Principles of Bioinformatics. BIO540/STA569/CSI660 Fall 2010
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1 Principles of Bioinformatics BIO540/STA569/CSI660 Fall 2010
2 Lecture 11 Multiple Sequence Alignment I
3 Administrivia
4 Administrivia The midterm examination will be Monday, October 18 th, in class. Closed book and notes. More details soon. Fall 2010 BIO540/STA569/CSI660 4
5 Administrivia I ve put up a set of exercises on pairwise sequence alignment on the class web page. They are not a formal homework, but rather a resource to help you study for the midterm exam. Fall 2010 BIO540/STA569/CSI660 5
6 Today s Content
7 Readings MSA Basics 4.5, 6.4 Blocks, Motifs and Patterns 4.8, 4.9, 6.1, 6.3 HMMs 6.2 Alternatives 4.10, 6.5, 6.6 Fall 2009 BIO540/CSI660 7
8 Multiple Sequence Alignment (MSA) Just as two sequences can be aligned to maximize the common elements of the pair, three or more sequences can be aligned in the same manner. This is multiple sequence alignment (MSA). Fall 2009 BIO540/CSI660 8
9 Multiple Sequence Alignment MSA has many uses: Detect the overall similarity of a set of sequences. Find similar regions in sequences. As the starting point of a phylogenetic analysis to determine evolutionary relatedness. Find overlapping DNA fragments as part of genome sequencing efforts. Fall 2009 BIO540/CSI660 9
10 From Pairwise to Multiple Sequence Alignment In principle, MSA can be done as an extension of the dynamic programming used for pairwise sequence alignment. However, each sequence adds a new dimension to the alignment matrix. This causes the matrix to grow too quickly to either store it or compute using it. Fall 2009 BIO540/CSI660 10
11 From Pairwise to Multiple Sequence Alignment In a pairwise alignment, the size of the matrix is approximately l 2 where l is the length of the longer of the two sequences. So, for two sequences of length 300, the matrix has 90,000 elements. Fall 2009 BIO540/CSI660 11
12 From Pairwise to Multiple Sequence Alignment For a MSA, the size of the matrix will be approximately l n where l is the length of the longest of the sequences to be aligned, and n is the number of sequences. Fall 2009 BIO540/CSI660 12
13 From Pairwise to Multiple Sequence Alignment Using the l n formula, and assuming we have length 300 sequences, we get the following matrix sizes: 3 sequences: = 27,000, sequences: = 2,430,000,000, sequences: = x = 5,904,900,000,000,000,000,000, sequences: = x sequences: = x Fall 2009 BIO540/CSI660 13
14 From Pairwise to Multiple Sequence Alignment Clearly, a straight-forward generalization of the dynamic programming algorithm is impractical for MSA. Fall 2009 BIO540/CSI660 14
15 Defining Multiple Sequence Alignment Before we look at how MSA is done in practice, we need to define just what it is we want to do. Fall 2009 BIO540/CSI660 15
16 Scoring Multiple Sequence Alignments A tractable way of doing MSA starts with the sum of pairs (SP) scoring function. Instead of, for example, an unconstrained combination of all the symbols, just look at the scores of the pairwise combinations. Fall 2009 BIO540/CSI660 16
17 Scoring Multiple Sequence Alignments The SP score is the sum of all pairwise scores of symbols in the alignment column. For example: S S - C - Fall 2009 BIO540/CSI660 17
18 Scoring Multiple Sequence Alignments The SP score is the sum of all pairwise scores of symbols in the alignment column. For example: S S - C - Here the SP-score = p(s,s) +p(s,-) + p(s,c) + p(s,-) + p(s,-) + p(s,c) + p(s,-) + p(-,c) + p(-,-) + p(c,-) Fall 2009 BIO540/CSI660 18
