1. Bioinformatics I Fall 2003 copyright Susan Smith 1 Phylogenetic Analysis

2. Bioinformatics I Fall 2003 copyright Susan Smith 2 General comments on phylogenetics Phylogenetics is the branch of biology that deals with evolutionary relatedness Uses some measure of evolutionary relatedness: e.g., morphological features Phylogenetics is the branch of biology that deals with evolutionary relatedness Uses some measure of evolutionary relatedness: e.g., morphological features

3. Bioinformatics I Fall 2003 copyright Susan Smith 3 Phylogenetics on sequence data is an attempt to reconstruct the evolutionary history of those sequences Relationships between individual sequences are not necessarily the same as those between the organisms they are found in Phylogenetics on sequence data is an attempt to reconstruct the evolutionary history of those sequences Relationships between individual sequences are not necessarily the same as those between the organisms they are found in

4. Bioinformatics I Fall 2003 copyright Susan Smith 4 The ultimate goal is to be able to use sequence data from many sequences (and whole genomes) to give information about phylogenetic history of organisms Phylogenetic relationships usually depicted as trees, with branches representing ancestors of “children”; the bottom of the tree (individual organisms) are leaves. Individual branch points are nodes. The ultimate goal is to be able to use sequence data from many sequences (and whole genomes) to give information about phylogenetic history of organisms Phylogenetic relationships usually depicted as trees, with branches representing ancestors of “children”; the bottom of the tree (individual organisms) are leaves. Individual branch points are nodes.

5. Bioinformatics I Fall 2003 copyright Susan Smith 5 Phylogenetic trees ABCD time A rooted tree A B C D An unrooted tree time?

6. Bioinformatics I Fall 2003 copyright Susan Smith 6 We will only consider binary trees: edges split only into two branches (daughter edges) rooted trees have an explicit ancestor; the direction of time is explicit in these trees unrooted trees do not have an explicit ancestor; the direction of time is undetermined in such trees Number of possible rooted or unrooted trees grows extremely fast We will only consider binary trees: edges split only into two branches (daughter edges) rooted trees have an explicit ancestor; the direction of time is explicit in these trees unrooted trees do not have an explicit ancestor; the direction of time is undetermined in such trees Number of possible rooted or unrooted trees grows extremely fast

7. Bioinformatics I Fall 2003 copyright Susan Smith 7 Terminal nodes correspond to data Internal nodes usually represent an inferred common ancestor Roots can be assigned to unrooted trees by using an outgroup Terminal nodes correspond to data Internal nodes usually represent an inferred common ancestor Roots can be assigned to unrooted trees by using an outgroup

8. Bioinformatics I Fall 2003 copyright Susan Smith 8 Exercise Draw all possible rooted and unrooted trees involving 3 species. Draw all possible unrooted and rooted trees involving 4 species Draw all possible rooted and unrooted trees involving 3 species. Draw all possible unrooted and rooted trees involving 4 species

9. Bioinformatics I Fall 2003 copyright Susan Smith 9 Types of phylogenetic analysis methods Phenetic: trees are constructed based on observed characteristics, not on evolutionary history Cladistic: trees are constructed based on fitting observed characteristics to some model of evolutionary history Phenetic: trees are constructed based on observed characteristics, not on evolutionary history Cladistic: trees are constructed based on fitting observed characteristics to some model of evolutionary history Distance methods Parsimony and Maximum Likelihood methods

10. Bioinformatics I Fall 2003 copyright Susan Smith 10 Sequence considerations Mutations are not all equal deleterious -- majority advantageous – small minority neutral – large minority? Mutation rates are not all equal r = K/(2T) where r = rate, K = number of substitutions per site, and T = time Mutations are not all equal deleterious -- majority advantageous – small minority neutral – large minority? Mutation rates are not all equal r = K/(2T) where r = rate, K = number of substitutions per site, and T = time

11. Bioinformatics I Fall 2003 copyright Susan Smith 11 Synonymous vs. nonsynonymous substitutions Synonymous – many substitutions at the 3 rd position of codons are synonymous = don’t lead to amino acid change Nonsynonymous – substitutions that do lead to amino acid changes Where would you predict substitution rates to be highest? Lowest? Synonymous – many substitutions at the 3 rd position of codons are synonymous = don’t lead to amino acid change Nonsynonymous – substitutions that do lead to amino acid changes Where would you predict substitution rates to be highest? Lowest?

