BMI/CS 776 Lecture #14: Multiple Alignment - MUSCLE. Colin Dewey

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1 BMI/CS 776 Lecture #14: Multiple Alignment - MUSCLE Colin Dewey

2 Importance of protein multiple alignment Phylogenetic tree estimation Prediction of protein secondary structure Critical residue identification AHLGHYGPEP SHVSHYGSDS SHVSHYGSDS TSVSHYGAEP PSASHYGVEH 2

3 Three cutting-edge multiple alignment methods MUSCLE (Edgar, 2004) progressive (profile alignment), fast tree building, refinement step ProbCons (Do et al., 2005) progressive alignment, PHMMs, maximum expected accuracy, consistency transformation AMAP (Schwartz & Pachter, 2007) sequence annealing, PHMMs, no tree, alignment metric accuracy 3

4 MUSCLE overview Edgar,

5 Stage 1- Draft progressive 1. Compute kmer distance between all pairs of input sequences 2. Construct initial tree with UPGMA and distances from Progressive profile alignment with tree from 2. 5

6 kmer distance Much faster than performing pairwise alignment to get distances Use compressed alphabet (elements represent classes of amino acids) d X,Y = τ min(n X(τ), n Y (τ)) min(l X, l Y ) k + 1 X, Y : sequences τ: kmer n X (τ): Number of occurrences of τ in X l X : Length of X 6

7 Compressed alphabet Table 1. Examples of compressed alphabets produced by different methods Alpha(N) SE-B(14) SE-B(10) SE-V(10) Li-A(10) Li-B(10) Solis-D(10) Solis-G(10) Murphy(10) SE-B(8) SE-B(6) Dayhoff(6) Classes A, C, D, EQ, FY, G, H, IV, KR, LM, N, P, ST, W AST, C, DN, EQ, FY, G, HW, ILMV, KR, P AST, C, DEN, FY, G, H, ILMV, KQR, P, W AC, DE, FWY, G, HN, IV, KQR, LM, P, ST AST, C, DEQ, FWY, G, HN, IV, KR, LM, P AM, C, DNS, EKQR, F, GP, HT, IV, LY, W AEFIKLMQRVW, C, D, G, H, N, P, S, T, Y A, C, DENQ, FWY, G, H, ILMV, KR, P, ST AST, C, DHN, EKQR, FWY, G, ILMV, P AST, CP, DEHKNQR, FWY, G, ILMV AGPST, C, DENQ, FWY, HKR, ILMV Alphabet names are de ned in the main text. E(A) = i A j A p ij log ( pij p i p j ) Edgar,

8 UPGMA vs. Neighborjoining UPGMA better for progressive alignment because forces alignment of most similar sequences first u v x y x y u v True tree, recovered by NJ UPGMA tree 8

9 Progressive profile alignment Profile: alignment of two alignments by matching up corresponding columns, scoring based on composition of columns Progressive: alignment at each node in tree from leaves to root X Y M Q T F L H T W L Q S W L T I F M T I W Profile alignment (figures from Edgar, 2004) M Q T - F L H T - W L Q S - W L - T I F M - T I W M Q T I F L H - I W L Q S - W L - S - F M Q T I F L H - I W L Q S W L - S F M Q T I F L H I W L Q S W L S F Progressive profile alignment 9

10 Profile alignment scoring How to score alignment of two profile positions? Common function (profile sum-of-pairs): PSP xy = i fi x f y j S ij = j i MUSCLE s log-expectation score: LE xy = (1 f x G)(1 f y G ) log i frequency of gaps in profile column x j f x i f y j log ( pij p i p j fi x f y j j ( pij p i p j ) ) 10

11 Stage 2 - Improved progressive 1. Using multiple alignment from Stage 1, extract all implied pairwise alignments 2. Compute Kimura distance between all pairs of sequences using pairwise alignments 3. Compute a new tree using Kimura distances 4. Compute new multiple alignment with new tree 11

12 Stage 3 - Refinement 1. Chose an edge in the tree 2. Divide sequences into two sets according to split in tree defined by edge 3. Extract multiple alignment (profile) for each set of sequences 4. Re-align the two profiles 5. Accept new alignment if SP score improves 6. Repeat 12

13 Performance Table 1. BAliBASE scores and times Method Q TC CPU MUSCLE MUSCLE-p T-Coffee NWNSI CLUSTALW FFTNS Table 6. Q scores and CPU times on SABmark Method All Superfamily Twilight CPU MUSCLE T-Coffee MUSCLE-p NWNSI CLUSTALW FFSNT Align-m Edgar,