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Multiple Sequence Alignment

Algorithms for Comparative Sequence Analysis. Summer 2013. Multiple Sequence Alignment. Tamer Kahveci CISE Department University of Florida. What is Multiple Sequence Alignment?. Alignment of more than two sequences Global: multiple alignment

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Multiple Sequence Alignment

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  1. Algorithms for Comparative Sequence Analysis Summer 2013 Multiple Sequence Alignment Tamer Kahveci CISE Department University of Florida

  2. What is Multiple Sequence Alignment? • Alignment of more than two sequences • Global: multiple alignment • 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....

  3. What is Local Multiple Alignment? • Local: motif • Blocks database: (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|P07305 ( 10) AKPKRAKASKKSTDHPKYSDMI 63 H5A_XENLA|P22844 ( 11) AKPKRSKALKKSTDHPKYSDMI 71 H10_RAT|P43278 ( 10) AKPKRAKAAKKSTDHPKYSDMI 70 H10_MOUSE|P10922 ( 10) AKPKRAKASKKSTDHPKYSDMI 63 Q91759 ( 9) AKPRRSKASKKSTDHPKYSDMI 71 H5B_XENLA|P22845 ( 9) AKPRRSKASKKSTDHPKYSDMI 71 H5_CHICK|P02259 ( 11) AKPKRVKASRRSASHPTYSEMI 100 H5_CAIMO|P06513 ( 12) AKPKRAKAPRKPASHPSYSEMI 91 H5_ANSAN|P02258 ( 12) AKPKRARAPRKPASHPTYSEMI 100

  4. 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

  5. How to Evaluate Multiple Alignments

  6. 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 A cwcyklpdwv pikqkvsgk cn C cycyglpesv kiwtsetnk c. A cwcyklpdwv pikqkvsgk. cn.. D cyceglsdst ptwplp.nkt csgk A cwcyklpdwv pikqkvsgk. cn.... B cycyglpdse ptktn..gk. cksgkk C cycyglpesv kiwtsetnk. c..... 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

  7. Finding Multiple Sequence Alignments

  8. Dynamic Programming

  9. Dynamic Programming • Similar to pairwise alignment • Compare NV and NS 22-1 = 3 cases N + V N S N + V N - N + - N S NV NS S = max V • If k sequences are aligned • => k-dimensional matrix is filled

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

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

  12. MSA (Carrillo, Lipman’ 88)

  13. MSA – Idea 2 3 1

  14. 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 x1, .., xr. • Alignment A, cost = c(A) • Optimal alignment A* • Aij = induced alignment on xi, .., xj on account of A • D(xi,xj) = cost of optimal pairwise alignment of xi,xj <= c(Aij )

  15. MSA algorithm (2/3) • d >= c(A*) = c(A*uv) + Σ c(A*ij) >= c(A*uv) + Σ D(xi,xj) • c(A*uv) <= d - Σ D(xi,xj) = 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) • beststuv = best pairwise alignment of xu,xv that passes through (s,t). • beststuv = distance of the prefixes up to (s,t) + cost(xsi,xsj) + distance of suffixes after (s,t) i < j (i,j) ≠ (u,v) i < j (i,j) ≠ (u,v) i < j (i,j) ≠ (u,v)

  16. MSA algorithm (3/3) • If beststuv > B(u,v), then • A* cannot pass through cell f • Discard such cells from computation of DP

  17. Question Align: s1: MPE s2: MKE s3: MSKE s4: SKE BLOSUM 62

  18. Progressive Alignment

  19. Star Alignment

  20. Star Alignments • Heuristic method for multiple sequence alignments • Select a sequence c as the center of the star • For each sequence x1, …, xk such that xi c, perform a Needleman-Wunsch global alignment for xi and c

  21. MPE | | MKE MSKE | || M-KE SKE || MKE M-PE M-KE MSKE S-KE M-PE M-KE MSKE Star Alignments Example s1: MPE s2: MKE s3: MSKE s4: SKE s3 s1 s2 MPE MKE s4 • 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?

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

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

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

  25. CLUSTAL-W (3/4) • Align sequences starting from leaf level • Edge weights are used to compute the score of the alignment • O(k2n2) time • O(n2) space • Result depends on sequence order A B C D E

  26. CLUSTAL-W (4/4) • Sample query using ClustalW • http://www.ebi.ac.uk/clustalw/

  27. Other Progressive Methods • T-COFFEE • PILUP • Muscle • …

  28. 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 • Use these scoring matrices during progressive alignment s2 Scoring matrix for s1 and s2

  29. T-Coffee (1/2) • Given sequences A, B, C, D, E • Create primary library

  30. T-Coffee (2/2) • Create extended library • Create similarity matrix SeqA

  31. Iterative Alignment

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

  33. PRRP 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 2. Construct phylogenetic tree based on multiple alignment A B C D E Go back if the result has improved 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

  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.pdf

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