12. Bioinformatics I Fall 2003 copyright Susan Smith 12 Mutation vs. substitution Mutation is a change Substitution is a change that has been preserved through evolution (has passed through some selective pressures) Initial frequency q of a mutation in a population: q = 1/(2N) where N = number of diploid organisms in the population Most substitutions now seen must be approximately selectively neutral otherwise they would have disappeared Probability that a mutation will be lost by chance is 1 – q; probability that it will be fixed (take over) = q Mean time for new neutral mutation to be fixed is 4N generations Mutation is a change Substitution is a change that has been preserved through evolution (has passed through some selective pressures) Initial frequency q of a mutation in a population: q = 1/(2N) where N = number of diploid organisms in the population Most substitutions now seen must be approximately selectively neutral otherwise they would have disappeared Probability that a mutation will be lost by chance is 1 – q; probability that it will be fixed (take over) = q Mean time for new neutral mutation to be fixed is 4N generations

13. Bioinformatics I Fall 2003 copyright Susan Smith 13 Estimating substitutions Jukes-Cantor assumption: each nucleotide is just as likely to change into any other nucleotide rate of change to a different nucleotide =  overall substitution rate for any nucleotide is 3  P C(1) = 1 – 3  P C(t) = ¼ + ¾ e -4  t PC(t) is the probability that a site will contain C at time t; takes into account mutations and back mutations Pairwise comparisons of sequences: K = -3/4 ln[1-(4/3)p] K = true number of substitutions; p = fraction of nucleotides that are different between pairs Jukes-Cantor assumption: each nucleotide is just as likely to change into any other nucleotide rate of change to a different nucleotide =  overall substitution rate for any nucleotide is 3  P C(1) = 1 – 3  P C(t) = ¼ + ¾ e -4  t PC(t) is the probability that a site will contain C at time t; takes into account mutations and back mutations Pairwise comparisons of sequences: K = -3/4 ln[1-(4/3)p] K = true number of substitutions; p = fraction of nucleotides that are different between pairs

14. Bioinformatics I Fall 2003 copyright Susan Smith 14 G TC A      C C C C T C T=0 T=1 T=2

15. Bioinformatics I Fall 2003 copyright Susan Smith 15 Transitions and Transversions Purine to purine (G, A) or pyrimidine to pyrimidine (T,C) = transitions Purine to pyrimidine or pyrimidine to purine = transversions Kimura model takes different rates into account Can include more parameters; turn out to be not much different than taking just transitions/transversion differences into account Purine to purine (G, A) or pyrimidine to pyrimidine (T,C) = transitions Purine to pyrimidine or pyrimidine to purine = transversions Kimura model takes different rates into account Can include more parameters; turn out to be not much different than taking just transitions/transversion differences into account G TC A      P CC(t) = ¼ + (¼)e -4  t + ½ e -2(  )t K = ½ ln [1/(1-2P-Q)] + ¼ ln [1/(1-2Q)] (P = # transitions, Q = # transversions)

16. Bioinformatics I Fall 2003 copyright Susan Smith 16 Distance methods Measuring distance -- just like when we talked about multiple alignment, distance represents all the differences at the various positions; these differences can be treated as equal or weighted according to empirical knowledge of substitution rates

17. Bioinformatics I Fall 2003 copyright Susan Smith 17 Another way to say this is that there are a set of distances d ij between each pair of sequences i,j in the dataset. d ij can be the fraction f of sites u where residues x i and x j differ; or d ij can be such a fraction but weighted in some way (e.g. Jukes-Cantor distance)

18. Bioinformatics I Fall 2003 copyright Susan Smith 18 Clustering algorithms UPGMA = unweighted pair group method with arithmetic mean -- this is the distance clustering method that is used in pileup to make the guide tree (also the method for clustering other data …) Computes all the pairwise distances, joins the smallest two into a group, recomputes, etc. UPGMA = unweighted pair group method with arithmetic mean -- this is the distance clustering method that is used in pileup to make the guide tree (also the method for clustering other data …) Computes all the pairwise distances, joins the smallest two into a group, recomputes, etc.

