
Step 5:
Recalculate new distance matrix of all other taxa to U with:
DxU = Dix + Djx - Dij

42. Example of Neighbor-joiningC D E 5 4 7 10 6 9 F 8 11
Step 1: S calculation : Sx = (sum all Dx) / (leaves - 2)
S(A) = ( ) / 4 = 7.5
S(B) = ( ) / 4 = 10.5
S(C) = ( ) / 4 = 8
S(D) = ( ) / 4 = 9.5
S(E) = ( ) / 4 = 8.5
S(F) = ( ) / 4 = 11

43. Example of Neighbor-joining cont 1
Step 2: Calculate pair with smallest M
Mij = Distance ij – Si – Sj
Smallest are
M(AB) = d(AB) – S(A) –S(B) = 5 – 7.5 – 10.5= -13
M(DE) = 5 – 9.5 – 8.5 = -13 A B C D E
-13
-11.5
-10
-10.5 F
-11

44. Example of Neighbor-joining cont 2
Step 3: Create a node U
S1U = (Dij / 2) + (Si – Sj) / 2
U1 joins A and B:
S(AU1) = d(AB) / 2 + (S(A) – S(B)) / 2
= 5 / 2 + ( ) / 2 = 1
S(BU1) = d(AB) / 2 + (S(B) – S(A)) / 2
= 5 / 2 + (10.5 – 7.5) / 2 = 4

45. Example of Neighbor-joining cont 3
Step 4: Join A and B according to S, and make all other taxa in form of a star. Branches in black are unknown length and Branches in red are known length

46. Example of Neighbor-joining cont 4
Step5: Calculate new distance matrix
Dxu = (Dix + Djx – Dij) / 2
d(CU) = (d(AC) + d(BC) - d(AB)) / 2
= ( ) / 2 =3
d(DU) = d(AD) + d(BD) - d(AB) / 2 = 6
Same as EU and FU
Then we get the new distance matrix U1 C D E 3 6 7 5 F 8 9

47. Example of Neighbor-joining cont 5
Repeat 1 to 5 until all branches are done
In this example, we will get this at the end

48. Downside of Neighbor-joining
Generates only one possible tree
Generates only unrooted tree

49. Computational Approaches to Phylogenetic Tree Computation
Distance Based Methods
UPGMA
Neighbor joining
Character State Methods
Maximum Parsimony Method
Maximum Likelihood Methods
Tree merging
Consensus trees, super-trees

50. Maximum Parsimony Method
Parsimony-score:
Number of character-changes (mutations) along the evolutionary tree
(tree containing labels on internal vertices)
Example:
Score = 4
Score = 3
AGA
AAA
AAG
GGA
AAA 1 2 1
AGA
AAA
AAG
GGA
Most parsimonious tree:
 Tree with minimal parsimony score
Minimal Evolution Principle

51. Small vs. Large Parsimony
We break the problem into two:
Small parsimony: Given the topology find the best assignment to internal nodes
Large parsimony: Find the topology which gives best score
Large parsimony is NP-hard
We’ll show solution to small parsimony (Fitch and Sankoff’s algorithms)
Input to small parsimony:
tree with character-state assignments to leaves
Example:
A: CAGGTA
B: CAGACA
C: CGGGTA
D: TGCACT
E: TGCGTA
Aardvark
Bison
Chimp
Dog
Elephant

52. Fitch’s Algorithm
Execute independently for each character:
Bottom-up phase: Determine set of possible states for each internal node
Top-down phase: Pick states for each internal node
Dynamic Programming framework 1 2
1 shows bottom up approach
2 shows top down approach
Aardvark
Bison
Chimp
Dog
Elephant
CAGGTA
CGGGTA
TGCGTA
CAGACA
TGCACT

53. Fitch’s Algorithm Bottom-up phase
Determine set of possible states for each internal node
Initialization: Ri = {si}
Do a post-order (from leaves to root) traversal of tree
Determine Ri of internal node i with children j, k: T T
Parsimony-score =
# union operations
AGT CT GT
score = 3 C T G T A T

54. Fitch’s Algorithm Top-down phase
Pick states for each internal node
Pick arbitrary state in Rroot for the root
Do pre-order (from root to leaves) traversal of tree
Determine sj of internal node j with parent i: T
Complexity: O(mnk)
#characters
#taxa/nodes
#states T
AGT CT GT
score = 3 C T G T A T

55. Weighted Parsimony Sankoff’s algorithm
Each mutation a↔b costs differently - S(a,b).
Bottom-up phase: Determine Ri(s) – cost of optimal state-assignment for subtree of i, when it is assigned state s.
Top-down phase: Pick optimal states for each internal node
Fitch’s algorithm as special case:
Ri – set of states which yield minimal-cost subtree of i
Same as algorithm for
optimal lifted tree alignment
(Tutorial #4)

