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Practical multiple sequence algorithms

Practical multiple sequence algorithms. Sushmita Roy BMI/CS 576 www.biostat.wisc.edu/bmi576/ Sushmita Roy sroy@biostat.wisc.edu Sep 24th, 2013. Goals for today. Review Guide-tree based m ultiple sequence alignment

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Practical multiple sequence algorithms

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  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 • AATAATAATAA_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 • AAAC • AGC • ACC • GAC • Generate pairwise alignments for all pairs of sequences

  9. Pairwise alignment for all the pairs of sequences Sequence pair Alignment % similarity Distance 1. and 2. AAAC_AGC 2/4 0.5 1. 2. 3. 4. 1. and 3. AAAC_ACC 2/4 0.5 1. 2. 1. and 4. AAAC_GAC 2/4 0.5 3. 4. 2. and 3. AGCACC 2/3 0.33 2. and 4. AGCGAC 1/3 0.67 3. and 4. ACCGAC 1/3 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 kcreated from two existing nodes iand j and other nodes l Distance between node k and l Number of elements in cluster associated with node j

  11. UPGMA in practice 1 2 3 4 1 Place new node at height d23/2 2 3 5 d23/2=1/6 4 1 2 3 4 1 4 5 1 4 5

  12. UPGMA in practice 1 4 5 1 d14/2=0.25 4 6 5 1/6 5 2 1 4 3 6 5 d56/2=0.29 7 d14/2=0.25 6 5 1/6 2 1 4 3

  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 Alignment A1 Alignment A2 AAAC_GAC AGCACC

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

  15. An example for aligning two alignments A A A C_ G A C A G CA C C Max of three options A _ A _ _ _ Alignment 1 A A _ _ A A 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 High gap penalty within 8 positions of existing gaps Hydrophilic stretches Existing gap Higgins et al, methods in Enzymology, 1996

  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 Let k=2 • K-mer distance is defined from common fractional k-mer count (F) • D=1-F # of instances in sequence 1 # of instances in sequence 1 A k-mer Length of sequences

  28. K-mer distance example

  29. Stage 2: Improved progressive 2.1 Recomputesimilarity 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 Derived pairwise alignment Fraction identity TGTTAAC TGT-AAC 6/7 An MSA Exclude gaps in both sequences -TGTTAAC -TGT-AAC -TGT--AC ATGT---C ATGT-GGC TGTTAAC TGT--AC 5/7 -TGTTAAC ATGT---C 4/8 -TGTTAAC ATGT-GGC 4/8 … …

  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 Recompute alignment for these nodes Branching order same Branching order different: x branches before v

  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 1 MQTIF MQTIF LH-IW 5 2 LHIW MQTIF LH-IW LQS-W LQSW 6 3 LQSW L-SW 4 LSF Branch selection order: 1,2,3,4,5,6

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

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

  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 Accuracy measures the fraction of residues correctly aligned with the reference alignment Edgar, 2004, BMC Bioinformatics

  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

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