1. Practical multiple sequence algorithms Sushmita Roy BMI/CS 576 www.biostat.wisc.edu/bmi576/ Sushmita Roy sroy@biostat.wisc.edu Sep 24th, 2013

2. Goals for today Review Guide-tree based multiple sequence alignment Two practical implementations of algorithms for multiple sequence alignment –CLUSTALW –MUSCLE

3. The problems with progressive alignment Greedy –The tree might not be correct, that is, reflect an incorrect ordering of how sequences should be joined –Errors in alignment Even if the tree is correct, there might be some positions that are misaligned. Choice of alignment parameters –Especially when the sequences are diverged and there are more mismatches than identities For closely related sequences, identities dominate over mismatches –Different weight matrices might be optimal for different evolutionary distances. –Gaps do not occur randomly Gaps more likely to occur between “secondary structures” rather than within them.

4. ClustalW A progressive alignment algorithm with several heuristics Based on a guide tree approach Dynamically varies the gap penalties in a position and residue specific manner Weight different sequences differently Thompson et al, 1994

5. Alignments based on guide trees Build up a multiple sequence alignment by progressively adding new sequences by following the order of a phylogenetic tree. Needs sequences to have different extents of divergence Start with aligning the closest pairs of sequences. Gaps inserted in the earlier alignments should be preserved as these gaps are most reliable.

6. Steps in ClustalW Align all pairs of sequences separately to create a pairwise distance matrix. Calculate a guide tree from the matrix Align sequences progressively according to guide tree starting from the leaves

7. Calculating the pairwise distance For two sequences with the following alignment –AATAATA ATAA_TA Similarity S –No. of identical bases/size of alignment 4/7 for the above example Distance=1-S

8. Example of creating distance matrix Consider four sequences 1.AAAC 2.AGC 3.ACC 4.GAC Generate pairwise alignments for all pairs of sequences

9. Pairwise alignment for all the pairs of sequences AAAC _AGC AAAC _ACC AAAC _GAC 1. and 2. 1. and 3. 1. and 4. 2. and 3. 2. and 4. AGC ACC AGC GAC 3. and 4. ACC GAC Sequence pairAlignment X0.5 X0.330.67 X X 1. 2.3.4. 2. 3. 4. % similarity 2/4 2/3 1/3 Distance 0.5 0.33 0.67

10. Creating a tree from the distance matrix using UPGMA UPGMA: Unweighted pair group method using arithmetic averages Represent all sequences as the leaf nodes of a tree Merge two closest nodes at a time to create a new node in the tree –Set new node at height determined by nodes being merged Let i and j be two existing nodes that are merged to create a new node Distance between a new node k created from two existing nodes i and j and other nodes l Distance between node k and lNumber of elements in cluster associated with node j

11. UPGMA in practice 1234 X0.5 X0.330.67 X X 1 1234 2 3 4 5 X0.5 X0.67 X 1 4 5 145 d 23 /2=1/6 Place new node at height d 23 /2

12. UPGMA in practice 12 3 4 5 X0.5 X0.67 X 1 4 5 145 1/6 d 14 /2=0.25 6 X0.59 X 5 6 12 3 4 5 1/6 d 14 /2=0.25 6 7 d 56 /2=0.29

13. Computing the sum of scores for two alignments Assume we have two alignments corresponding to intermediate nodes of the guide tree At each step we maximize over score from –aligning column i in A1 to a column j in A2 –aligning column i in A1 to gaps in A2 –aligning column j in A2 to gaps in A1 ClustalW uses an average of all pairwise comparisons between two alignments AAAC _GAC AGC ACC Alignment A1Alignment A2

14. ClustalW scores for aligning columns from two alignments AAAC _GAC AGC ACC Alignment 1 Alignment 2 Score of aligning column 3 from Alignment 1 and column 2 from alignment 2 Assume a score of 1 for mismatch, 2 for match and 0 for gap

