1. Multiple Sequence Alignments and Phylogeny

2. vtrevino@itesm.mx  Within a protein sequence, some regions will be more conserved than others. As more conserved, more important.  for function  for 3D structure  for localization  for modification  for interaction  for regulation/control  for transcriptional regulation (in DNA) REASONS TO PERFORM SEQUENCE SIMILARITY ANALYSIS AND SEARCHES

3. vtrevino@itesm.mx  Procedure for comparing two (pair-wise alignment) or more (multiple sequence alignment) sequences by searching for similar patterns that are in the same order in the sequences  Identical residues (nt or aa) are placed in the same column  Non-identical residues can be placed in the same column or indicated as gaps Wikipedia, http://www-personal.umich.edu/~lpt/fgf/fgfrcomp.htm Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press Overall similitude

4. vtrevino@itesm.mx  Interesting regions  Promoter regions  Consensus sequence for probe design

5. vtrevino@itesm.mx Multiple Sequence Alignment - MSA Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

6. vtrevino@itesm.mx Dynamical programming is designed for two sequences  It would take quite a long time for three or more (see MSA program) Sequence A Sequence B Sequence C

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8.  Extenstions of sequence pair alignment  MSA  Progressive Methods  CLUSTALW  Iterative Methods  Hidden Markov Models (HMM)

9. vtrevino@itesm.mx Algorithm 1. Calculate all pair-wise alignment scores (alignment costs). 2. Use the scores (costs) to predict a tree. 3. Calculate pair weights based on the tree. 4. Produce a heuristic msa based on the tree. 5. Calculate the maximum for each sequence pair. 6. Determine the spatial positions that must be calculated to obtain the optimal alignment. 7. Perform the optimal alignment. 8. Report the epsilon found compared to the maximum epsilon. epsilon for a given sequence pair is the difference between the score of the alignment of that pair in the msa and the score of the optimal pair-wise alignment. The bigger the value of, the more divergent the msa from the pair-wise alignment and the smaller the contribution of tht alignment to the msa. For example, if an extra copy of one of the sequences is added to the alignment project, then for sequence pairs that do not include that sequence will increase, indicating a lesser role because the contributions of that pair have been out-voted by the alike sequences.

10. vtrevino@itesm.mx Dynamical programming is designed for two sequences  It would take quite a long time for three or more (see MSA program) Therefore… 1. Pair-wise all sequences 2. Determine "distances between each one" 3. Align the two most similar then get the alignment 4. Get the next more similar and perform the same steps until all sequences has been included 5. E.G. 1. (S3+S4)=c1, 2. (S1+S2)=c2 3. (c1+c2)=c3 4. (c3+S5)=final S1 S2 S3 S4 S5

11. vtrevino@itesm.mx Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press (then normalized to largest = 1) Alignment Score for column CLUSTALW METHOD

12. vtrevino@itesm.mx Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press 1 2 3

13. vtrevino@itesm.mx  Dependency on the most similar sequences  Nested problems when most similar sequences are actually different  So, for closely related sequence, CLUSTALW is the best  Choice of suitable scoring matrices Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

14. vtrevino@itesm.mx  Try to correct for the dependency on the most similar sequences in progressive methods  Repeatedly realigning subgroups, then aligning these on the global alignment  Based in tree ordering, separation of sequences, or random grupo selection Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

15. vtrevino@itesm.mx Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

16. vtrevino@itesm.mx Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press D1

17. vtrevino@itesm.mx Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

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19.  Determination of how the family might have been derived during evolution  Sequences is depicted as branches on a tree  Very similar sequences are located as neighbours in a branch  The goal is to discover all the branching relationships and the branch lengths Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

20. vtrevino@itesm.mx  Phylogenetic relationships among the genes can help to predict which ones might have an equivalent function.  Phylogenetic analysis may also be used to follow the changes occurring in a rapidly changing species, such as a virus  Important for discovering  function, 3D structure, localization, modification, interaction, regulation/control, transcriptional regulation Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

21. vtrevino@itesm.mx  Related to SEQUENCE ALIGNMENT Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

22. vtrevino@itesm.mx Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

23. vtrevino@itesm.mx Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

24. vtrevino@itesm.mx Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

25. vtrevino@itesm.mx  An evolutionary tree is a two-dimensional graph showing evolutionary relationships among organisms  The separate sequences are referred to as taxa (singular taxon), defined as phylogenetically distinct units on the tree  The tree is composed of outer branches (or leaves) representing the taxa and nodes and branches representing relationships among the taxa Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

