needleman-wunch algorithm

1. NEEDLEMAN WUNCH
ALGORITHM
HARSHITA BHAWSAR
M.SC LIFE SCIENCE
NIT ROURKELA

2. What is Needleman-Wunsch
algorithm?
The Needleman–Wunsch algorithm is an algorithm used
in bioinformatics to align protein or nucleotide sequences.
 It performs a global alignment on two sequences.
 The algorithm was developed by Saul B. Needleman and Christian D.
Wunsch and published in 1970.
 It is an example of Dynamic Programming and It was one of the first
applications of dynamic programming to compare biological sequences.

3.  Even for relatively short sequences, there are lots of possible alignments.
But it will take a long time to assess each alignment one-by-one , to find
the best alignment.
 The Needleman-Wunsch algorithm saves us the trouble of assessing all the
many possible alignments to find the best one.
 The N-W algorithm takes time proportion to n2 to find the best alignment
of two sequences that are both n letters long.
.

4. Alignment methods
 Alignment:- Arranging the sequence of DNA/RNA or PROTEIN to identify
similarities.
 2 types:-
Global and local sequence alignment methods
 Global : Needleman-Wunch algorithm
 Local : Smith-Waterman algorithm
These two dynamic programming alignment algorithm are guaranteed to
give OPTIMAL alignments

5. Goals of sequence alignment
 Measure the similarity
 Observe patterns of sequence conservation between related biological
species and variability of sequences over time.
 Infer evolutionary relationships.

6. Algorithm

7. Steps
 1. Initialization
 2 Matrix fill or scoring
 3. Traceback and alignment

8. RULES
 Put the gap in the first
 Fill the first column and last row with gap values
 Value of Box beside + Gap value
 Value of Box bottom + Gap value
 Diagonal value + {match/mismatch}

9. Lets see an example….
TWO SEQUENCES WILL BE ALIGNED:-
GATC (#SEQUENCE 1)
GAGC (#SEQUENCE 2)

10. Initilization
Create Matrix with M + 1 columns and N + 1 rows.
M= sequence 1
N= sequence 2
0
C
G
A
G
-
- G A T C

11. Matrix Fill
Fill the first column and For match=+1; Mismatch= -1; Gap= -2
last row with gap values
We putting the values by adding the gap values
With the beside box
0 -2 -4
C
G
A
G
-
- G A T C

12. For match=+1; Mismatch= -1; Gap= -2
-8
-6
-4
-2
0 -2 -4 -6 -8
C
G
A
G
-
- G A T C

13. Scoring
Parameters
 Value of Box beside + Gap value match=+1; Mismatch= -1;
 Value of Box bottom + Gap value Gap= -2
 Diagonal value + {match/mismatch}
-8
-6
-4
-2
0 -2 -4 -6 -8
C
G
A
G
-
- G A T C
-
4 -4+1
1

14. Scoring
match=+1; Mismatch= -1; Gap= -2
-8
-6
-4
-2 1 -1 -3 -5
0 -2 -4 -6 -8
C
G
A
G
-
- G A T C

15. Continuing the procedure…
match= +1; Mismatch= -1; Gap= -2
-8 -5 -2 -1 2
-6 -3 0 1 -1
-4 -1 2 0 -2
-2 1 -1 -3 -5
0 -2 -4 -6 -8
C
G
A
G
-
- G A T C

16. Traceback Step
 After scoring is done we get the maximum global alignment score at the
end. It may be in negative or positive.
 The trace back step will determine the actual alingment(s) that result in the
maximum score.
 In this step we need to come back towards zero.
 Since we have kept the pointers
to all the predecessors, so the
traceback step become simple.
-8 -5 -2 -1 2
-6 -3 0 1 -1
-4 -1 2 0 -2
-2 1 -1 -3 -5
0 -2 -4 -6 -8
C
G
A
G
-
- G A T C

17. we follow the pointers
-8 -5 -2 -1 2
-6 -3 0 1 -1
-4 -1 2 0 -2
-2 1 -1 -3 -5
0 -2 -4 -6 -8
C
G
A
G
-
- G A T C

18. GAGC
It`s the optimal alignment
GA T C
-8 -5 -2 -1 2
-6 -3 0 1 -1
-4 -1 2 0 -2
-2 1 -1 -3 -5
0 -2 -4 -6 -8
C
G
A
G
-
- G A T C

19. Other example…
AGC and AACC
For alignment we need to look at the pointers:-
= sequence
= gaps
We got 3 optimal alignment:-
A-GC AG-C -AGC
AAAC AACC AACC
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

20. Checking..!
 We can also check our alignment is right or not, by doing scoring
manually.
 Eg:- GAGC A-GC
GATC AACC
+1+1-1+1 +1-2-1+1
= 2 = -1
This score should must be equal to the maximum score of traceback.
If it is then it`s a perfect alingment.