15. An example for aligning two alignments 00000 011.522 0 0 A A A C _ G A C A G C A C C Max of three options A_A_ A A_A_ _ ____ A Alignment 1 Alignment 2

16. Assigning sequence weights in ClustalW ClustalW also considers different weights for different sequences Closely related sequences need to be down- weighted Divergent sequences are up-weighted Uses the branch length of the tree to calculate weights

17. ClustalW weights of sequences Weight of a sequence: sum of branch lengths from root to leaf, but sequences sharing a branch share the weight For example, weight for Hbb_Human=0.081+(0.226/2)+(0.061/4)+(0.015/5)+(0.062/6)

18. ClustalW score computation

19. ClustalW gap handling rules Gap penalties are dynamically adjusted For each position in the alignment compute a possible gap penalty value –If there is a gap in any of the sequences being aligned reduce its penalty –If there is no gap, and this position is <8 positions from another gap, increase the gap open penalty –Reduce gap penalty for positions inside a hydrophilic stretch of 5 residues –Otherwise use the gap penalty associated with residue-specific gap penalties estimated based on the known alignments –different amino acid substitution matrices depending upon the estimated divergence of sequences being aligned at a particular stage may be selected.

20. Position-specific gap penalties in ClustalW Higgins et al, methods in Enzymology, 1996 Hydrophilic stretchesExisting gap High gap penalty within 8 positions of existing gaps

21. Switching weight matrices Dynamically switch between matrices depending upon the average similarity between sequences being aligned PAM 80-100%: PAM20 60-80%: PAM60 40-60%: PAM120 0-40%: PAM350 BLOSUM 80-100%: BLOSUM80 60-80%: BLOSUM62 30-60%: BLOSUM45 0-30%: BLOSUM30

22. Applying ClustalW to SH3 domain proteins Proteins share <12% sequence identity Alignment blocks correspond to beta strand secondary structures

23. Summary of ClustalW Guide tree method Complex gap penalty rules Sequences are weighted to reduce the importance of very similar sequences Adaptive scoring matrix

24. MUSCLE: Multiple Sequence Comparison by log-expectation Progressive + iterative Has three main stages Stage1: Draft Progressive Stage 2: Improved Progressive Stage 3: Refinement: –Select pairs of subtrees and re-align the alignment for the subtrees. –Keep if it improves alignment

25. Steps in MUSCLE Stage 1: Draft progressive Stage 2: Improved progressive Stage 3: Refinement

26. MUSCLE Stage 1 1.1 Compute k-mer distance matrix 1.2 Use UPGMA to make tree (TREE1) 1.3. Use guide tree to make first MSA

27. K-mer distance K-mer distance is defined from common fractional k-mer count ( F ) D=1-F Let k=2 Sequence2-mers AKFLAAK,KF, FL,LA LKFLLK, KF, FL A k-mer # of instances in sequence 1 Length of sequences

28. K-mer distance example Sequence2-mers AKFLAAK,KF, FL,LA LKFLFLLK, KF, FL,LF,FL K-mer (τ)# in sequence 1# in sequence 2Min(n1(t),n2(t)) AK100 KF111 FL121 LA100 LK010 LF020

29. Stage 2: Improved progressive 2.1 Recompute similarity of sequences of pairs using mutual alignment in MSA 2.2 Construct a phylogenetic tree (TREE2) using an alignment-based distance 2.3 Build a new progressive alignment only for subtrees where branching order has changed between TREE1 and TREE2 2.4 Repeat 2.3 until number of “reordered nodes” does not decrease.

