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Multiple Sequence Alignments. Lecture 12, Tuesday May 13, 2003 Reading Durbin’s book: Chapter 6.1-6.4 Gusfield’s book: Chapter 14.1, 14.2, 14.5, 14.6.1.

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Presentation on theme: "Multiple Sequence Alignments. Lecture 12, Tuesday May 13, 2003 Reading Durbin’s book: Chapter 6.1-6.4 Gusfield’s book: Chapter 14.1, 14.2, 14.5, 14.6.1."— Presentation transcript:

1 Multiple Sequence Alignments

2 Lecture 12, Tuesday May 13, 2003 Reading Durbin’s book: Chapter 6.1-6.4 Gusfield’s book: Chapter 14.1, 14.2, 14.5, 14.6.1 Papers: avid, lagan Optional: All of Gusfield chapter 14, Papers: tcoffee, slagan, scl

3 Lecture 12, Tuesday May 13, 2003 Definition Given N sequences x 1, x 2,…, x N : –Insert gaps (-) in each sequence x i, such that All sequences have the same length L Score of the global map is maximum The sum-of-pairs score of an alignment is the sum of the scores of all induced pairwise alignments S(m) =  k<l s(m k, m l ) s(m k, m l ):score of induced alignment (k,l)

4 Lecture 12, Tuesday May 13, 2003 Consensus -AGGCTATCACCTGACCTCCAGGCCGA--TGCCC--- TAG-CTATCAC--GACCGC--GGTCGATTTGCCCGAC CAG-CTATCAC--GACCGC----TCGATTTGCTCGAC CAG-CTATCAC--GACCGC--GGTCGATTTGCCCGAC Find optimal consensus string m * to maximize S(m) =  i s(m *, m i ) s(m k, m l ):score of pairwise alignment (k,l)

5 Multiple Sequence Alignments Algorithms

6 Lecture 12, Tuesday May 13, 2003 Example: in 3D (three sequences): 7 neighbors/cell F(i,j,k) = max{ F(i-1,j-1,k-1)+S(x i, x j, x k ), F(i-1,j-1,k )+S(x i, x j, - ), F(i-1,j,k-1)+S(x i, -, x k ), F(i-1,j,k )+S(x i, -, - ), F(i,j-1,k-1)+S( -, x j, x k ), F(i,j-1,k )+S( -, x j, x k ), F(i,j,k-1)+S( -, -, x k ) } Multidimensional Dynamic Programming

7 Lecture 12, Tuesday May 13, 2003 Progressive Alignment Multiple Alignment is NP-complete Most used heuristic: Progressive Alignment Algorithm: 1.Align two of the sequences x i, x j 2.Fix that alignment 3.Align a third sequence x k to the alignment x i,x j 4.Repeat until all sequences are aligned Running Time: O( N L 2 )

8 Lecture 12, Tuesday May 13, 2003 Progressive Alignment When evolutionary tree is known: –Align closest first, in the order of the tree Example: Order of alignments: 1. (x,y) 2. (z,w) 3. (xy, zw) x w y z

9 Lecture 12, Tuesday May 13, 2003 Progressive Alignment: CLUSTALW CLUSTALW: most popular multiple protein alignment Algorithm: 1.Find all d ij : alignment dist (x i, x j ) 2.Construct a tree (Neighbor-joining hierarchical clustering) 3.Align nodes in order of decreasing similarity + a large number of heuristics

10 Lecture 12, Tuesday May 13, 2003 CLUSTALW & the CINEMA viewer

11 Lecture 12, Tuesday May 13, 2003 Iterative Refinement One problem of progressive alignment: Initial alignments are “frozen” even when new evidence comes Example: x:GAAGTT y:GAC-TT z:GAACTG w:GTACTG Frozen! Now clear correct y = GA-CTT

12 Lecture 12, Tuesday May 13, 2003 Iterative Refinement Algorithm (Barton-Stenberg): 1.Align most similar x i, x j 2.Align x k most similar to (x i x j ) 3.Repeat 2 until (x 1 …x N ) are aligned 4.For j = 1 to N, Remove x j, and realign to x 1 …x j-1 x j+1 …x N 5.Repeat 4 until convergence Note: Guaranteed to converge

13 Lecture 12, Tuesday May 13, 2003 2. Iterative Refinement (cont’d) For each sequence y 1.Remove y 2.Realign y (while rest fixed) x y z x,z fixed projection allow y to vary

14 Lecture 12, Tuesday May 13, 2003 Iterative Refinement Example: align (x,y), (z,w), (xy, zw): x:GAAGTTA y:GAC-TTA z:GAACTGA w:GTACTGA After realigning y: x:GAAGTTA y:G-ACTTA + 3 matches z:GAACTGA w:GTACTGA

15 Lecture 12, Tuesday May 13, 2003 Iterative Refinement Example not handled well: x:GAAGTTA y 1 :GAC-TTA y 2 :GAC-TTA y 3 :GAC-TTA z:GAACTGA w:GTACTGA Realigning any single y i changes nothing

16 Lecture 12, Tuesday May 13, 2003 Restricted MDP Here is a final way to improve a multiple alignment: 1.Construct progressive multiple alignment m 2.Run MDP, restricted to radius R from m Running Time: O(2 N R N-1 L)

17 Lecture 12, Tuesday May 13, 2003 1. Restricted MDP Run MDP, restricted to radius R from m x y z Running Time: O(2 N R N-1 L)

18 Lecture 12, Tuesday May 13, 2003 Restricted MDP (2) x:GAAGTTA y 1 :GAC-TTA y 2 :GAC-TTA y 3 :GAC-TTA z:GAACTGA w:GTACTGA Within radius 1 of the optimal  Restricted MDP will fix it.

