Recent Progress in Multiple Sequence Alignments: A Survey

1. Recent Progress in Multiple Sequence Alignments:A Survey Cédric Notredame

2. Our Scope What are The existing Methods? How Do They Work: -Assemby Algorithms -Weighting Schemes. When Do They Work ? Which Future?

3. Outline -Introduction -A taxonomy of the existing Packages -A few algorithms… -Performance Comparison using BaliBase

4. Introduction

5. LIKE ANYMODEL What Is A Multiple Sequence Alignment? A MSA is a MODEL It Indicates the RELATIONSHIP between residues of different sequences. It REVEALS -Similarities -Inconsistencies

6. How Can I Use A Multiple Sequence Alignment? chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD wheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE trybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP mouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP ***. ::: .: .. . : . . * . *: * chite AATAKQNYIRALQEYERNGG- wheat ANKLKGEYNKAIAAYNKGESA trybr AEKDKERYKREM--------- mouse AKDDRIRYDNEMKSWEEQMAE * : .* . : Extrapolation Motifs/Patterns Multiple Alignments Are CENTRAL to MOST Bioinformatics Techniques. Profiles Phylogeny Struc. Prediction

7. How Can I Use A Multiple Sequence Alignment? Multiple Alignments Is the most INTEGRATIVE Method Available Today. We Need MSA to INCORPORATE existing DATA

8. BIOLOGY:What is A Good Alignment COMPUTATIONWhat is THE Good Alignment chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD wheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE trybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP mouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP ***. ::: .: .. . : . . * . *: * Why Is It Difficult To Compute A multiple Sequence Alignment? A CROSSROAD PROBLEM

9. Why Is It Difficult To Compute A multiple Sequence Alignment ? BIOLOGY COMPUTATION CIRCULAR PROBLEM.... Good Good Alignment Sequences

10. A Taxonomy of Multiple Sequence Alignment Methods

11. Grouping According to the assembly Algorithm

12. Simultaneous As opposed to Progressive [Simultaneous: they simultaneously use all the information] Exact As opposed to Heursistic [Heuristics: cut corners like Blast Vs SW] [Heuristics: do not guarranty an optimal solution] StochasticAs opposed to Determinist [Stochastic: contain an element of randomness] [Stochastic: Example of a Monte Carlo Surface estimation ] Iterative As opposed to Non Iterative [Iterative: run the same algorithm many times] [Iterative: Most stochastic methods are iterative]

13. Simultaneous Progressive Clustal DCA T-Coffee Combalign MSA POA Dialign Iteralign GAs GA SAGA Prrp SAM HMMer Non tree based HMMs Iterative OMA PralineMAFFT

14. Simultaneous Progressive Clustal DCA T-Coffee Combalign MSA POA Dialign OMA PralineMAFFT Iteralign GA Prrp SAM HMMer Iterative SAGA Stochastic

15. NEARLY EVERY OPTIMISATIONALGORITHMHAS BEEN APPLIED TO THEMSA PROBLEM!!!

16. Grouping According to the Objective Function

17. Scoring an Alignment: Evolutionary based methods BIOLOGY How many events separate my sequences? Such an evaluation relies on a biological model. COMPUTATION Every position musd be independant

18. A A C AA A C C REAL Tree Model: ALL the sequences evolved from the same ancestor A C Tree: Cost=1 A A C PROBLEM: We do not know the true tree

19. AA A C C STAR Tree Model: ALL the sequences have the same ancestor A C Star Tree: Cost=2 A A C A PROBLEM: the tree star is phylogenetically wrong

20. [s(a,b): matrix] AA A C C A C [i: column i] Sums of Pairs: Cost=6 [k, l: seq index] A C A Sums of Pairs Model=Every sequence is the ancestor of every sequence PROBLEM: -over-estimation of the mutation costs -Requires a weighting scheme

21. Cost= 5*N*(N-1)/2 [5: Leucine Vs Leucine with Blosum50] LL L L L Cost=5*N*(N-1)/2-(5)*(N-1) - (-4)*(N-1) G [glycine effect] Cost=5*N*(N-1)/2-(9)*(N-1) Sums of Pairs: Some of itslimitations (Durbin, p140)

22. 2*(9)*(N-1) (9) Delta= = 5*N*(N-1) 5*N Delta G LL L L L N Sums of Pairs: Some of its limitations (Durbin, p140) Conclusion: The more Leucine, the less expensive it gets to add a Glycin to the column...

