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2. CIS786, Lecture 8 Usman Roshan Some of the slides are based upon material by Dennis Livesay and David La of California State University at Pomona

3. Previously…

4. Evaluation of multiple sequence alignments • Compare to benchmark “true” alignments • Use simulation • Measure conservation of an alignment • Measure accuracy of phylogenetic trees • How well does it align motifs?

5. ROSE • Evolve sequences under an i.i.d. Markov Model • Root sequence: probabilities given by a probability vector (for proteins default is Dayhoff et. al. values) • Substitutions • Edge length are integers • Probability matrix M is given as input (default is PAM1*) • For edge of length b probabilty of x y is given by Mbxy • Insertion and deletions: • Insertions and deletions follow the same probabilistic model • For each edge probability to insert is iins . • Length of insertion is given by discrete probability distribution (normally exponential) • For edge of length b this is repeated b times. • Model tree can be specified as input

6. Phylogeny accuracy

7. Running time

8. Evaluating alignments using motif detection • Let’s evaluate alignments by searching for motifs • If alignment X reveals more functional motifs than Y using technique Z then X is better than Y w.r.t. Z • Motifs could be functional sites in proteins or functional regions in non-coding DNA

9. What is a “Functional Site”? • Defining what constitutes a “functional site” is not trivial • Residues that include and cluster around known functionality are clear candidates for functional sites • We define a functional site as catalytic residues, binding sites, and regions that clustering around them.

10. Functional Sites (FS)

11. Phylogenetic motifs • PMs are short sequence fragments that conserve the overall familial phylogeny • Are they functional? • How do we detect them?

12. Map PMs to the Structure Map Set PSZ Threshold

13. PMs in Various Structures

14. PMs and Traditional Motifs

15. TIM Phylogenetic Similarity False Positive Expectation

16. Cytochrome P450 Phylogenetic Similarity False Positive Expectation

17. Enolase Phylogenetic Similarity False Positive Expectation

18. Glycerol Kinase Phylogenetic Similarity False Positive Expectation

19. Myoglobin Phylogenetic Similarity False Positive Expectation

20. Evaluating alignments • For a given alignment compute the PMs • Determine the number of functional PMs • Those identifying more functional PMs will be classified as better alignments

21. Running time

22. Functional PMs PAl=blue MUSCLE=red Both=green (a)=enolase, (b)ammonia channel, (c)=tri-isomerase, (d)=permease, (e)=cytochrome

23. Today • More simulations… • Comparison of MP and NJ trees on different protein alignments • Simultaneous alignment and phylogeny reconstruction • Starting trees for POY • Boosting it with RecIDCM3

24. NJ vs MP on 50 taxa and 500 mean sequence length MP NJ

25. NJ vs MP on 100 taxa and 500 mean sequence length MP NJ

26. NJ vs MP on 400 taxa and 500 mean sequence length MP NJ

27. MP trees on 800 taxa alignments

28. Increasing sequence lengths on 50 taxa datasets 1000 200 500

29. Increasing sequence lengths on 400 taxa datasets 1000 200 500

30. Simultaneous alignment and phylogeny reconstruction---POY • Performs TBR through tree space to search for better tree alignments • Uses variant of progressive alignment without profiles • Assigns ancestral sequences to internal nodes using MP • Removes gaps in ancestral sequences • Optional median alignment is possible

31. Starting trees for POY • Poy-default (greedy method) • Poy-approxbuild (faster greedy method) • Heuristic maximum parsimony trees generated on the following alignments using the TNT program (TBR search with one saved tree): • ClustalW(fast distance estimation) • Muscle1(default): progressive alignment (BLASTZ scoring matrix) • Muscle2(default): improved iterative progressive alignment (BLASTZ scoring matrix) • Muscle1MP: progressive alignment (scoring matrix for parsimony: match=1, mismatch=0, gapopen=gapextend=-1) • Muscle2MP: improved iterative progressive alignment (parsimony scoring matrix as above) • Muscle1MP(CW-guidetree): Muscle1-MP on the ClustalW guide-tree (fast distance estimation)

32. Simulation study parameters • Model trees: uniform random distribution and uniformly selected random edge lengths • Model of evolution: HKY95 with insertions and deletions probabilities selected from a gamma distribution (see ROSE software package) • Generated data: Settings of 250, 500, 1000 taxa, mean sequence lengths of 1000 and 2000, and avg branch lengths of 0.2 were selected. For each setting 1 dataset was produced. • Criterion for branch length and sequence length selection: Evolutionary rate was selected such that the starting Poy tree was between 20% and 30% error rate---not too hard or easy. Mean sequence lengths of 1000 and 2000 are realistic for protein coding sequences.

33. Comparison of Poy to MUSCLE and ClustalW under simulation 250 taxa, 941 mean sequence length, 0.2 avg branch length

34. Comparison of Poy to MUSCLE and ClustalW under simulation 500 taxa, 981 mean sequence length, 0.2 avg branch length

35. Comparison of Poy to MUSCLE and ClustalW under simulation 1000 taxa, 993 mean sequence length, 0.2 avg branch length

36. Comparison of Poy to MUSCLE and ClustalW on real data 218 taxa RNA metazoan dataset

37. Comparison of Poy to MUSCLE and ClustalW on real data 585 taxa RNA archaea dataset

38. Comparison of Poy to MUSCLE and ClustalW on real data 1040 taxa RNA mitochondria dataset

39. Comparison of Poy to MUSCLE and ClustalW on real data 1766 taxa RNA metazoa dataset