26. vtrevino@itesm.mx  A and B are derived from a common ancestor  each node in the tree represents a splitting of the evolutionary path of the gene into two different species that are isolated reproductively Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

27. vtrevino@itesm.mx  Beyond spliting, any further evolutionary changes in each new branch are independent of those in the other new branch  The length of each branch to the next node represents the number of sequence changes that occurred prior to the next level of separation Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

28. vtrevino@itesm.mx  Uniform mutation rate  Molecular Clock Hypothesis, suitable for closely related species  Special cases could use non-uniform rates  The root is defined by including a taxon that we are reasonably sure branched off earlier than the other Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

29. vtrevino@itesm.mx  The sum of all the branch lengths in a tree is referred to as the tree length.  The tree is also a bifurcating or binary tree, in that only two branches emanate from each node.  Trees can have more than one branch emanating from a node if the events separating taxa are so close that they cannot be resolved, or to simplify the tree.  The unrooted tree also shows the evolutionary relationships among sequences A–D, but it does not reveal the location of the oldest ancestry. Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

30. vtrevino@itesm.mx  The number of possible rooted trees increases very rapidly with the number of sequences or taxa Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

31. vtrevino@itesm.mx To find the evolutionary tree or trees that best account for the observed variation in a group of sequences  Maximum Parsimony  Distance  Maximum Likelihood

32. vtrevino@itesm.mx Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

33. vtrevino@itesm.mx  Not Large number of gaps  Phylogenetic methods analyze conserved regions that are represented in all the sequences (Local Alignments)

34. vtrevino@itesm.mx  Predicts the evolutionary tree by minimizing the number of steps required to generate the observed sequence changes  Requires a multiple sequence alignment  Method revise each informative position and each possible tree  same residue in at least two sequences but not all  Used for sequences that are quite similar and for small number of sequences

35. vtrevino@itesm.mx Non informative Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

36. vtrevino@itesm.mx  Employs the number of changes between each pair  Sequence pairs that have the smallest number of sequence changes are "neighbours" sharing a node in the tree  Very related to Multiple sequence alignment method (CLUSTALW) which produced DISTANCE MATRICES then analysed by distance methods  Remember Distance vs Similarity (and gaps) Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

37. vtrevino@itesm.mx Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press "Idealized"

38. vtrevino@itesm.mx  Fitch and Margoliash Method  Neighbor-joining Method  Unweighted Pair Group Method with Arithmetic Mean (UPGMA)

39. vtrevino@itesm.mx  Choosing a outgroup (Grupo Fuera) improves prediction because methods are informed about the "order" of the outgroup

40. vtrevino@itesm.mx  Uses probability of the number of sequence changes  Analysis is performed for each informative residue (like in maximum parsimony)  All possible trees are considered (so, for small number of sequences)  Consider variations in mutation rates, so it can be used for most distant sequences  Main disadvantage: Computation Time

41. vtrevino@itesm.mx  Needs a model that provides estimates of substitution rates for each residue pair

42. vtrevino@itesm.mx  Bootstrap method randomly resampling residues within columns (robustness test)  Good evidence if more than 70% predictions are conserved then  Collapse branches and confirm tree length  Compare distinct methods and parameters

43. vtrevino@itesm.mx  PHYLIP http://evolution.genetics.washington.edu/phylip.html  PAUP http://paup.csit.fsu.edu/downl.html  Phylemon http://phylemon.bioinfo.cipf.es/cgi-bin/tools.cgi

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45. http://bioinformatics.ca/links_directory/index.php?search=phylogeny&submit=Search+Directory

46. vtrevino@itesm.mx http://bioinformatics.ca/links_directory/index.php?search=phylogeny&submit=Search+Directory

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48.  Select a gene  Get the sequence in at least 7 species  Select a site (Phylemon)  Perform the multiple sequence alignment (ClustalW)  Perform Phylogeny to obtain a tree  At least 2 tree methods  At least 3 parameter(s) changes  Take DNA/Protein  Report results and discussion 12 MSA+Trees

49. vtrevino@itesm.mx  Phylogeny-aware gap placement prevents errors in sequence alignment and evolutionary analysis – Loytynoja, Goldman, Science 2008  Insertions and deletions treated as different events

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