23. AA A C C Enthropy based Functions Model: Minimize the enthropy (variety) in each Column [number of Alanine (a) in column i] [Score of column i] [a: alphabet] [P can incorporate pseudocounts] S=0 if the column is conserved PROBLEM: -requires a simultaneous alignment -assumes independant sequences

24. AA A C C Consistency based Functions Model: Maximise the consistency (agreement) with a list of constraints (alignments) [kand l are sequences, i is a column] [the two residues are found aligned in the list of constraints] PROBLEM: -requires a list of constraints

25. Weighted Sums of Pairs Concistency Based Combalign MSA T-Coffee ClustalPOA Praline DCA Dialign Prrp Iteralign SAGA MAFFTOMA GIBBS SAM HMMer Enthropy

26. A few Multiple Sequence Alignment Algorithms

27. A Few Algorithms MSA and DCA POA ClustalW MAFFT Dialign II Prrp SAGA GIBBS Sampler

28. Simultaneous: MSA and DCA

29. Simultaneous Alignments : MSA 1) Set Bounds on each pair of sequences (Carillo and Lipman) 2) Compute the Maln within the Hyperspace -Few Small Closely Related Sequence. -Memory and CPU hungry -Do Well When They Can Run.

30. chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD wheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE trybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP mouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD trybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP MSA: the carillo and Lipman bounds ( ) S = ) ( S chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD wheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE + ) ( S … [Pairwise projection of sequences k and l]

31. Upper ? Lower a(k,m) â(k,m) a(k,l) â(k,l) MSA: the carillo and Lipman bounds a(k,l)=score of the projection k l in the optimal MSA S(a(x,y))=score of the complete multiple alignment â(k,l)=score of the optimal alignment of k l

32. â(k,l) ? LM+ â(k,l)-S(â(x,y)) a(k,l) â(k,l) MSA: the carillo and Lipman bounds LM: a lower bound for the complete MSA LM<=S(â(x,y)) - (â(k,l)-a(k,l)) a(k,l)>=LM +â(k,l)-S(â(x,y))

33. MSA: the carillo and Lipman bounds â(k,l) LM+ â(k,l)-S(â(x,y)) a(k,l) ä(k,l) â(k,l) LM: can be measured on ANY heuristic alignment LM = S(ä(x,y)) The better LM, the tighter the bounds…

34. MSA: the carillo and Lipman bounds N N 0 0 Best( M-i, N-j) Best( 0-i, 0-j) + M M Forward backward

35. Simultaneous Alignments : MSA 1) Set Bounds on each pair of sequences (Carillo and Lipman) 2) Compute the Maln within the Hyperspace -Few Small Closely Related Sequence. -Memory and CPU hungry -Do Well When They Can Run.

36. -Few Small Closely Related Sequence, but less limited than MSA -Memory and CPU hungry, but less than MSA -Do Well When Can Run. Simultaneous Alignments : DCA

37. Simultaneous With a New Sequence Representaion: POA-Partial Ordered Graph

40. POA POA makes it possible to represent complex relationships: -domain deletion -domain inversions

41. Progressive: ClustalW

42. Progressive Alignment: ClustalW Feng and Dolittle, 1988; Taylor 198ç Clustering

43. Dynamic Programming Using A Substitution Matrix Progressive Alignment: ClustalW

44. Tree based Alignment : Recursive Algorithm Align ( Node N) { if ( N->left_child is a Node) A1=Align ( N->left_child) else if ( N->left_child is a Sequence) A1=N->left_child if (N->right_child is a node) A2=Align (N->right_child) else if ( N->right_child is a Sequence) A2=N->right_child Return dp_alignment (A1, A2) } C A D F G E B

45. Progressive Alignment : ClustalW -Depends on the CHOICE of the sequences. -Depends on the ORDER of the sequences (Tree). • -Depends on the PARAMETERS: • Substitution Matrix. • Penalties (Gop, Gep). • Sequence Weight. • Tree making Algorithm.

46. Progressive Alignment : ClustalW Weighting Weighting Within ClustalW

47. Progressive Alignment : ClustalW GOP Position Specific GOP

48. Progressive Alignment : ClustalW 2 2 3 -Scales Well: N, N L -Greedy Heuristic (No Guarranty). -Fast ClustalW is the most Popular Method

49. Progressive Alignment With a Heuristic DP: MAFFT