19 Scoring Multiple Sequence Alignments The SP score is the sum of all pairwise scores of symbols in the alignment column. For example: S S - C - Here the SP-score = p(s,s) +p(s,-) + p(s,c) + p(s,-) + p(s,-) + p(s,c) + p(s,-) + p(-,c) + p(-,-) + p(c,-) Fall 2009 BIO540/CSI660 19
20 Scoring Multiple Sequence Alignments The SP score is the sum of all pairwise scores of symbols in the alignment column. For example: S S - C - Here the SP-score = p(s,s) +p(s,-) + p(s,c) + p(s,-) + p(s,-) + p(s,c) + p(s,-) + p(-,c) + p(-,-) + p(c,-) Fall 2009 BIO540/CSI660 20
21 Scoring Multiple Sequence Alignments The SP score is the sum of all pairwise scores of symbols in the alignment column. For example: S S - C - Here the SP-score = p(s,s) +p(s,-) + p(s,c) + p(s,-) + p(s,-) + p(s,c) + p(s,-) + p(-,c) + p(-,-) + p(c,-) Fall 2009 BIO540/CSI660 21
22 Scoring Multiple Sequence Alignments The SP score is the sum of all pairwise scores of symbols in the alignment column. For example: S S - C - Here the SP-score = p(s,s) +p(s,-) + p(s,c) + p(s,-) + p(s,-) + p(s,c) + p(s,-) + p(-,c) + p(-,-) + p(c,-) Fall 2009 BIO540/CSI660 22
23 Scoring Multiple Sequence Alignments How do we score p(-,-)? Generally, p(-, -) = 0. Fall 2009 BIO540/CSI660 23
24 Scoring Multiple Sequence Alignments The rationale for this is that, if we set p(-, -) = 0, then if we pick out a pair of sequences where we have spaces for both in the column, then the pairwise score of them is the same as the SP-score for the two. Fall 2009 BIO540/CSI660 24
25 Scoring Multiple Sequence Alignments The SP score is the sum of all pairwise scores of symbols in the alignment column. For example: S S - C - So, the example s SP-score = = -13 Fall 2009 BIO540/CSI660 25
26 Scoring Multiple Sequence Alignments Picking a pairwise alignment out of a multiple alignment is known as an induced pairwise alignment. In an induced pairwise alignment, a column that has a gap in both sequences ( -,- ) is removed. Fall 2009 BIO540/CSI660 26
27 Scoring Multiple Sequence Alignments In general, the SP-score for the entire multiple sequence list, α ( alpha ), is the same as the sum of the pairwise alignment scores. SP-score(α) = Σ i < j score(α ij ) For this to be true, p(-, -) must equal zero. Fall 2009 BIO540/CSI660 27
28 Scoring Multiple Sequence Alignments Now that we have a way to evaluate multiple alignments, we can now try and determine the alignment of a set of sequences that gives us a maximum alignment score. These alignments of maximum score are known as optimal alignments. Fall 2009 BIO540/CSI660 28
29 From Pairwise to Multiple Sequence Alignment In practice, a dynamic programming MSA algorithm will only construct and examine a very small portion of the multidimensional alignment matrix. Glossing over some details, the algorithm sets a threshold, and it will not build alignments beyond elements that score worse than the threshold. Fall 2009 BIO540/CSI660 29
30 From Pairwise to Multiple Sequence Alignment By analogy to mining, this algorithm only tunnels into portions of the matrix that have rich veins of mineral - in this case high partial alignment values. Fall 2009 BIO540/CSI660 30
31 From Pairwise to Multiple Sequence Alignment Fall 2009 BIO540/CSI660 31
32 Progressive Methods of MSA Even so, these dynamic programming MSA methods have some serious problems. They are still limited to alignments of about a half-dozen sequences. They do not guarantee an optimal solution. Although it is very likely that they will find an alignment that is at least very close to optimal. Fall 2009 BIO540/CSI660 32