19 MLAGAN: Multiple Alignment 1.Multi-Anchoring 2.Progressive Alignment 3.Iterative Refinement

20 Lecture 12, Tuesday May 13, 2003 1.Multi-anchoring X Z Y Z X/Y Z To anchor the (X/Y), and (Z) alignments:

21 Lecture 12, Tuesday May 13, 2003 2.Progressive Alignment Given N sequences, phylogenetic tree Align pairwise, in order of the tree (LAGAN) Human Baboon Mouse Rat

22 Lecture 12, Tuesday May 13, 2003 3. Iterative Refinement For each sequence y 1.Remove y 2.Anchor “good” spots 3.Realign y using LAGAN x y z x,z fixed projection

23 Lecture 12, Tuesday May 13, 2003 Cystic Fibrosis (CFTR), 12 species The “zoo” project Human sequence length: 1.8 Mb Total genomic sequence: 13 Mb Human Baboon Cat Dog Cow Pig Mouse Rat Chimp Chicken Fugu Zebrafish

24 Lecture 12, Tuesday May 13, 2003 Performance in the CFTR region Exons Perfect Exons > 90% TIME (sec) MAX MEMORY (Mb) MUMmer Mammals 25%40%4540 Chicken & Fishes 0% 740 AVID Mammals 95%98%1563600 Chicken & Fishes 23%27%212387 LAGAN Mammals 98%99.7%55090 Chicken & Fishes 80%84%86290 MLAGAN Mammals 98%99.8% 4547670 Chicken & Fishes 82%91%

25 Alignment & Rearrangements

26 Lecture 12, Tuesday May 13, 2003 Evolution at the DNA level …ACGGTGCAGTTACCA… …AC----CAGTCCACCA… Mutation SEQUENCE EDITS REARRANGEMENTS Deletion Inversion Translocation Duplication

27 Lecture 12, Tuesday May 13, 2003 Local & Global Alignment AGTGCCCTGGAACCCTGACGGTGGGTCACAAAACTTCTGGA AGTGACCTGGGAAGACCCTGAACCCTGGGTCACAAAACTC AGTGCCCTGGAACCCTGACGGTGGGTCACAAAACTTCTGGA AGTGACCTGGGAAGACCCTGAACCCTGGGTCACAAAACTC Local Global

28 Lecture 12, Tuesday May 13, 2003 Glocal Alignment Problem Find least cost transformation of one sequence into another using new operations Sequence edits Inversions Translocations Duplications Combinations of above AGTGCCCTGGAACCCTGACGGTGGGTCACAAAACTTCTGGA AGTGACCTGGGAAGACCCTGAACCCTGGGTCACAAAACTC

29 Lecture 12, Tuesday May 13, 2003 Shuffle-LAGAN A glocal aligner for long DNA sequences

30 Lecture 12, Tuesday May 13, 2003 S-LAGAN: Find Local Alignments 1.Find Local Alignments 2.Build Rough Homology Map 3.Globally Align Consistent Parts

31 Lecture 12, Tuesday May 13, 2003 S-LAGAN: Build Homology Map 1.Find Local Alignments 2.Build Rough Homology Map 3.Globally Align Consistent Parts

32 Lecture 12, Tuesday May 13, 2003 Building the Homology Map d a b c Chain (using Eppstein Galil); each alignment gets a score which is MAX over 4 possible chains. Penalties are affine (event and distance components) Penalties: a)regular b)translocation c)inversion d)inverted translocation

33 Lecture 12, Tuesday May 13, 2003 S-LAGAN: Build Homology Map 1.Find Local Alignments 2.Build Rough Homology Map 3.Globally Align Consistent Parts

34 Lecture 12, Tuesday May 13, 2003 S-LAGAN: Global Alignment 1.Find Local Alignments 2.Build Rough Homology Map 3.Globally Align Consistent Fragments

35 Lecture 12, Tuesday May 13, 2003 S-LAGAN alignments LocalLocal GlocalGlocal

36 Lecture 12, Tuesday May 13, 2003 S-LAGAN alignments Hum/MusHum/Mus Hum/RatHum/Rat

37 Lecture 12, Tuesday May 13, 2003 S-LAGAN alignments (Chr 20) Human Chr 20 v. homologous Mouse Chr 2. 270 Segments of conserved synteny 70 Inversions

38 Lecture 12, Tuesday May 13, 2003 Some more examples Hum/Mus Hum/Rat

39 Lecture 12, Tuesday May 13, 2003 Some more examples Hum/Mus Hum/Rat

40 Lecture 12, Tuesday May 13, 2003 Some more examples Hum/Mus Hum/Rat

41 Lecture 12, Tuesday May 13, 2003 Some more examples Hum/Mus Hum/Rat

42 Lecture 12, Tuesday May 13, 2003 Some more examples Hum/MusHum/Rat

43 Lecture 12, Tuesday May 13, 2003 Some more examples Hum/Mus Hum/Rat

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