The Needleman–Wunsch algorithm is an algorithm used in bioinformatics to align protein or nucleotide sequences. 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.The algorithm essentially divides a large problem (e.g. the full sequence) 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.

1. By Tarun shekhawat
Roll no.:DTU/2K14/BT/027

2.  Dynamic programming is used for optimal
alignment of two sequences. It finds the
alignment in a more quantitative way by
giving some scores for matches and
mismatches (Scoring matrices), rather than
only applying dots. By searching the highest
scores in the matrix, alignment can be
accurately obtained.

3.  Sequence alignment is a way of arranging the
sequences of DNA, RNA or Protein to identify
regions of similarity between the sequences.
 The procedure of comparing two (pair-wise
alignment) or more multiple sequences is to search
for a series of individual characters or patterns that
are in the same order in the sequences.
 There are two types of alignment: local and global.

4.  Global alignment is attempting to match as much of
the sequence as possible. The tool for Global
alignment is based on Needleman-Wunsch
algorithm.
 Local alignment is to try to find the regions with
highest density of matches. The tool for local
alignment is based on Smith-Waterman.

5.  Example:Global alignment vs Local alignment
 L G P S S K Q T G K G S - S R I W D N
| | | | | | | Global alignment
L N - I T K S A G K G A I M R L G D A
 - - - - - - - T G K G - - - - - - - -
| | | Local alignment
- - - - - - - A G K G - - - - - - - -

6.  In optimal alignment procedures, mostly
Needleman-Wunsch and Smith-Waterman
algorithms use scoring system. For nucleotide
sequence alignment, the scoring matrices used
are relatively simpler since the frequency of
mutation for all the bases are equal.
 Positive or higher value is assigned for a match
and a negative or a lower value is assigned for
mismatch.

7. •The Needleman–Wunsch algorithm performs a global alignment on two
sequences (called A and B here).
•It is commonly used in bioinformatics to align protein or nucleotide
sequences.
•The algorithm was proposed in 1970 by Saul Needleman and Christian
Wunsch.

9.  The dynamic programming matrix is defined
with three different steps.
 1.Initialization of the matrix with the scores possible.
2.Matrix filling with maximum scores.
3.Trace back the residues for appropriate alignment.

10.  This example assumes that there is gap penalty. First
row and first column of the matrix can be initially filled
with 0. If the gap score is assumed, the gap score can be
added to the previous cell of the row or column.

11.  step of the algorithm is matrix filling starting from the
upper left hand corner of the matrix. To find the maximum
score of each cell, it is required to know the neighbouring
scores (diagonal, left and right) of the current position.
 In terms of matrix positions, it is important to know
[M(i-1,j-1)+S(i,j),M(i,j-1)+w,M(i-1,j)+w]
 using the above equation and method, fill all the remaining
rows and columns. Place the back pointers to the cell from
where the maximum score is obtained, which are
predecessors of the current cell

13.  The final step in the algorithm is the trace back
for the best alignment.
 there may be two or more alignments possible
between the two example sequences.

14. By Tarun Shekhawat