1. 1 CAP5510 – Bioinformatics Multiple Alignment Tamer Kahveci CISE Department University of Florida

2. 2 Goals Understand –What is multiple alignment –Why align multiple sequences Learn –How multiple alignments are scored –Major multiple alignment methods Dynamic programming –Standard –MSA Progressive alignment –Star –CLUSTALW

3. 3 What is Multiple Alignment? Alignment of more than two sequences Global: multiple alignment –http://www-igbmc.u-strasbg.fr/BioInfo/BAliBASE/http://www-igbmc.u-strasbg.fr/BioInfo/BAliBASE/ scxa_buteu vrdgyiaddk dcayfcgr...naycdeeck...kgaesgk cwyagqygna scx1_titse.kdgypveyd ncayicwnyd.naycdklck..dkkadsgy cyw...vhil scx6_titse.regypadsk gckitcflta.agycntect..lkkgssgy caw.....pa scx1_cenno.kdgylvdak gckkncyklg kndycnrecr mkhrggsygy c.....ygfg six2_leiqu..dgyirkrd gcklsclfg..negcnkeck..syggsygy cwt...wgla scxa_buteu cwcyklpdwv pikqkvsgk. cn.... scx1_titse cycyglpdse ptktn..gk. cksgkk scx6_titse cycyglpesv kiwtsetnk. c..... scx1_cenno cyceglsdst ptwplp.nkt csgk.. six2_leiqu cwceglpd.e ktwksetn.t cg....

4. 4 What is Local Multiple Alignment? Local: motif Local: motif (http://blocks.fhcrc.org/blocks-bin/getblock.sh?PR00624 )http://blocks.fhcrc.org/blocks-bin/getblock.sh?PR00624 ID HISTONEH5; BLOCK AC PR00624A; distance from previous block=(9,12) DE Histone H5 signature BL adapted; width=22; seqs=9; 99.5%=986; strength=1407 H10_HUMAN|P07305H10_HUMAN|P07305 ( 10) AKPKRAKASKKSTDHPKYSDMI 63 H5A_XENLA|P22844H5A_XENLA|P22844 ( 11) AKPKRSKALKKSTDHPKYSDMI 71 H10_RAT|P43278H10_RAT|P43278 ( 10) AKPKRAKAAKKSTDHPKYSDMI 70 H10_MOUSE|P10922H10_MOUSE|P10922 ( 10) AKPKRAKASKKSTDHPKYSDMI 63 Q91759Q91759 ( 9) AKPRRSKASKKSTDHPKYSDMI 71 H5B_XENLA|P22845H5B_XENLA|P22845 ( 9) AKPRRSKASKKSTDHPKYSDMI 71 H5_CHICK|P02259H5_CHICK|P02259 ( 11) AKPKRVKASRRSASHPTYSEMI 100 H5_CAIMO|P06513H5_CAIMO|P06513 ( 12) AKPKRAKAPRKPASHPSYSEMI 91 H5_ANSAN|P02258H5_ANSAN|P02258 ( 12) AKPKRARAPRKPASHPTYSEMI 100

5. 5 Why Multiple Alignment Basis for phylogeny Helps find conserved regions in sets of proteins –Conserved regions Provide insight into substitution patterns Gives hints about functional sites

6. 6 How to Evaluate Multiple Alignments

7. 7 Sum of Pairs (SP) Sum of induced pairwise alignment score of all pairs Ignore space pairs aligned together A cwcyklpdwv pikqkvsgk. cn.... B cycyglpdse ptktn..gk. cksgkk C cycyglpesv kiwtsetnk. c..... D cyceglsdst ptwplp.nkt csgk.. A cwcyklpdwv pikqkvsgk cn.... B cycyglpdse ptktn..gk cksgkk A cwcyklpdwv pikqkvsgk cn C cycyglpesv kiwtsetnk c. A cwcyklpdwv pikqkvsgk. cn.. D cyceglsdst ptwplp.nkt csgk B cycyglpdse ptktn..gk cksgkk C cycyglpesv kiwtsetnk c..... B cycyglpdse ptktn.gk. cksgkk D cyceglsdst ptwplpnkt csgk.. C cycyglpesv kiwtsetnk. c... D cyceglsdst ptwplp.nkt csgk +

8. 8 BAliBASE Benchmark Compare to a set of hand-aligned sequences Check positions of letters –If the letters appear at the same position as the benchmark => good Score between 0 ( ) and 1 ( ) http://www-igbmc.u- strasbg.fr/BioInfo/BAliBASE/prog_scores.htmlhttp://www-igbmc.u- strasbg.fr/BioInfo/BAliBASE/prog_scores.html

9. 9 Finding Multiple Alignments

10. 10 Dynamic Programming

11. 11 Similar to pairwise alignment –Compare NV and NS Dynamic Programming If k sequences are aligned –=> k-dimensional matrix is filled V S NV NS = max N + V N S N + V N - N + - N S 2 2 -1 = 3 cases

12. 12 V S A k=3 2 k –1=7 cases Dynamic Programming

13. 13 Complexity Space complexity: O(n k ) for k sequences each n long. Computing at a cell: O(2 k ). cost of computing δ. Time complexity: O(2 k n k ). cost of computing δ. Finding the optimal solution is exponential in k Proven to be NP-complete for a number of cost functions

14. 14 MSA (Carrillo, Lipman’ 88)

15. 15 MSA – Idea 1 2 3

16. 16 MSA algorithm (1/3) Find pairwise alignment Trial multiple alignment produced by a tree, cost = d This provides a limit to the volume within which optimal alignments are found Specifics –Sequences x 1,.., x r. –Alignment A, cost = c(A) –Optimal alignment A* –A ij = induced alignment on x i,.., x j on account of A –D(x i,x j ) = cost of optimal pairwise alignment of x i,x j <= c(A ij )

