1. Methods of molecular phylogeny
Peter Norberg

2. Content Introduction to Evolution and taxonomy Phylogenetic analysis
Algorithmics
Applied phylogenetics
Computer Software
Practical session

3. Evolution Charles Darwin ”Tree of life” Phylogenetic tree
Root = Ancestor to all species

4. Rooted or unrooted trees?
Trees show evolutionary relationships
The root shows direction

5. Different representationsB C D A B C D A B C D A B C D A B C D

6. Trees can be based on: Outer appearances (example shape of bills)
Functionality
Complexity
A combination of…
………..
…..
DNA, RNA, AA, gene order….

7. Phylogenetic trees based on DNA
AATTGGCC
AATAGGCC
AATAGGCA
AGTTGGCG
AATAGGAC
AATAGGCA
AGTTGGCG
TATTGGCG
AATAGGAC
TATTGGCG
AATTGGCG

8. Phylogenetic trees based on DNA
AATTGGCC
AATAGGCC
AATAGGAC
AATTGGCG
AGTTGGCG
TATTGGCG
AATAGGCA
AATAGGAC
AATAGGCA
AGTTGGCG
TATTGGCG

9. Genomic region Same genomic region for all taxa! Not too similar
Not too diverged
Insertions/deletions

10. Sequence alignment Aligned: Not aligned: (1) AATGGCAACCGCATTCAGGATTTAA
(3) ATGGTAACCGCATTGAGGATTTAA
(2) AATGGTAACCGCAAGGATTTAA
(5) TGGTAACCGCATTCAGGAATTAA
(4) AATGGTAACCGCATTCAGGAATTA
Aligned:
Not aligned:
(1) AATGGCAACCGCATTCAGGATTTAA
(1) AATGGCAACCGCATTCAGGATTTAA
(2) AATGGTAACCGCAA GGATTTAA
(2) AATGGTAACCGCAAGGATTTAA
(3) ATGGTAACCGCATTGAGGATTTAA
(3) ATGGTAACCGCATTGAGGATTTAA
(4) AATGGTAACCGCATTCAGGAATTA
(4) AATGGTAACCGCATTCAGGAATTA
(5) TGGTAACCGCATTCAGGATTTAA
(5) TGGTAACCGCATTCAGGATTTAA

11. Sequence alignment, our example
AATTGGCC
AATAGGCC
AATAGGCA
AGTTGGCG
AATAGGAC
TATTGGCG
AATTGGCG
AATTGGCC
AATTGGCC
AATAGGCC
AATAGGCC
AATTGGCG
AATTGGCG
AATAGGAC
AATAGGAC
AGTTGGCG
AGTTGGCG
TATTGGCG
TATTGGCG
AATAGGCA
AATAGGCA

12. Phylogenetic principles
Similar DNA sequences = closely related
Inherited mutations.
Simplest “route”!
Homoplasy unlikely (not always true).

13. Homology vs. homoplasy Homology = similarity due to a common ancestor
Homoplasy = similarity due to convergent evolution, but independent origins

14. Algorithms for constructing phylogenetic trees
What is an algorithm?
Several different phylogenetic algorithms exist.
How do they work?

15. Algorithms for constructing phylogenetic trees
Distance matrices
Neighbour Joining
UPGMA
Maximum Parsimony
Maximum Likelihood
Bayesian inference

16. Distance matrices Based on the genetic distance
Genetic distance based on nucleotide substitutions
Typically # of differences / totalt # of nt
AATTCCGG
AATACCGG
AATTAATG
1 2 3
1 0
2 1 0
1 0

17. Neighbour Joining Cluster in pairs Shortest distance first
=> Similar sequences located closely together in the tree
Fast algorithm!
1 0 2 1 3 A B C D

18. Maximum Parsimony Utilizes so-called informative sites.
Simplest path (fewest mutations)
Build all possible trees.
Choose the tree, which requires the fewest mutations
Relatively fast

19. Maximum Parsimony, example1 2 3 4 a 1 2 3 4 a
AATTCC
AAGTCC
AAGTCT 1 3 2 4 a a a 1 2 4 3 a a 1 2 3 4 a 1 2 3 4 a 1 4 2 3 a

20. Maximum Likelihood and Bayesian inference
Statistical method including an evolutionary model
Summarize the likelihood for all columns
Calculate the likelihood for all possible trees
Good but slow!
Bayesian inference faster

21. To test all possible trees
Is it possible?
=> Takes too much time!!!!
To analyze 20 taxa gives ~1022 different possible trees ( )
What to do?
=> Use sophisticated algorithms to limit the search space…..
Usually produce good results, but not necessarily the best

22. To root an unrooted tree
Include an “outgroup”
Outgroup = more distantly related (but not too distantly)
Place the root where the outgroup connects to the tree

23. Rooting a tree
outgroup A F B D A F C D B C E E G G

24. Significance Is the tree reliable? Is it the only probable?
Bootstrap, Jack knife etc.

25. Bootstrap Construct several new sequence sets (1000 st.)
A new sequence set is generated by randomly picking of columns from the original set
Apply the phylogenetic algorithm on all sets.
Make one consensus tree from all trees

26. Bootstrapping A: AACTTAACCACGCTATCGATGCAATTATATA
B: AATTTGACTGCGGTACCGATCCAATTATATA
C: AATTTGACTGGGCTACCGATCCAATTATATA
D: AACTTAACCGCGCTACTGATCGAATTATATA
A: CACC
B: TGCT
C: TGCT
D: CAGC A D B C A C B D A B C D 96 1 3 96 1 3

27. Pitfalls? Homoplasy (convergent evolution) - Selection pressure
Hyper variable regions
Random events
Gene duplication
Recombination
- Different regions have different ancestries

28. Recombination A B
Recombination
Recombinants

29. Detection of recombinantsH X C A D E H B I F G

30. Detection of recombinantsH X A B C D E F G H I A B C D E F G H I

31. Phylogenetic networksA B C D A B C D R A B C D R A B C D R A B C D R

32. Applied phylogenetics
Reconstruct evolutionary history
Animals, plants, bacteria, viruses, plasmids, ……
Establish evolutionary mechanisms
Functional studies
Trace pandemic diseases
Forensic medicine

33. Examples

37. Practical session

38. Phylip Software package for phylogenetic analysis
Several small (command-line) applications
Many different algorithms
Widely used by the scientific community
seqboot -> Constructs bootstrap sets
dnapars -> Constructs a maximum parsimony tree
consence -> Constructs a consensus tree
drawtree -> Draws the tree

39. Herpes Simplex Virus Type 1 & 2
Usually asymptomatic
Cause oral and genital lesions, encephalitis, meningitis and keratitis
Transferred via direct contact
Life long infection in the sensorial ganglia
HSV-1: 70-80%, HSV-2: 20-30%
~100 nm in diameter.
Capsid surrounded by envelope.
Different glycoproteins in envelope.
Photo by Linda M. Stannard, University of Cape Town.

40. HSV-1 US7 (Glycoprotein I)

41. Clinical samples