![15
11-Dec-2015
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 the
MAXIMUM 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)]](https://image.slidesharecdn.com/presentation1-151211152325/85/Needleman-Wunsch-Algorithm-15-320.jpg)
Needleman-Wunsch Algorithm
- 2. Presented By: • Proshanta Kumar Shil ID:141-15-3140 Section:B Department of CSE Daffodil International University 11-Dec-2015 2
- 3. 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. 3 11-Dec-2015
- 4. Alignment methods • Global and local sequence alignment methods • Global : Needleman-Wunch • Local : Smith-Waterman • Database Search • BLAST • FASTA 4 11-Dec-2015
- 5. 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. 5 11-Dec-2015
- 6. 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 6 11-Dec-2015
- 7. 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. 7 11-Dec-2015
- 8. 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. 8 11-Dec-2015
- 9. Needleman-Wunsch Method • Given the payoff matrix • $payoff = { match => 4, • mismatch => -3, • gap_open => -2, • gap_extend => -1 }; 9 11-Dec-2015
- 10. 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. 10 11-Dec-2015
- 11. Steps: • 1. Initialization • 2 Matrix fill or scoring • 3. Traceback and alignment 11 11-Dec-2015
- 12. Lets see an example… 12 11-Dec-2015
- 13. Fill in the Table 13 11-Dec-2015 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
- 14. Initialization step: Create Matrix with M + 1 columns and N + 1 rows. For match=+1; Mismatch=-1; Gap=-2 14 11-Dec-2015 A G C 0 -2 -4 -6 A -2 A -4 C -6 C -8
- 15. 15 11-Dec-2015 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 the MAXIMUM 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)]
- 16. Continuing the procedure 16 11-Dec-2015 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
- 17. Traceback step: Position at current cell and look at direct predecessors 17 11-Dec-2015 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
- 18. Traceback step: Position at current cell and look at direct predecessors 18 11-Dec-2015 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-C AAAC -AGC AAAC A-GC AAAC
- 19. 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. 11-Dec-2015 19
- 20. Any Question? Thanks to All 20