19. Bioinformatics I Fall 2003 copyright Susan Smith 19 Branch lengths can be computed from the distance matrix, assuming rates of evolution equal The branch length is just half of the distance between the two nodes (each branch gets half the total distance) Branch lengths can be computed from the distance matrix, assuming rates of evolution equal The branch length is just half of the distance between the two nodes (each branch gets half the total distance)

20. Bioinformatics I Fall 2003 copyright Susan Smith 20 Exercise Using given data, generate a pairwise distance matrix, join the first group, recompute, and iterate until you have a set of matrices that will allow you to create a tree. Estimate the branch lengths of your tree. Using given data, generate a pairwise distance matrix, join the first group, recompute, and iterate until you have a set of matrices that will allow you to create a tree. Estimate the branch lengths of your tree.

21. Bioinformatics I Fall 2003 copyright Susan Smith 21 Problems with UPGMA UPGMA assumes a molecular clock mechanism of evolution This can be, but is not always, a reasonable assumption One indication that this is not a reasonable assumption is that branch lengths of UPGMA-generated trees do not add up to the predicted values UPGMA assumes a molecular clock mechanism of evolution This can be, but is not always, a reasonable assumption One indication that this is not a reasonable assumption is that branch lengths of UPGMA-generated trees do not add up to the predicted values

22. Bioinformatics I Fall 2003 copyright Susan Smith 22 Taking different evolutionary rates into account is possible using the transformed distance method, and the neighbor-joining method Transformed distance method: use an outgroup, which you say diverged from all the other groups before they diverged from each other d’ ij = (d ij – d iD – d jD )/2 + d Dav Taking different evolutionary rates into account is possible using the transformed distance method, and the neighbor-joining method Transformed distance method: use an outgroup, which you say diverged from all the other groups before they diverged from each other d’ ij = (d ij – d iD – d jD )/2 + d Dav

23. Bioinformatics I Fall 2003 copyright Susan Smith 23 Neighbor-joining: corrects for UPGMA’s assumption of the same rate of evolution for each branch by modifying the distance matrix to reflect different rates of change Covered very well in text Neighbor-joining: corrects for UPGMA’s assumption of the same rate of evolution for each branch by modifying the distance matrix to reflect different rates of change Covered very well in text

24. Bioinformatics I Fall 2003 copyright Susan Smith 24 PAUP tutorial We will now follow some of the Quick-start tutorial instructions for using PAUP on the command line Log in to the server as usual To see the tutorial: –Hartford: In a unix window, type acroread /local/paup/Docs/Quick_start_v1.pdf –Troy: open a unix window FROM YOUR SERVER DIRECTORY, then type acroread Quick_start_v1.pdf Still from the server, in a different window, type paup Follow instructions for “Portable”. The sample data set should just be in your directory so you shouldn’t have to change directories. You don’t really need to stop logging when the tutorial covers that. You also don’t need to actually do the “recall assumptions” part on p.9. Follow the directions until you get to page 11 When you get to that section, skip ahead to “Setting the optimality criterion to distance” on p.16 Follow the directions on p.16; when you get to p. 17, type UPGMA; (don’t type nj) Examine the tree We will now follow some of the Quick-start tutorial instructions for using PAUP on the command line Log in to the server as usual To see the tutorial: –Hartford: In a unix window, type acroread /local/paup/Docs/Quick_start_v1.pdf –Troy: open a unix window FROM YOUR SERVER DIRECTORY, then type acroread Quick_start_v1.pdf Still from the server, in a different window, type paup Follow instructions for “Portable”. The sample data set should just be in your directory so you shouldn’t have to change directories. You don’t really need to stop logging when the tutorial covers that. You also don’t need to actually do the “recall assumptions” part on p.9. Follow the directions until you get to page 11 When you get to that section, skip ahead to “Setting the optimality criterion to distance” on p.16 Follow the directions on p.16; when you get to p. 17, type UPGMA; (don’t type nj) Examine the tree

25. Bioinformatics I Fall 2003 copyright Susan Smith 25 Parsimony methods Parsimony methods are based on the idea that the most probable evolutionary pathway is the one that requires the smallest number of changes from some ancestral state For sequences, this implies treating each position separately and finding the minimal number of substitutions at each position Parsimony methods are based on the idea that the most probable evolutionary pathway is the one that requires the smallest number of changes from some ancestral state For sequences, this implies treating each position separately and finding the minimal number of substitutions at each position

26. Bioinformatics I Fall 2003 copyright Susan Smith 26 Informative vs. uninformative positions Informative sites allow some of the possible trees to distinguished from the others on the basis of how many mutations they must invoke Obviously uninformative sites: all the same character at that site Less-obviously uninformative site: one substitution only Less-obviously uninformative site: less than two different characters, each character appears less than twice Informative sites allow some of the possible trees to distinguished from the others on the basis of how many mutations they must invoke Obviously uninformative sites: all the same character at that site Less-obviously uninformative site: one substitution only Less-obviously uninformative site: less than two different characters, each character appears less than twice