33 Progressive Methods of MSA However, a different use of dynamic programming can be used for practical MSA. This technique, progressive alignment, starts by aligning the most similar sequences, and then progressively adds the other, less similar, sequences to the overall alignment. Fall 2009 BIO540/CSI660 33
34 Progressive Methods of MSA Progressive alignment is usually done as follows: Make an assessment of the phylogenetic relatedness of the sequences and their branching. Progressively align more and more sequences, starting with the closest ones. At each point, either align a sequence to a subalignment, or two sub-alignments, depending on what's closest. Fall 2009 BIO540/CSI660 34
35 Progressive Methods of MSA The method is heuristic, that is, it is not guaranteed to produce optimal alignments. Fall 2009 BIO540/CSI660 35
36 Progressive Methods of MSA The relatedness of the sequences is done by creating a tree. The tree shows how closely related the sequences to be aligned are. The tree can be created by doing pairwise comparisons of all the sequences. Fall 2009 BIO540/CSI660 36
37 Progressive Methods of MSA Trees can be created either using a full Needleman-Wunsch dynamic programming global alignment of each pair of sequences, or a simplified, faster comparison that only looks at identical amino acids in k-tuples in the sequences. k-tuples are patterns of characters of length k occurring in the two sequences. Fall 2009 BIO540/CSI660 37
38 Progressive Methods of MSA Using the tree as a guide, the closest two sequences are aligned. This creates a sub-alignment. Fall 2009 BIO540/CSI660 38
39 Progressive Methods of MSA Then, the next two closest sequences/subalignments are aligned. Fall 2009 BIO540/CSI660 39
40 Progressive Methods of MSA This continues until all the sequences have been aligned. Fall 2009 BIO540/CSI660 40
41 Progressive Methods of MSA Seq1 Seq2 Seq3 Seq4 Seq5 Seq6 Fall 2009 BIO540/CSI660 41
42 Progressive Methods of MSA Seq2 Seq3 Seq4 Seq5 Seq6 Seq1 Fall 2009 BIO540/CSI660 42
43 Progressive Methods of MSA Seq2 Seq3 Seq4 Seq5 Seq1 Seq6 Fall 2009 BIO540/CSI660 43
44 Progressive Methods of MSA Seq3 Seq4 Seq5 Seq1 Seq6 Seq2 Fall 2009 BIO540/CSI660 44
45 Progressive Methods of MSA Seq3 Seq4 Seq1 Seq6 Seq2 Seq5 Fall 2009 BIO540/CSI660 45
46 Progressive Methods of MSA Seq3 Seq4 Seq1 Seq6 Seq2 Seq5 Fall 2009 BIO540/CSI660 46
47 Progressive Methods of MSA Seq3 Seq4-43 Seq1 Seq6 Seq2 Seq5 Fall 2009 BIO540/CSI660 47
48 Progressive Methods of MSA Seq3 45 Seq4 Seq Seq6 Seq2 Seq5 Fall 2009 BIO540/CSI660 48
49 Progressive Methods of MSA Seq3 45 Seq4 25 Seq Seq Seq2-25 Seq5 Fall 2009 BIO540/CSI660 49
50 Progressive Methods of MSA Seq3 45 Seq4 25 Seq Seq Seq2-25 Seq5 Fall 2009 BIO540/CSI660 50
51 Progressive Methods of MSA Seq3 Seq4 Seq Seq Seq2-25 Seq5 Fall 2009 BIO540/CSI660 51
52 Progressive Methods of MSA Seq3 Seq4 Seq Seq Seq2-25 Seq5 Fall 2009 BIO540/CSI660 52
53 Progressive Methods of MSA Seq3 Seq4 Seq Seq Seq2-25 Seq5 Fall 2009 BIO540/CSI660 53
54 Progressive Methods of MSA Seq3 Seq Seq6 Seq1-37 Seq2 Seq5 Fall 2009 BIO540/CSI660 54
55 Progressive Methods of MSA Seq3 Seq4 17 Seq Seq5 Seq6 Seq2 Fall 2009 BIO540/CSI660 55
56 Progressive Methods of MSA Seq3 Seq4 17 Seq Seq5 Seq6 Seq2 Fall 2009 BIO540/CSI660 56
57 Progressive Methods of MSA Seq3 Seq4 17 Seq6 Seq1 Seq2 Seq5 Fall 2009 BIO540/CSI660 57
58 Progressive Methods of MSA Seq3 Seq4 Seq Seq6 Seq2 Seq5 Fall 2009 BIO540/CSI660 58
59 Progressive Methods of MSA Seq3 Seq4 Seq Seq6 Seq2 Seq5 Fall 2009 BIO540/CSI660 59