30. Stage 2.1. Recomputing pairwise sequence similarity from a multiple alignment -TGTTAAC -TGT-AAC -TGT--AC ATGT---C ATGT-GGC An MSA TGTTAAC TGT-AAC TGTTAAC TGT--AC -TGTTAAC ATGT---C -TGTTAAC ATGT-GGC … Derived pairwise alignmentFraction identity 6/7 5/7 4/8 … Exclude gaps in both sequences

31. Stage 2.2: Phylogenetic tree creation Construct a phylogenetic tree using a Kimura distance D: fractional identity of sequences

32. Stage 2.3 Re-align only when branching order is changed Branching order same Branching order different: x branches before v Recompute alignment for these nodes

33. Stage 3: Iterative Refinement 3.1 Select a branch 3.2 Extract profiles 3.3 Re-align profiles 3.4 Update MSA if its score is better than current MSA

34. 3.1 Selecting a branch Select a branch in order of decreasing distance from the root MQTIF LH-IW LQSW MQTIF LHIW MQTIF LH-IW LQS-W LSF LQSW L-SW 1 2 3 4 5 6 Branch selection order: 1,2,3,4,5,6

35. 3.2 Extracting a profile MQTIF LH-IW LQSW MQTIF LHIW MQTIF LH-IW LQS-W L-S-W LSF LQSW L-SW 1 2 3 4 5 6 Delete branch 1 MQTIF Re-align profiles for subtrees LH-IW LQS-W L-S-W Is score better? yes Keep new alignment Discard LHI-W MQTIF LQS-W L-S-W

36. 3.2 Extracting a profile MQTIF LH-IW LQSW LHIW MQTIF LH-IW LQS-W L-S-W LSF LQSW L-SW 2 3 4 5 6 Delete branch 2 Re-align profiles for subtrees MQTIF LQS-W L-S-W Is score better? yes Keep new alignment Discard MQTIF LHIW LHI-W MQTIF LQS-W L-S-W 1

37. Summary of MUSCLE Three stage algorithm Stage 1: Draft progressive –k-mer distance –UPGMA tree (TREE1) –Guide tree based alignment (MSA1) Stage 2: Improved progressive –Distance derived from MSA1 –UPGMA tree (TREE2) –Redo alignment for nodes with changed orderings –Repeat until number of re-ordered nodes does not change Stage 3: Iterative refinement –Generate subtree profiles –Realign profiles –Keep realignment if of higher score –Repeat until no more improvement or fixed number of steps. MUSCLE-fast: Stage 1 MUSCLE-p: Stage1 and 2

38. Accuracy scores of different MSA algorithms on benchmark datasets Edgar, 2004, BMC Bioinformatics Accuracy measures the fraction of residues correctly aligned with the reference alignment

39. Run time of different MSA algorithm

40. Summary of algorithms ClustalW –Lots of heuristics for gaps –One guide tree and then alignment –Weights sequences –Dynamically selects scoring matrix depending upon sequence identity MUSCLE –Three-stage algorithm: Draft, Improved, Iterative refinement –Two guide trees –Uses k-mer distance for first tree –Selectively re-aligns using second tree –Refines iteratively by working on subtree-associated alignments –Fast and has as good or better quality alignments

41. How do MUSCLE and CLUSTALW work in practice Consider coding sequences of 15 yeast species Consider promoter sequences of 15 yeast species Align with MUSCLE and CLUSTALW

42. Protein sequence alignment MUSCLE CLUSTALW

43. Promoter sequence alignment MUSCLE CLUSTALW

44. Comparing alignment of promoters to shuffled sequences in CLUSTALW Original sequences Shuffled sequences

45. Comparing alignment of promoters to shuffled sequences in MUSCLE Original sequences Shuffled sequences

46. Conclusion Algorithms seemed similar for protein/coding sequences Algorithms gave different alignments for DNA sequence –Possibly DNA sequence is harder to align –DNA sequence in non-coding regions are even harder to align

47. Summary of sequence alignment algorithms Pairwise alignment –Global: (Needleman-Wunsch) –Local: (Smith-Waterman) Database searching –BLAST Multiple sequence alignment –Star alignment –Progressive alignment with guide tree: CLUSTALW –Progressive + Iterative alignment with guide tree: MUSCLE