27. Bioinformatics I Fall 2003 copyright Susan Smith 27 Example 1 2 3 4 5 6 1 G G G G G G 2 G G G A G T 3 G G A T A G 4 G A T C A T 1 2 3 4 5 6 1 G G G G G G 2 G G G A G T 3 G G A T A G 4 G A T C A T

28. Bioinformatics I Fall 2003 copyright Susan Smith 28 Parsimony uses only the informative sites The most parsimonious tree is the one that invokes the fewest substitutions at informative sites Scoring means counting the number of substitutions needed There can be several trees with different topology that are equivalently parsimonious … The most parsimonious tree is the one that invokes the fewest substitutions at informative sites Scoring means counting the number of substitutions needed There can be several trees with different topology that are equivalently parsimonious …

29. Bioinformatics I Fall 2003 copyright Susan Smith 29 Example of parsimonious tree building Tree on left requires only one change, tree on left requires two: left tree is most parsimonious

30. Bioinformatics I Fall 2003 copyright Susan Smith 30 Parsimony methods assign a cost to each tree available to the dataset, then screen trees available to the dataset and select the most parsimonious Screening all the trees available to even a smallish dataset would take too much time; branch and bound method Parsimony methods assign a cost to each tree available to the dataset, then screen trees available to the dataset and select the most parsimonious Screening all the trees available to even a smallish dataset would take too much time; branch and bound method

31. Bioinformatics I Fall 2003 copyright Susan Smith 31 Can weight the cost to correspond with things known about the data – e.g., transitions occur more frequently than transversions You can inspect the dataset to be used to get the values of the weights to be used … Can weight the cost to correspond with things known about the data – e.g., transitions occur more frequently than transversions You can inspect the dataset to be used to get the values of the weights to be used …

32. Bioinformatics I Fall 2003 copyright Susan Smith 32 Parsimony is fundamentally different from UPGMA It is not based on a distance, but on a presumed evolutionary path to an outcome One of the best ways to decide if a topology is well-supported is to compare trees generated by these two kinds of methods It is not based on a distance, but on a presumed evolutionary path to an outcome One of the best ways to decide if a topology is well-supported is to compare trees generated by these two kinds of methods

33. Bioinformatics I Fall 2003 copyright Susan Smith 33 In-class exercise II Return to the PAUP tutorial, and this time follow the directions for using parsimony as the optimality criterion starting on p. 16. To see your tree, you will have to type showtrees; Inspect your tree. Compare it to the distance generated tree. Return to the PAUP tutorial, and this time follow the directions for using parsimony as the optimality criterion starting on p. 16. To see your tree, you will have to type showtrees; Inspect your tree. Compare it to the distance generated tree.

34. Bioinformatics I Fall 2003 copyright Susan Smith 34 Maximum likelihood methods Maximum likelihood reconstructs a tree according to an explicit model of evolution. For the given model, no other method will work as well But, such models must be simple, because the method is computationally intensive Maximum likelihood reconstructs a tree according to an explicit model of evolution. For the given model, no other method will work as well But, such models must be simple, because the method is computationally intensive

35. Bioinformatics I Fall 2003 copyright Susan Smith 35 Actually, all the other methods discussed implicitly use a simple model of evolution similar to the typical model made explicit in maximum likelihood: All sites selectively neutral All mutate independently, forward and reverse rates equal, given by  Actually, all the other methods discussed implicitly use a simple model of evolution similar to the typical model made explicit in maximum likelihood: All sites selectively neutral All mutate independently, forward and reverse rates equal, given by 

36. Bioinformatics I Fall 2003 copyright Susan Smith 36 Maximum likelihood is maybe the “gold standard” for phylogenetic analysis; but because of its computational intensity it can only be used for select data and only after much initial fine tuning of many parameters of sequence alignments Often used to distinguish between several already generated trees Maximum likelihood is maybe the “gold standard” for phylogenetic analysis; but because of its computational intensity it can only be used for select data and only after much initial fine tuning of many parameters of sequence alignments Often used to distinguish between several already generated trees

37. Bioinformatics I Fall 2003 copyright Susan Smith 37 DO NOT USE MAXIMUM LIKELIHOOD TO BUILD TREES, TODAY OR FOR YOUR PROJECT. PLEASE! DO NOT USE MAXIMUM LIKELIHOOD TO BUILD TREES, TODAY OR FOR YOUR PROJECT. PLEASE!