60 Progressive Methods of MSA Seq3 Seq4 Seq1 Seq6 Seq2 Seq5 Fall 2009 BIO540/CSI660 60
61 Progressive Methods of MSA Seq3 Seq4 7 Seq1 Seq6 Seq2 Seq5 Fall 2009 BIO540/CSI660 61
62 Progressive Methods of MSA Seq3 Seq4 Seq1 Seq6 Seq2 Seq5 Fall 2009 BIO540/CSI660 62
63 Progressive Methods of MSA Seq3 Seq4 Seq1 Seq6 Seq2 Seq5 Fall 2009 BIO540/CSI660 63
64 Progressive Methods of MSA Seq3 Seq4 Seq1 Seq6 Seq2 Seq5 Fall 2009 BIO540/CSI660 64
65 Progressive Methods of MSA Seq3 Seq4 Seq1 Seq6 Seq2 Seq5 Fall 2009 BIO540/CSI660 65
66 Progressive Methods of MSA Seq3 Seq4 Seq1 Seq6 Seq2 Seq5 Fall 2009 BIO540/CSI660 66
67 Progressive Methods of MSA Seq3 Seq4 Seq1 Seq2 Seq6 Seq5 Fall 2009 BIO540/CSI660 67
68 Progressive Methods of MSA Seq3 Seq4 Seq1 Seq2 Seq6 Seq5 Fall 2009 BIO540/CSI660 68
69 Progressive Methods of MSA Seq3 Seq4 Seq1 Seq2 Seq5 Seq6 Fall 2009 BIO540/CSI660 69
70 Progressive Methods of MSA Seq3 Seq4 Seq1 Seq2 Seq5 Seq6 Fall 2009 BIO540/CSI660 70
71 Progressive Methods of MSA Seq3 Seq4 Seq1 Seq2 Seq5 Seq6 Fall 2009 BIO540/CSI660 71
72 Progressive Methods of MSA Seq3 Seq4 Seq1 Seq2 Seq5 Seq6 Fall 2009 BIO540/CSI660 72
73 Progressive Methods of MSA Seq3 Seq4 Seq1 Seq2 Seq5 Seq6 Fall 2009 BIO540/CSI660 73
74 Clustal The most popular program to do progressive multiple sequence alignment is Clustal. The current version is ClustalW. If you re using a UNIX computer with X- Windows, there is a ClustalX version. Fall 2009 BIO540/CSI660 74
75 Clustal The relatedness of any two sequences is determined by a genetic distance. This distance is the number of mismatched positions divided by the number of matched positions. Gaps are not counted in this value. Fall 2009 BIO540/CSI660 75
76 Clustal Genetic Distance Example: LQLLKDE--QWIDVPPMRHSIVVNLGDQ LQATRDGGRTWITVQPVEGAFVVNLGDH Number of plusses: 12 Number of minuses: 13: Genetic Distance = 13/12 = Fall 2009 BIO540/CSI660 76
77 Clustal The W in ClustalW stands for weighting. Different sequences are given different weights, or significance values in the alignment. Fall 2009 BIO540/CSI660 77
78 Clustal In the weighting scheme for ClustalW, distant sequences are more heavily weighted in the alignment than more closely related ones. Fall 2009 BIO540/CSI660 78
79 Clustal The rationale for this is that closely related sequences provide little extra information for alignment purposes. This is similar in spirit to PSI-BLAST s removing almost identical sequences. Fall 2009 BIO540/CSI660 79
80 Clustal How gaps are placed is very important in determining the overall quality of the final multiple sequence alignment. Researchers have found patterns in gaps in related proteins - gaps avoid secondary structure elements, and conserved regions in the aligned sequences. ClustalW tries to model these patterns. Fall 2009 BIO540/CSI660 80
81 Clustal ClustalW uses an affine gap model, with a gap opening penalty and a further penalty for each position in the gap. The cost of the gap opening penalty changes in different circumstances. Fall 2009 BIO540/CSI660 81
82 Clustal First, the basic Gap Opening Penalty (GOP) and a Gap Extension Penalty (GEP) are chosen. Then, they are modified as follows for each pair of sequences/ sub-alignments to be aligned: GOP A B (GOP initial + log (min(m, N))) GEP GEP initial (1.0 + log (min(m, N))) where A is the average value for a mismatch in the amino acid weight matrix, B is the percent identity of the two sequences, and M and N are the lengths of the sequences to be aligned, Fall 2009 BIO540/CSI660 82
