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**Pairwise Sequence Alignment**

BMI/CS 576 Colin Dewey Fall 2010

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**Overview What does it mean to align sequences?**

How do we cast sequence alignment as a computational problem? What algorithms exist for solving this computational problem?

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**What is sequence alignment?**

Pattern matching ...CATCGATGACTATCCG... ATGACTGT Database searching . CATGCATGCTGCGTAC CATGGTTGCTCACAAGTAC CATGCCTGCTGCGTAA TACGTGCCTGACCTGCGTAC CATGCCGAATGCTG CATGCTTGCTGGCGTAAA suffix trees, locality-sensitive hashing,... BLAST Optimization problem Needleman-Wunsch, Smith-Waterman,... Statistical problem Pair HMMs, TKF, Karlin-Altschul statistic...

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**DNA sequence edits Substitutions: ACGA AGGA Insertions: ACGA ACCGGAGA**

Deletions: ACGGAGA AGA Transpositions: ACGGAGA AAGCGGA Inversions: ACGGAGA ACTCCGA

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Alignment scales For short DNA sequences (gene scale) we will generally only consider Substitutions: cause mismatches in alignments Insertions/Deletions: cause gaps in alignments For longer DNA sequences (genome scale) we will consider additional events Transposition Inversion In this course we will focus on the case of short sequences

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**What is a pairwise alignment?**

We will focus on evolutionary alignment matching of homologous positions in two sequences positions with no homologous pair are matched with a space ‘-’ A group of consecutive spaces is a gap CA--GATTCGAAT CGCCGATT---AT gap

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**Dot plots Not technically an “alignment”**

But gives picture of correspondence between pairs of sequences Dot represents similarity between segments of the two sequences

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Gaps sequences may have diverged from a common ancestor through various types of mutations: substitutions (ACGA AGGA) insertions (ACGA ACCGGAGA) deletions (ACGGAGA AGA) the latter two will result in gaps in alignments

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The Role of Homology character: some feature of an organism (could be molecular, structural, behavioral, etc.) homology: the relationship of two characters that have descended from a common ancestor homologous characters tend to be similar due to their common ancestry and evolutionary pressures thus we often infer homology from similarity thus we can sometimes infer structure/function from sequence similarity

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**Homology Example: Evolution of the Globins**

globins involved in transport of oxygen – ancient and diverse family Recall that hemoglobin is composed of four globin alpha polypeptides: 2 alpha and 2 beta Various versions of the alpha and beta subunits that are expressed at different times in development (e.g. embryonic, fetal, adult). Alphas and betas evolved from a common ancestor. A gene duplication event gave rise to precursor of alphas and precursor of betas. These evolved separately and then gene duplication events gave rise to various alpha and beta forms.

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**Homology homologous sequences can be divided into three groups**

orthologous sequences: sequences that diverged due to a speciation event (e.g. human a-globin and mouse a-globin) paralogous sequences: sequences that diverged due to a gene duplication event (e.g. human a-globin and human b-globin, various versions of both ) xenologous sequences: sequences for which the history of one of them involves interspecies transfer since the time of their common ancestor

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**Insertions/Deletions and Protein Structure**

Why is it that two “similar” sequences may have large insertions/deletions? some insertions and deletions may not significantly affect the structure of a protein loop structures: insertions/deletions here not so significant

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**Example Alignment: Globins**

figure at right shows prototypical structure of globins figure below shows part of alignment for 8 globins (-’s indicate gaps)

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**Issues in Sequence Alignment**

the sequences we’re comparing probably differ in length there may be only a relatively small region in the sequences that matches we want to allow partial matches (i.e. some amino acid pairs are more substitutable than others) variable length regions may have been inserted/deleted from the common ancestral sequence

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**Two main classes of pairwise alignment**

Global: All positions are aligned Local: A (contiguous) subset of positions are aligned CA--GATTCGAAT CGCCGATT---AT semi-global: find best match without penalizing gaps on the ends of the alignment ..GATT..... ....GATT..