38. Bioinformatics I Fall 2003 copyright Susan Smith 38 Assessing trees The bootstrap: randomly sample all positions (columns in an alignment) with replacement -- meaning some columns can be repeated -- but conserving the number of positions; build a large dataset of these randomized samples

39. Bioinformatics I Fall 2003 copyright Susan Smith 39 Bootstrap alignment process

40. Bioinformatics I Fall 2003 copyright Susan Smith 40 Then use your method (distance, parsimony, likelihood) to generate another tree Do this a thousand or so times Note that if the assumptions the method is based on hold, you should always get the same tree from the bootstrapped alignments as you did originally The frequency of some feature of your phylogeny in the bootstrapped set gives some measure of the confidence you can have for this feature Then use your method (distance, parsimony, likelihood) to generate another tree Do this a thousand or so times Note that if the assumptions the method is based on hold, you should always get the same tree from the bootstrapped alignments as you did originally The frequency of some feature of your phylogeny in the bootstrapped set gives some measure of the confidence you can have for this feature

41. Bioinformatics I Fall 2003 copyright Susan Smith 41 Protein sequences and phylogenetic analysis Despite the fact that DNA sequences have more characters (and contain more information), phylogenetic analysis is often done with protein sequences For your project, you can use either nucleic acids or proteins To do a phylogenetic analysis, you must start with a good MSA You will probably not want to use the whole length sequences, but instead use sections of the alignment that are 50 – 200 characters long For modular genes/proteins, it might be interesting to look at the trees made by aligned sections from the different modules … Despite the fact that DNA sequences have more characters (and contain more information), phylogenetic analysis is often done with protein sequences For your project, you can use either nucleic acids or proteins To do a phylogenetic analysis, you must start with a good MSA You will probably not want to use the whole length sequences, but instead use sections of the alignment that are 50 – 200 characters long For modular genes/proteins, it might be interesting to look at the trees made by aligned sections from the different modules …

42. Bioinformatics I Fall 2003 copyright Susan Smith 42 Running paup on protein MSA’s Find the file called phylo10.msf in my directory; copy it to your directory view this file in seqlab’s editor start the paup program type the command (all on one line) tonexus format=gcg fromfile=phylo10.msf tofile=phylo10.nex Find the file called phylo10.msf in my directory; copy it to your directory view this file in seqlab’s editor start the paup program type the command (all on one line) tonexus format=gcg fromfile=phylo10.msf tofile=phylo10.nex

43. Bioinformatics I Fall 2003 copyright Susan Smith 43 Execute the file Then run distance tree, bootstrapped must use ‘total’ or ‘mean’ as the distance measure for proteins dset distance=total dset distance=mean to see a bootstrapped tree for distance, type boot Execute the file Then run distance tree, bootstrapped must use ‘total’ or ‘mean’ as the distance measure for proteins dset distance=total dset distance=mean to see a bootstrapped tree for distance, type boot

44. Bioinformatics I Fall 2003 copyright Susan Smith 44  Then run a parsimony tree, bootstrapped  remember to change criterion, then you can use boot again  When the prompt asks you if you want to change the number of trees looked at, say no; then wait for the output just one or two groups per classroom should do this may take a few minutes  Then run a parsimony tree, bootstrapped  remember to change criterion, then you can use boot again  When the prompt asks you if you want to change the number of trees looked at, say no; then wait for the output just one or two groups per classroom should do this may take a few minutes

45. Bioinformatics I Fall 2003 copyright Susan Smith 45 Making a section of an alignment Find an area (50-100 characters long) of the MSA that has few gaps Select all the sequences’ names Select everything up to the first character of the section using Edit  select range; cut the selected area Repeat, only select everything after the last character of the selection and cut that selection Find an area (50-100 characters long) of the MSA that has few gaps Select all the sequences’ names Select everything up to the first character of the section using Edit  select range; cut the selected area Repeat, only select everything after the last character of the selection and cut that selection

46. Bioinformatics I Fall 2003 copyright Susan Smith 46 Save the file as something you can remember This file must be made into a nexus file before you can use it in PAUP (tonexus etc.) Save the file as something you can remember This file must be made into a nexus file before you can use it in PAUP (tonexus etc.)