83 Clustal Then for each position in the alignment, they are further modified as follows: Use lower gap penalties at positions where gaps already occur, Increase gap penalties adjacent to positions where gaps already occur, Reduce gap penalties where stretches of hydrophilic residues occur, and Increase or decrease gap penalties using tables of the observed frequencies of gaps adjacent to each of the 20 amino acids. Fall 2009 BIO540/CSI660 83
84 MSA with Clustal
85 MSA with Clustal There are several things that should be taken into account when doing MSA with any program. Here are some things to be aware of when doing MSA with ClustalW. Fall 2009 BIO540/CSI660 85
86 Clustal & Substitution Matrices
87 MSA with Clustal The first decision is what substitution matrix to use. For example, the ClustalW server at EBI ( has PAM BLOSUM Gonnet, and DNA identity matrices. Fall 2009 BIO540/CSI660 87
88 MSA with Clustal We are familiar with the amino acid substitution matrices. The EBI web site has the following comments: The Blosum matrices are best for detecting local alignments. The Blosum62 matrix is the best for detecting the majority of weak protein similarities. The Blosum45 matrix is the best for detecting long and weak alignments. Fall 2009 BIO540/CSI660 88
89 PAM and BLOSUM According to the EBI web site help, the following matrices are roughly equivalent: PAM100 and Blosum90 PAM120 and Blosum80 PAM160 and Blosum60 PAM200 and Blosum52 PAM250 and Blosum45 Fall 2009 BIO540/CSI660 89
90 Gonnet Matrices Gonnet, Cohen and Benner (1992) used exhaustive pairwise alignments of the protein databases then available to create a distance matrix. This matrix was used iteratively to refine the alignment, and then refine the matrix. They propose that their matrix should be used for an initial alignment, and that a PAM matrix suitable for the task s distance then be used for subsequent ones. Fall 2009 BIO540/CSI660 90
91 DNA Identity Matrices The last class of matrices for use with Clustal at EBI is the DNA identity matrix. Compared to the others, it is a simple matrix. Each DNA base match is worth +1, and Each mismatch is worth -10,000. This virtually ensures aligning no mismatched positions, creating gaps in preference to mismatches. Depending on the gap penalties, of course. Fall 2009 BIO540/CSI660 91
92 Other Factors
93 MSA with ClustalW Some things to keep in mind when aligning with ClustalW. If the input to Clustalw is already aligned, any subsequent alignment will not remove existing gaps. The order of the sequences in the input is significant. Different orders of the same sequences can give different results. Fall 2009 BIO540/CSI660 93
94 MSA with ClustalW Some things to keep in mind when aligning with ClustalW. ClustalW has many parameters (cf. the server at EBI). Some of them are often not worthwhile to adjust (e.g. the gap penalties). If you are using ClustalW extensively, you should read the papers on it by the authors. Fall 2009 BIO540/CSI660 94
95 MSA with ClustalW If you are using ClustalW extensively, you should read the papers on it by the authors. Higgins, D.G., Thompson, J.D., and Gibson, T.J Using CLUSTAL for Multiple Sequence Alignments. Methods Enzymol. 266: Several older references. Fall 2009 BIO540/CSI660 95
96 MSA with ClustalW Additional things to keep in mind when aligning with ClustalW. If you know the secondary structures of one of the sequences in the alignment, that information can be used to bias the alignment. Gaps will be penalized more highly in secondary structure regions. Fall 2009 BIO540/CSI660 96
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