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**Pairwise Alignment: Task Definition**

Given a pair of sequences (DNA or protein) a method for scoring a candidate alignment Do find an alignment for which the score is maximized

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**Scoring An Alignment: What Is Needed?**

substitution matrix S(a,b) indicates score of aligning character a with character b gap penalty function w(k) indicates cost of a gap of length k

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**Linear Gap Penalty Function**

different gap penalty functions require somewhat different algorithms the simplest case is when a linear gap function is used where s is a constant we’ll start by considering this case

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Scoring an Alignment the score of an alignment is the sum of the scores for pairs of aligned characters plus the scores for gaps example: given the following alignment VAHV---D--DMPNALSALSDLHAHKL AIQLQVTGVVVTDATLKNLGSVHVSKG we would score it by S(V,A) + S(A,I) + S(H,Q) + S(V,L) + 3s + S(D,G) + 2s …

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**The Space of Global Alignments**

some possible global alignments for ELV and VIS ELV VIS -ELV VIS- --ELV VIS-- ELV- -VIS E-LV VIS- ELV-- --VIS EL-V -VIS

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**Number of Possible Alignments**

given sequences of length m and n assume we don’t count as distinct and we can have as few as 0 and as many as min{m, n} aligned pairs therefore the number of possible alignments is given by C- -G -C G- Two strings of length=100 No gaps: 2N-1 possible alignments 199 in this example Gaps: 10**77 in this case See p. 188 in Waterman for an analysis of this issue. There must be k aligned pairs, 0 < k < min{n,m} There are n-choose-k ways to select the residues in n, and m-choose-k ways to select the residues in m So we have sum_{k>=0} (n-choose-k)(m-choosek) = (n+m choose k) possible alignments (for a local)

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**Number of Possible Alignments**

there are possible global alignments for 2 sequences of length n e.g. two sequences of length 100 have possible alignments but we can use dynamic programming to find an optimal alignment efficiently Stirling’s formula n! ~ sqrt{2\pin}(n/e)^n Two strings of length=100 No gaps: 2N-1 possible alignments 199 in this example Gaps: 10**77 in this case See p. 188 in Waterman for an analysis of this issue. There must be k aligned pairs, 0 < k < min{n,m} There are n-choose-k ways to select the residues in n, and m-choose-k ways to select the residues in m So we have sum_{k>=0} (n-choose-k)(m-choosek) = (n+m choose k) possible alignments (for a local)

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Dynamic Programming Algorithmic technique for optimization problems that have two properties: Optimal substructure: Optimal solution can be computed from optimal solutions to subproblems Overlapping subproblems: Subproblems overlap such that the total number of distinct subproblems to be solved is relatively small

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**Dynamic Programming Break problem into overlapping subproblems**

use memoization: remember solutions to subproblems that we have already seen 3 5 7 1 8 2 4 6

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Fibonacci example 1,1,2,3,5,8,13,21,... fib(n) = fib(n - 2) + fib(n - 1) Could implement as a simple recursive function However, complexity of simple recursive function is exponential in n x, x - 1, x - 2, x - 3, x - 4,... 1, 1, 2, 3, 5 Draw out function call stack

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**Fibonacci dynamic programming**

Two approaches Memoization: Store results from previous calls of function in a table (top down approach) Solve subproblems from smallest to largest, storing results in table (bottom up approach) Both require evaluating all (n-1) subproblems only once: O(n)

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**Dynamic Programming Graphs**

Dynamic programming algorithms can be represented by a directed acyclic graph Each subproblem is a vertex Direct dependencies between subproblems are edges 1 2 3 4 5 6 graph for fib(6)

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**Why “Dynamic Programming”?**

Coined by Richard Bellman in 1950 while working at RAND Government officials were overseeing RAND, disliked research and mathematics “programming”: planning, decision making (optimization) “dynamic”: multistage, time varying “It was something not even a Congressman could object to. So I used it as an umbrella for my activities”

