1. Lecture 19: Evolution/Phylogeny
Introduction to Bioinformatics

2. Bioinformatics
“Nothing in Biology makes sense except in the light of evolution” (Theodosius Dobzhansky ( ))
“Nothing in bioinformatics makes sense except in the light of Biology”

3. Evolution Most of bioinformatics is comparative biology
Comparative biology is based upon evolutionary relationships between compared entities
Evolutionary relationships are normally depicted in a phylogenetic tree

4. Where can phylogeny be used
For example, finding out about orthology versus paralogy
Predicting secondary structure of RNA
Studying host-parasite relationships
Mapping cell-bound receptors onto their binding ligands
Multiple sequence alignment (e.g. Clustal)

5. Phylogenetic tree (unrooted)
human
Drosophila
internal node
fugu
mouse
leaf
OTU – Observed taxonomic unit
edge

6. Phylogenetic tree (unrooted)
human
Drosophila
internal node
fugu
mouse
leaf
OTU – Observed taxonomic unit
edge

7. Phylogenetic tree (rooted)
time
edge
internal node (ancestor)
leaf
OTU – Observed taxonomic unit
Drosophila
human
fugu
mouse

8. How to root a tree f m
Outgroup – place root between distant sequence and rest group
Midpoint – place root at midpoint of longest path (sum of branches between any two OTUs)
Gene duplication – place root between paralogous gene copies h D D f m h 1 f m 3 1 4 2 2 3 1 1 1 5 h D f m h D
f-
f-
h-
f-
h-
f-
h-
h-

9. Combinatoric explosion
# sequences # unrooted # rooted
trees trees
,395
, ,135
,135 2,027,025
,027, ,459,425

10. Tree distances
Evolutionary (sequence distance) = sequence dissimilarity 5
human x
mouse x
fugu x
Drosophila x
human 1 2
mouse 1
fugu 6
Drosophila
human
mouse
fugu
Drosophila

11. Phylogeny methods
Parsimony – fewest number of evolutionary events (mutations) – relatively often fails to reconstruct correct phylogeny, but methods have improved recently
Distance based – pairwise distances
Maximum likelihood – L = Pr[Data|Tree]

12. Parsimony & Distance parsimony Sequences distance 1 2 3 4 5 6 7
Drosophila t t a t t a a
fugu a a t t t a a
mouse a a a a a t a
human a a a a a a t
Drosophila
mouse 1 6 4 5 2 3 7
human
fugu
distance
human x
mouse x
fugu x
Drosophila x
Drosophila 2
mouse 2 1 1 1
human
fugu
human
mouse
fugu
Drosophila

13. Maximum likelihood
If data=alignment, hypothesis = tree, and under a given evolutionary model,
maximum likelihood selects the hypothesis (tree) that maximises the observed data
Extremely time consuming method
We also can test the relative fit to the tree of different models (Huelsenbeck & Rannala, 1997)

14. Bayesian methods
Calculates the posterior probability of a tree (Huelsenbeck et al., 2001) –- probability that tree is true tree given evolutionary model
Most computer intensive technique
Feasible thanks to Markov chain Monte Carlo (MCMC) numerical technique for integrating over probability distributions
Gives confidence number (posterior probability) per node

15. Distance methods: fastest
Clustering criterion using a distance matrix
Distance matrix filled with alignment scores (sequence identity, alignment scores, E-values, etc.)
Cluster criterion

16. Phylogenetic tree by Distance methods (Clustering)1 2 3 4 5
Multiple
alignment
Similarity criterion
Similarity
matrix
Scores
5×5
Phylogenetic tree

