1. Multiple Sequence alignment and Phylogenetic trees

2. Multiple Sequence Alignment MSA

3. VTISCTGSSSNIGAG-NHVKWYQQLPG VTISCTGTSSNIGS--ITVNWYQQLPG LRLSCSSSGFIFSS--YAMYWVRQAPG LSLTCTVSGTSFDD--YYSTWVRQPPG PEVTCVVVDVSHEDPQVKFNWYVDG-- ATLVCLISDFYPGA--VTVAWKADS-- AALGCLVKDYFPEP--VTVSWNSG--- VSLTCLVKGFYPSD--IAVEWWSNG-- Like pairwise alignment BUT compare n sequences instead of 2 Rows represent individual sequences Columns represent ‘same’ position Gaps allowed in all sequences

4. How to find the best MSA GTCGTAGTCG-GC-TCGAC GTC-TAG-CGAGCGT-GAT GC-GAAG-AG-GCG-AG-C GCCGTCG-CG-TCGTA-AC GTCGTAGTCGGCTCGAC GTCTAGCGAGCGTGAT GCGAAGAGGCGAGC GCCGTCGCGTCGTAAC 1*1 2*0.75 11*0.5 Score=8 4*1 11*0.75 2*0.5 Score=13.25 Score : 4/4 =1, 3/4 =0.75, 2/4=0.5, 1/4= 0

5. Alignment of 3 sequences: Complexity: length A  length B  length C Aligning 100 proteins, 1000 amino acids each Complexity: 10 300 table cells

6. Feasible Approach Based on pairwise alignment scores –Build n by n table of pairwise scores Align similar sequences first –After alignment, consider as single sequence –Continue aligning with further sequences Progressive alignment (Feng & Doolittle).

7. –For n sequences, there are n  (n-1)/2 pairs GTCGTAGTCG-GC-TCGAC GTC-TAG-CGAGCGT-GAT GC-GAAG-AG-GCG-AG-C GCCGTCG-CG-TCGTA-AC

8. 1 GTCGTAGTCG-GC-TCGAC 2 GTC-TAG-CGAGCGT-GAT 3 GC-GAAGAGGCG-AGC 4 GCCGTCGCGTCGTAAC 1 GTCGTA-GTCG-GC-TCGAC 2 GTC-TA-G-CGAGCGT-GAT 3 G-C-GAAGA-G-GCG-AG-C 4 G-CCGTCGC-G-TCGTAA-C

9. CLUSTAL method Higgins and Sharp 1988 –ref: CLUSTAL: a package for performing multiple sequence alignment on a microcomputer. Gene, 73, 237–244. [Medline][Medline] An approximation strategy (heuristic algorithm) yields a possible alignment, but not necessarily the best one Applies Progressive Sequence Alignment

10. For what do we need MSA? 10

11. 11 Phylogeny is the inference of evolutionary relationships. Traditionally, phylogeny relied on the comparison of morphological features between organisms. Today, molecular sequence data are mainly used for phylogenetic analyses. One tree of life A sketch Darwin made soon after returning from his voyage on HMS Beagle (1831–36) showed his thinking about the diversification of species from a single stock (see Figure, overleaf). This branching, extended by the concept of common descent,

12. 12 Haeckel (1879)Pace (2001)

13. 13 Molecular phylogeny uses trees to depict evolutionary relationships among organisms. These trees are based upon DNA and protein sequence data Human Chimpanzee Gorilla Orangutan Gorilla Chimpanzee Orangutan Human Molecular analysis: Chimpanzee is related more closely to human than the gorilla Pre-Molecular analysis: The great apes (chimpanzee, Gorilla & orangutan) Separate from the human

14. 14 What can we learn from phylogenetics tree?

15. Was the extinct quagga more like a zebra or a horse? 1. Determine the closest relatives of one organism in which we are interested

16. 16 Which species are closest to Human? Human Chimpanzee Gorilla Orangut an Gorilla Chimpanzee Orangutan Human

17. 17 Example Metagenomics A new field in genomics aims the study the genomes recovered from environmental samples. A powerful tool to access the wealthy biodiversity of native environmental samples 2. Help to find the relationship between the species and identify new species

18. 10 6 cells/ ml seawater 10 7 virus particles/ ml seawater >99% uncultivated microbes

19. 19 From : “The Sorcerer II Global Ocean Sampling Expedition: Metagenomic Characterization of Viruses within Aquatic Microbial Samples” Williamson et al, PLOS ONE 2008