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**Pairwise Alignment Via Dynamic Programming**

first algorithm by Needleman & Wunsch, Journal of Molecular Biology, 1970 dynamic programming algorithm: determine best alignment of two sequences by determining best alignment of all prefixes of the sequences

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**Dynamic Programming Idea**

consider last step in computing alignment of AAAC with AGC three possible options; in each we’ll choose a different pairing for end of alignment, and add this to the best alignment of previous characters AAA C AG AAAC C AG - Write out scenarios more abstractly Consider last column X_i aligned to Y_j X_i gapped Y_j gapped AAA - AGC C consider best alignment of these prefixes score of aligning this pair +

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**DP Algorithm for Global Alignment with Linear Gap Penalty**

Subproblem: F(i,j) = score of best alignment of the length i prefix of x and the length j prefix of y. Note base cases

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**Dynamic Programming Implementation**

given an n-character sequence x, and an m-character sequence y construct an (n+1) (m+1) matrix F F ( i, j ) = score of the best alignment of x[1…i ] with y[1…j ] A C G score of best alignment of AAA to AG

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**Initializing Matrix: Global Alignment with Linear Gap Penalty**

s 2s C G 3s 4s

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**DP Algorithm Sketch: Global Alignment**

initialize first row and column of matrix fill in rest of matrix from top to bottom, left to right for each F ( i, j ), save pointer(s) to cell(s) that resulted in best score F (m, n) holds the optimal alignment score; trace pointers back from F (m, n) to F (0, 0) to recover alignment

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**Global Alignment Example**

suppose we choose the following scoring scheme: +1 -1 s (penalty for aligning with a space) = -2

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**Global Alignment Example**

-2 -4 C G -6 -8 1 -1 -3 -5 one optimal alignment x: A A A C y: A G - C but there are three optimal alignments here (can you find them?)

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DP Comments works for either DNA or protein sequences, although the substitution matrices used differ finds an optimal alignment the exact algorithm (and computational complexity) depends on gap penalty function (we’ll come back to this issue)

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**Equally Optimal Alignments**

many optimal alignments may exist for a given pair of sequences can use preference ordering over paths when doing traceback highroad 1 lowroad 3 2 2 3 1 highroad and lowroad alignments show the two most different optimal alignments

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**Highroad & Lowroad Alignments**

C x: y: A - G C highroad alignment -4 -6 -2 A -2 1 -1 -3 A -4 -1 -2 lowroad alignment A x: A A A C -6 -3 -2 -1 y: - A G C C -8 -5 -4 -1

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**Dynamic Programming Analysis**

recall, there are possible global alignments for 2 sequences of length n but the DP approach finds an optimal alignment efficiently Two strings of length=100 No gaps: 2N-1 possible alignments 199 in this example Gaps: 10**77 in this case See p. 188 in Waterman for an analysis of this issue.

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**Computational Complexity**

initialization: O(m), O(n) where sequence lengths are m, n filling in rest of matrix: O(mn) traceback: O(m + n) hence, if sequences have nearly same length, the computational complexity is

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Local Alignment so far we have discussed global alignment, where we are looking for best match between sequences from one end to the other often, we will only want a local alignment, the best match between contiguous subsequences (substrings) of x and y

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**Local Alignment Motivation**

useful for comparing protein sequences that share a common motif (conserved pattern) or domain (independently folded unit) but differ elsewhere useful for comparing DNA sequences that share a similar motif but differ elsewhere useful for comparing protein sequences against genomic DNA sequences (long stretches of uncharacterized sequence) more sensitive when comparing highly diverged sequences

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**Local Alignment DP Algorithm**

original formulation: Smith & Waterman, Journal of Molecular Biology, 1981 interpretation of array values is somewhat different F (i, j) = score of the best alignment of a suffix of x[1…i ] and a suffix of y[1…j ]

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**Local Alignment DP Algorithm**

the recurrence relation is slightly different than for global algorithm Explain why zero: we can always choose a suffix of length zero from both strings