17. Lactate dehydrogenase multiple alignment
Human KITVVGVGAVGMACAISILMKDLADELALVDVIEDKLKGEMMDLQHGSLFLRTPKIVSGKDYNVTANSKLVIITAGARQ
Chicken KISVVGVGAVGMACAISILMKDLADELTLVDVVEDKLKGEMMDLQHGSLFLKTPKITSGKDYSVTAHSKLVIVTAGARQ
Dogfish –KITVVGVGAVGMACAISILMKDLADEVALVDVMEDKLKGEMMDLQHGSLFLHTAKIVSGKDYSVSAGSKLVVITAGARQ
Lamprey SKVTIVGVGQVGMAAAISVLLRDLADELALVDVVEDRLKGEMMDLLHGSLFLKTAKIVADKDYSVTAGSRLVVVTAGARQ
Barley TKISVIGAGNVGMAIAQTILTQNLADEIALVDALPDKLRGEALDLQHAAAFLPRVRI-SGTDAAVTKNSDLVIVTAGARQ
Maizey casei -KVILVGDGAVGSSYAYAMVLQGIAQEIGIVDIFKDKTKGDAIDLSNALPFTSPKKIYSA-EYSDAKDADLVVITAGAPQ
Bacillus TKVSVIGAGNVGMAIAQTILTRDLADEIALVDAVPDKLRGEMLDLQHAAAFLPRTRLVSGTDMSVTRGSDLVIVTAGARQ
Lacto__ste -RVVVIGAGFVGASYVFALMNQGIADEIVLIDANESKAIGDAMDFNHGKVFAPKPVDIWHGDYDDCRDADLVVICAGANQ
Lacto_plant QKVVLVGDGAVGSSYAFAMAQQGIAEEFVIVDVVKDRTKGDALDLEDAQAFTAPKKIYSG-EYSDCKDADLVVITAGAPQ
Therma_mari MKIGIVGLGRVGSSTAFALLMKGFAREMVLIDVDKKRAEGDALDLIHGTPFTRRANIYAG-DYADLKGSDVVIVAAGVPQ
Bifido KLAVIGAGAVGSTLAFAAAQRGIAREIVLEDIAKERVEAEVLDMQHGSSFYPTVSIDGSDDPEICRDADMVVITAGPRQ
Thermus_aqua MKVGIVGSGFVGSATAYALVLQGVAREVVLVDLDRKLAQAHAEDILHATPFAHPVWVRSGW-YEDLEGARVVIVAAGVAQ
Mycoplasma -KIALIGAGNVGNSFLYAAMNQGLASEYGIIDINPDFADGNAFDFEDASASLPFPISVSRYEYKDLKDADFIVITAGRPQ
Distance Matrix
1 Human
2 Chicken
3 Dogfish
4 Lamprey
5 Barley
6 Maizey
7 Lacto_casei
8 Bacillus_stea
9 Lacto_plant
10 Therma_mari
11 Bifido
12 Thermus_aqua
13 Mycoplasma

19. Cluster analysis – (dis)similarity matrix
C1 C2 C3 C4 C5 C6 .. 1 2 3 4 5
Raw table
Similarity criterion
Similarity
matrix
Scores
5×5
Di,j = (k | xik – xjk|r)1/r Minkowski metrics
r = 2 Euclidean distance
r = 1 City block distance

20. Cluster analysis – Clustering criteria
Similarity
matrix
Scores
5×5
Cluster criterion
Phylogenetic tree
Single linkage - Nearest neighbour
Complete linkage – Furthest neighbour
Group averaging – UPGMA
Ward
Neighbour joining – global measure

21. Neighbour joining
Global measure – keeps total branch length minimal, tends to produce a tree with minimal total branch length
At each step, join two nodes such that distances are minimal (criterion of minimal evolution)
Agglomerative algorithm
Leads to unrooted tree

22. Neighbour joining y x x x y (c) (a) (b) x x x y y (f) (d) (e)
At each step all possible ‘neighbour joinings’ are checked and the one corresponding to the minimal total tree length (calculated by adding all branch lengths) is taken.

23. How to assess confidence in tree
Bayesian method – time consuming
The Bayesian posterior probabilities (BPP) are assigned to internal branches in consensus tree
Bayesian Markov chain Monte Carlo (MCMC) analytical software such as MrBayes (Huelsenbeck and Ronquist, 2001) and BAMBE (Simon and Larget,1998) is now commonly used
Uses all the data
Distance method – bootstrap:
Select multiple alignment columns with replacement
Recalculate tree
Compare branches with original (target) tree
Repeat times, so calculate different trees
How often is branching (point between 3 nodes) preserved for each internal node?
Uses samples of the data

24. The Bootstrap 1 2 3 4 5 6 7 8 C C V K V I Y S M A V R L I F S
M C L R L L F T
V K V S I I S I
V R V S I I S I
L R L T L L T L 5 1 2 3
Original 4 2x 3x 1 1 2 3
Non-supportive
Scrambled 5