20. 3. Discover a function of an unknown gene or protein 20 RBP1_HS RBP2_pig RBP_RAT ALP_HS ALPEC_BV ALPA1_RAT ECBLC Hypothetical protein X

21. 21 Relationships can be represented by Phylogenetic Tree or Dendrogram A B C D E F

22. 22 Phylogenetic Tree Terminology Graph composed of nodes & branches Each branch connects two adjacent nodes A B C D E F R

23. 23 Rooted tree Human Chimp Chicken Gorilla Human Chimp Chicken Gorilla Un-rooted tree Phylogenetic Tree Terminology

24. 24 Rooted vs. unrooted trees 1 2 3 31 2

25. 25 How can we build a tree with molecular data? -Trees based on DNA sequence (rRNA) -Trees based on Protein sequences

26. 26 Approach 1 - Distance methods Algorithms : - UPGMA (rooted), - Neighbor joining (unrooted) Approach 2 - State methods Algorithms: –Maximum parsimony (MP) –Maximum likelihood (ML)

27. Basic algorithm for constructing a rooted tree Unweighted Pair Group Method using Arithmetic Averages (UPGMA) Assumption: Divergence of sequences is assumed to occur at a constant rate  Distance to root is equal Sequence a ACGCGTTGGGCGATGGCAAC Sequence b ACGCGTTGGGCGACGGTAAT Sequence c ACGCATTGAATGATGATAAT Sequence d ACACATTGAGTGTGATAATA abcd

28. 28 abcd a0875 b8039 c7308 d5980 Basic Algorithm UPGMA Distance Table Sequence a ACGCGTTGGGCGATGGCAAC Sequence b ACACATTGAGTGTGATCAAC Sequence c ACACATTGAGTGAGGACAAC Sequence d ACGCGTTGGGCGACGGTAAT Distances * Sequences Dab = 8 Dac = 7 Dad = 5 Dbc = 3 Dbd = 9 Dcd = 8 * Can be calculated using different distance metrics

29. 29 abcd a0875 b8039 c7308 d5980 a d c b Choose the nodes with the shortest distance and fuse them. Selection step

30. 30 a Then recalculate the distance between the rest of the remaining sequences (a and d) to the new node (e) and remove the fused nodes from the table. d c,b e a ade a056 d507 e670 D (EA) = (D(AC)+ D(AB)-D(CB))/2 Next Step D (ED) = (D(DC)+ D(DB)-D(CB))/2 abcd a0875 b8039 c7308 d5980

31. 31 In order to get a tree, un-fuse c and b by calculating their distance to the new node (e) !!!The distances Dce and Dde are calculated independently (formula will be given in tirgul) d c e a ade a056 d507 e670 b D ce D de Next Step

32. 32 a a,d c e ade a056 d507 e670 b D ce D de f Next… We want to fuse the next closest nodes

33. 33 a c e fe f04 e40 b D af D de f d D ce D bf Finally D (EF) = (D(EA)+ D(ED)-D(AD))/2 We need to calculate the distance between e and f

34. 34 a d c b acbd f e

35. 35 IMPORTANT !!! Usually we don’t assume a constant mutation rate and in order to choose the nodes to fuse we have to calculate the relative distance of each node to all other nodes. Neighbor Joining (NJ)- is an algorithm which is suitable to cases when the rate of evolution varies

36. 36 Neighbor Joining (NJ) Reconstructs an unrooted tree Calculates branch lengths Based on pairwise distances In each stage, the two nearest nodes of the tree are chosen and defined as neighbors in our tree. This is done recursively until all of the nodes are paired together.

37. 37 Advantages -It is fast and thus suited for large datasets -Permits lineages with largely different branch lengths Disadvantages - Sequence information is reduced - Gives only one possible tree Advantages and disadvantages of the neighbor-joining method

38. Problems with phylogenetic trees - Using different regions from a same alignment may produce different trees.

39. Problems with phylogenetic trees

40. Bacillus E.coli Pseudomonas Salmonella Aeromonas Lechevaliera Burkholderias Problems with phylogenetic trees

41. What to do ?

42. 42 A.We create new data sets by sampling N positions with replacement. B.We generate 100 - 1000 such pseudo-data sets. C.For each such data set we reconstruct a tree, using the same method. D.We note the agreement between the tree reconstructed from the pseudo-data set to the original tree. Note: we do not change the number of sequences ! Bootstrapping

43. Bootstrapped tree Less reliable Branch Highly reliable branch

44. 44 Open Questions Do DNA and proteins from the same gene produce different trees ? Can different genes have different evolutionary history ?

45. 45