Needleman-Wunsch Algorithm 1

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Presented By:

•Proshanta Kumar Shil ID:141-15-3140 Section:B Department of CSE Daffodil International University

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What is Needleman-Wunsch algorithm?

The Needleman–Wunsch algorithm is an algorithm used in bioinformatics to align protein or nucleotide sequences.• It was one of the first applications of dynamic programming to

compare biological sequences. • The algorithm was developed by Saul B. Needleman and

Christian D. Wunsch and published in 1970.

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Alignment methods

• Global and local sequence alignment methods• Global : Needleman-Wunch• Local : Smith-Waterman

• Database Search • BLAST• FASTA

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Goals of sequence databases

• To know about a newly sequenced database.• To find the similarity of a unique sequence to another gene

that has a known function.• To find the similarity of a new protein in a lower organism to

a protein from another species.

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Alignment Algorithms

• Global : Needleman-Wunch• Local : Smith-Watermann• These two dynamic programming alignment algorithm

are guaranteed to give OPTIMAL alignments• But O(m*n) quadratic

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Needleman-Wunsch Method

• For example, the two hypothetical sequences• abcdefghajklm• abbdhijk

• could be aligned like this • abcdefghajklm• || | | || • abbd...hijk• As shown, there are 6 matches,• 2 mismatches, and one gap of length 3.

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Needleman-Wunsch Method

• The alignment is scored according to a payoff matrix

• $payoff = { match => $match,• mismatch => $mismatch,• gap_open => $gap_open,• gap_extend => $gap_extend };

• For correct operation, match must be positive,• and the other entries must be negative.

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Needleman-Wunsch Method

• Given the payoff matrix • $payoff = { match => 4,• mismatch => -3,• gap_open => -2,• gap_extend => -1 };

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Needleman-Wunsch Method

• The sequences • abcdefghajklm• abbdhijk• are aligned and scored like this • a b c d e f g h a j k l m• | | | | | | • a b b d . . . h i j k• match 4 4 4 4 4 4 • mismatch -3 -3• gap_open -2• gap_extend -1-1-1• for a total score of 24-6-2-3 = 13.

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Steps:

• 1. Initialization

• 2 Matrix fill or scoring

• 3. Traceback and alignment

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Lets see an example…

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Fill in the Table 13

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A

G C

A

A

C

C

Two sequences will be aligned.

AGC (sequence #1) AACC (sequence #2)A simple scoring scheme will be used

Initialization step: Create Matrix with M + 1 columnsand N + 1 rows.For match=+1; Mismatch=-1; Gap=-2

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A

G C

0 -2 -4 -6

A -2

A -4

C -6

C -8

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Fill in the Table

A

G C

0 -2 -4 -6

A -2 1

A -4

C -6

C -8

Matrix fill step: Each position Mi,j is defined to be theMAXIMUM score at position i,j Mi,j = MAXIMUM [

Mi-1, j-1 + si,,j (match or mismatch in the diagonal)

Mi, j-1 + w (gap in sequence #1)Mi-1, j + w (gap in sequence #2)]

Continuing the

procedure16

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A

G C

0 -2 -4 -6

A -2 1 -1 -3

A -4 -1 0 -2

C -6 -3 -2 -1

C -8 -5 -4 -1

Traceback step:Position at current cell and look at direct predecessors

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A

G C

0 -2 -4 -6

A -2 1 -1 -3

A -4 -1 0 -2

C -6 -3 -2 -1

C -8 -5 -4 -1

Traceback step:Position at current cell and look at direct predecessors

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A

G C

0 -2 -4 -6

A -2 1 -1 -3

A -4 -1 0 -2

C -6 -3 -2 -1

C -8 -5 -4 -1

AG-CAAAC

-AGCAAAC

A-GCAAAC

Summary

• The algorithm essentially divides a large problem into a series of smaller problems and uses the solutions to the smaller problems to reconstruct a solution to the larger problem.•  It is also sometimes referred to as the optimal matching

algorithm and the global alignment technique.• The Needleman–Wunsch algorithm is still widely used for

optimal global alignment, particularly when the quality of the global alignment is of the utmost importance.

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Any Question?

Thanks to All20