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**Local Alignment DP Algorithm**

initialization: first row and first column initialized with 0’s traceback: find maximum value of F(i, j); can be anywhere in matrix stop when we get to a cell with value 0

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**Local Alignment Example**

1 2 3 x: y: G A Match: +1 Mismatch: -1 Space: -2

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**More On Gap Penalty Functions**

a gap of length k is more probable than k gaps of length 1 a gap may be due to a single mutational event that inserted/deleted a stretch of characters separated gaps are probably due to distinct mutational events a linear gap penalty function treats these cases the same it is more common to use gap penalty functions involving two terms a penalty g associated with opening a gap a smaller penalty s for extending the gap

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Gap Penalty Functions linear affine

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**Dynamic Programming for the Affine Gap Penalty Case**

to do in time, need 3 matrices instead of 1 best score given that x[i] is aligned to y[j] best score given that x[i] is aligned to a gap best score given that y[j] is aligned to a gap

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**Why Three Matrices Are Needed**

consider aligning the sequences WFP and FW using g = -4, s = -1 and the following values from the BLOSUM-62 substitution matrix: S(F, W) = S(W, W) = 11 S(F, F) = S(W, P) = -4 S(F, P) = -4 the matrix shows the highest-scoring partial alignment for each pair of prefixes W F P -5 -6 -7 1 -4 6 2 No longer optimal substructure with previous subproblem formulization -WFP FW-- optimal alignment best alignment of these prefixes; to get optimal alignment, need to also remember WF FW -WF FW-

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**Global Alignment DP for the Affine Gap Penalty Case**

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**Global Alignment DP for the Affine Gap Penalty Case**

initialization traceback start at largest of stop at any of note that pointers may traverse all three matrices

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**Global Alignment Example (Affine Gap Penalty)**

g = -3, s = -1 A C A C T M : 0 -∞ -∞ -∞ -∞ -∞ Ix : -3 -∞ -∞ -∞ -∞ -∞ Iy : -3 -4 -5 -6 -7 -8 A -∞ 1 -5 -4 -7 -8 -4 -∞ -∞ -∞ -∞ -∞ -∞ -∞ -3 -4 -5 -6 A -∞ -3 -2 -5 -6 -5 -3 -9 -8 -11 -12 -∞ -∞ -7 -4 -5 -6 T -∞ -6 -4 -1 -3 -4 -6 -4 -4 -6 -9 -10 -∞ -∞ -10 -8 -5 -6

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**Global Alignment Example (Continued)**

-∞ -∞ -∞ -∞ -∞ Ix : -3 -∞ -∞ -∞ -∞ -∞ Iy : -3 -4 -5 -6 -7 -8 A -∞ 1 -5 -4 -7 -8 -4 -∞ -∞ -∞ -∞ -∞ -∞ -∞ -3 -4 -5 -6 A -∞ -3 -2 -5 -6 -5 -3 -9 -8 -11 -12 -∞ -∞ -7 -4 -5 -6 T -∞ -6 -4 -1 -3 -4 -6 -4 -4 -6 -9 -10 -∞ -∞ -10 -8 -5 -6 ACACT AA--T ACACT A--AT ACACT --AAT three optimal alignments:

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**Local Alignment DP for the Affine Gap Penalty Case**

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**Local Alignment DP for the Affine Gap Penalty Case**

initialization traceback start at largest stop at In his notes, Thomas had start at largest of M(I,j), I_x(I,j), I_y(I,j). This doesn’t seem right. It suggest we might get a higher score by ending an alignment with a gap than by not including that gap.

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**Gap Penalty Functions linear: affine:**

concave: a function for which the following holds for all k, l, m ≥ 0 e.g.

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**Concave Gap Penalty Functions**

1 2 3 4 5 6 7 8 9 10

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**Computational Complexity and Gap Penalty Functions**

linear: affine: concave Concave result due to Eppstein general: assuming two sequences of length n

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**Alignment (Global) with General Gap Penalty Function**

consider every previous element in the row consider every previous element in the column

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