56. Sankoff’s Algorithm Bottom-up phase
Determine Ri(s) for each internal node
Initialization:
Do a post-order (from leaves to root) traversal of tree
Determine Ri of internal node i with children j, k:
Natural generalization
For non-binary trees
Remember pointers
ss’ C T G T A T

57. Sankoff’s Algorithm Top-down phase
Pick states for each internal node
Select minimal cost character for root (s minimizing Rroot(s))
Do pre-order (from root to leaves) traversal of tree:
- For internal node j, with parent i, select state that produced
minimal cost at i (use pointers kept in 1st stage)
Complexity: O(mnk2)
#characters
#taxa/nodes
#states C T G T A T

58. Fitch’s Algorithm as special case of Sankoff’s algorithm
Unweighted parsimony:
Sankoff’s algorithm:
Ri(s) - cost of optimal subtree of i, when it is assigned state s
Fitch’s algorithm:
Score(i) - cost of optimal state-assignment for subtree of i
Ri set of optimal state-assignment for subtree of i
We need to show that:
Optimal tree assigns node i with state from Ri.
Fitch’s bottom-up recursive formula for Ri. is correct:
Check for yourselves

59. Fitch’s Algorithm as special case of Sankoff’s algorithm
Unweighted parsimony:
Score(i) - cost of optimal state-assignment for subtree of i
Ri set of optimal state-assignment for subtree of i
We need to show that:
Optimal tree assigns node i with state from Ri.
Trivially true for the root
Assume (to the contrary) that in an optimal assignment, some node – j is assigned sj∉Rj
root i j
Parsimony-score is integer
Why is this not
the case for the
weighted version?
sj∉Rj  Rj(sj) ≥ Score(j)+1 
By switching from sj to some s∊Rj
we do not raise the parsimony-score

60. Computational Approaches to Phylogenetic Tree Computation
Distance Based Methods
UPGMA
Neighbor joining
Character State Methods
Maximum Parsimony Method
Maximum Likelihood Methods
Tree merging
Consensus trees, super-trees

61. Maximum likelihood
Originally developed for statistics by Ronald Fisher between 1912 and 1922
Therefore, explicit statistical model
Uses all the data
Tends to outperform parsimony or distance matrix methods

62. How to construct a tree with Maximum likelihood?
Step 1: Make all possible trees depending on the number of leaves
Step 2: Calculate likelihood of occurring with the given data
L(Tree) = probability of each tree.
optimizing branch length
generating tree topology
Step 3: Pick the tree that have the highest likelihood.

63. Sounds really great?
Num of leaves
Num of possible trees 3 1 5 15 10 13 20
Maximum likelihood is very expensive and extremely slow to compute

64. Comparison of Methods Distance Maximum parsimony Maximum likelihood
Uses only pairwise distances
Uses only shared derived characters
Uses all data
Minimizes distance between nearest neighbors
Minimizes total distance
Maximizes tree likelihood given specific parameter values
Very fast
Slow
Very slow
Easily trapped in local optima
Assumptions fail when evolution is rapid
Highly dependent on assumed evolution model
Good for generating tentative tree, or choosing among multiple trees
Best option when tractable (<30 taxa, homoplasy rare)
Good for very small data sets and for testing trees built using other methods

65. Methods of evaluating trees
Bootstrap: resample initial data set with one datum removed and replaced with another member
Jackknife: resample initial distribution with one datum missing and not replaced
MCMC: complex, but generates random numbers to produce a desired probability distribution with which to compare model

66. Phylogeny Flowchart

67. Difference in Methods
Maximum-likelihood and parsimony methods have models of evolution
Distance methods do not necessarily
Useful aspect in some circumstances
E.g., trees built based on whole genomes, presence or absence of genes
Religious wars over which methods to use
Most people now believe ML based methods are best: most sensitive at large evolutionary distances – but also most time-consuming & depend on specific model of evolution used
Most commonly used packages contain software for all three methods: may want to use more than 1 to have confidence in built tree

68. Phylip URL: http://evolution.genetics.washington.edu/phylip.html
Parsimony
DNApenny or Protpars
Distance
Compute distance measure using DNAdist or Protdist
Neighbor (can use NJ or UPGMA) ML
DNAml

69. Visualising trees Treeview
You can change the graphic presentation of a tree (cladogram, rectangular cladogram, radial tree, phylogram), but not change the structure of a tree

72. Reference
Mostly from Web