17. 17 i < j (i,j) ≠ (u,v) i < j (i,j) ≠ (u,v) MSA algorithm (2/3) d >= c(A*) = c(A* uv ) + Σ c(A* ij ) >= c(A* uv ) + Σ D(x i,x j ) c(A* uv ) <= d - Σ D(x i,x j ) = B(u,v) Compute B(u,v) for each pair of u,v Consider any cell f with projection (s,t) on u,v plane. If A* passes through f then A* uv passes through (s,t) –best st uv = best pairwise alignment of x u,x v that passes through (s,t). –best st uv = distance of the prefixes up to (s,t) + cost(x s i,x s j ) + distance of suffixes after (s,t) i < j (i,j) ≠ (u,v)

18. 18 MSA algorithm (3/3) If best st uv > B(u,v), then –A* cannot pass through cell f –Discard such cells from computation of DP

19. 19 Question s 1 : MPE s 2 : MKE s 3 : MSKE s 4 : SKE Align : BLOSUM 62

20. 20 Progressive Alignment

21. 21 Star Alignment

22. 22 Star Alignments Heuristic method for multiple sequence alignments Select a sequence c as the center of the star For each sequence x 1, …, x k such that x i  c, perform a Needleman-Wunsch global alignment for x i and c

23. 23 Star Alignments Example s2s2 s1s1 s3s3 s4s4 s 1 : MPE s 2 : MKE s 3 : MSKE s 4 : SKE MPE | MKE MSKE | || M-KE SKE || MKE MPE MKE M-PE M-KE MSKE S-KE M-PE M-KE MSKE All induced pairwise alignments to the center sequence is the optimal one. How should we choose a center? (Exercise: try s4 as the center) Try all of them?

24. 24 CLUSTAL-W (Thompson, Higgins, Gibson 1994)

25. 25 CLUSTAL-W (1/4) Given sequences A, B, C, D, E Compare all pairs and construct a distance matrix ABCDE A B C D E

26. 26 CLUSTAL-W (2/4) Find phylogenetic tree for A, B, C, D, E using neighbor joining DB A C E DB A C E DBACE DB A C E

27. 27 CLUSTAL-W (3/4) Align sequences starting from leaf level –Edge weights are used to compute the score of the alignment DBACE O(k 2 n 2 ) time O(n 2 ) space Result depends on sequence order

28. 28 CLUSTAL-W (4/4) Sample query using ClustalW http://www.cise.ufl.edu/~tamer/teaching/fall2007/other/sampleMSAquery http://www.ebi.ac.uk/clustalw/

29. 29 Other Progressive Methods T-COFFEE PILUP Muscle …

30. 30 T-coffee (Notredame, Higgins, Heringa 2000) Find a library of alignments between pairs of sequences. Create a new scoring matrix for each pair of sequences using the library –Directly from alignment of s1 and s2 –Indirectly through alignment of s1, s3 and s3, s2. s1 s2 Scoring matrix for s1 and s2 Use these scoring matrices during progressive alignment

31. 31 Iterative Alignment

32. 32 PRRP (Gotoh 1996) Motivation: If the initial sequences are not good ones, progressive alignment fails. Idea: Iteratively update the alignment

33. 33 PRRP DBACE 2. Construct phylogenetic tree based on multiple alignment A cwcyklpdwv pikqkvsgk. cn.... B cycyglpdse ptktn..gk. cksgkk C cycyglpesv kiwtsetnk. c..... D cyceglsdst ptwplp.nkt csgk.. E cyceglpdst piwplp.nkt ctgk.. 3. Align sequences A cwcyklpdwv pikqkvsgk. cn.... B cycyglpdse ptktn..gk. cksgkk C cycyglpesv kiwtsetnk. c..... D cyceglsdst ptwplp.nkt csgk.. E cyceglpdst piwplp.nkt ctgk.. 1. Find some initial alignment Go back if the result has improved

34. 34 Other methods Genetic algorithm (machine learning) Partial order graphs (graph matching) HMMER (hidden markov model) For a comparison: –http://www.cise.ufl.edu/~tamer/papers/psb2006.pdfhttp://www.cise.ufl.edu/~tamer/papers/psb2006.pdf

35. 35 Motif Logos ID HISTONEH5; BLOCK AC PR00624A; distance from previous block=(9,12) DE Histone H5 signature BL adapted; width=22; seqs=9; 99.5%=986; strength=1407 H10_HUMAN|P07305H10_HUMAN|P07305 ( 10) AKPKRAKASKKSTDHPKYSDMI 63 H5A_XENLA|P22844H5A_XENLA|P22844 ( 11) AKPKRSKALKKSTDHPKYSDMI 71 H10_RAT|P43278H10_RAT|P43278 ( 10) AKPKRAKAAKKSTDHPKYSDMI 70 H10_MOUSE|P10922H10_MOUSE|P10922 ( 10) AKPKRAKASKKSTDHPKYSDMI 63 Q91759Q91759 ( 9) AKPRRSKASKKSTDHPKYSDMI 71 H5B_XENLA|P22845H5B_XENLA|P22845 ( 9) AKPRRSKASKKSTDHPKYSDMI 71 H5_CHICK|P02259H5_CHICK|P02259 ( 11) AKPKRVKASRRSASHPTYSEMI 100 H5_CAIMO|P06513H5_CAIMO|P06513 ( 12) AKPKRAKAPRKPASHPSYSEMI 91 H5_ANSAN|P02258H5_ANSAN|P02258 ( 12) AKPKRARAPRKPASHPTYSEMI 100