A simple version of the Needleman-Wunsch algorithm in Python.

needleman-wunsch.py

#!/usr/bin/env python
"""
The Needleman-Wunsch Algorithm
==============================

This is a dynamic programming algorithm for finding the optimal alignment of
two strings.

Example
-------

    >>> x = "GATTACA"
    >>> y = "GCATGCU"
    >>> print(nw(x, y))
    G-ATTACA
    GCA-TGCU


LICENSE

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"""

import numpy as np

def nw(x, y, match = 1, mismatch = 1, gap = 1):
    nx = len(x)
    ny = len(y)
    # Optimal score at each possible pair of characters.
    F = np.zeros((nx + 1, ny + 1))
    F[:,0] = np.linspace(0, -nx * gap, nx + 1)
    F[0,:] = np.linspace(0, -ny * gap, ny + 1)
    # Pointers to trace through an optimal aligment.
    P = np.zeros((nx + 1, ny + 1))
    P[:,0] = 3
    P[0,:] = 4
    # Temporary scores.
    t = np.zeros(3)
    for i in range(nx):
        for j in range(ny):
            if x[i] == y[j]:
                t[0] = F[i,j] + match
            else:
                t[0] = F[i,j] - mismatch
            t[1] = F[i,j+1] - gap
            t[2] = F[i+1,j] - gap
            tmax = np.max(t)
            F[i+1,j+1] = tmax
            if t[0] == tmax:
                P[i+1,j+1] += 2
            if t[1] == tmax:
                P[i+1,j+1] += 3
            if t[2] == tmax:
                P[i+1,j+1] += 4
    # Trace through an optimal alignment.
    i = nx
    j = ny
    rx = []
    ry = []
    while i > 0 or j > 0:
        if P[i,j] in [2, 5, 6, 9]:
            rx.append(x[i-1])
            ry.append(y[j-1])
            i -= 1
            j -= 1
        elif P[i,j] in [3, 5, 7, 9]:
            rx.append(x[i-1])
            ry.append('-')
            i -= 1
        elif P[i,j] in [4, 6, 7, 9]:
            rx.append('-')
            ry.append(y[j-1])
            j -= 1
    # Reverse the strings.
    rx = ''.join(rx)[::-1]
    ry = ''.join(ry)[::-1]
    return '\n'.join([rx, ry])

x = "GATTACA"
y = "GCATGCU"
print(nw(x, y))
# G-ATTACA
# GCA-TGCU

np.random.seed(42)
x = np.random.choice(['A', 'T', 'G', 'C'], 50)
y = np.random.choice(['A', 'T', 'G', 'C'], 50)

print(nw(x, y, gap = 0))
# ----G-C--AGGCAAGTGGGGCACCCGTATCCT-T-T-C-C-AACTTACAAGGGT-C-CC-----CGT-T
# GTGCGCCAGAGG-AAGT----CA--C-T-T--TATATCCGCG--C--AC---GGTACTCCTTTTTC-TA-

print(nw(x, y, gap = 1))
# GCAG-GCAAGTGG--GGCAC-CCGTATCCTTTC-CAAC-TTACAAGGGTCC-CCGT-T-
# G-TGCGCCAGAGGAAGTCACTTTATATCC--GCGC-ACGGTAC-----TCCTTTTTCTA

print(nw(x, y, gap = 2))
# GCAGGCAAGTGG--GGCAC-CCGTATCCTTTCCAACTTACAAGGGTCCCCGTT
# GTGCGCCAGAGGAAGTCACTTTATATCC-GCGCACGGTAC-TCCTTTTTC-TA

@JFOZ1010

Mantenemos la puntuación por coincidencia, no coincidencia y gap. La elección en cada posición depende de la mejor puntuación posible para la alineación global.

Podemos aumentar la puntuación no coincidente para evitar (A - T):

In [2]: print(nw(x, y, gap = 1, mismatch = 1))
GCAG-GCAAGTGG--GGCAC-CCGTATCCTTTC-CAAC-TTACAAGGGTCC-CCGT-T-
G-TGCGCCAGAGGAAGTCACTTTATATCC--GCGC-ACGGTAC-----TCCTTTTTCTA

In [3]: print(nw(x, y, gap = 1, mismatch = 2))
--GCAGGCA-AGTG-GGGCACCCGTATCCT-T-TCCAACTTACAAGGGT-C-CC-----CGTT
GTGC-GCCAGAG-GAAGTCA--C-T-T--TATATCC-GC--GC-ACGGTACTCCTTTTTC-TA

In [4]: print(nw(x, y, gap = 1, mismatch = 3))
----G-C--AGGCAAGTGGGGCACCCGTATCCT-T-T-C-C-AACTTACAAGGGT-C-CC-----CGT-T
GTGCGCCAGAGG-AAGT----CA--C-T-T--TATATCCGCG--C--AC---GGTACTCCTTTTTC-TA-

In [5]: print(nw(x, y, gap = 1, mismatch = 4))
----G-C--AGGCAAGTGGGGCACCCGTATCCT-T-T-C-C-AACTTACAAGGGT-C-CC-----CGT-T
GTGCGCCAGAGG-AAGT----CA--C-T-T--TATATCCGCG--C--AC---GGTACTCCTTTTTC-TA-

---

This R code is slow, but it might be useful for visualizing some examples of sequence alignments: https://gist.github.com/slowkow/508393

The score matrix F is represented with numbers.

The pointer matrix P is represented with arrows pointing up, left, or up-left to indicate gaps (up or left) or match/mismatch (up-left).

can you please tell me the changes i need to do in this code for finding the most optimal alignment cosidering Smith Watterman algorithm for local alignment ??

@ayush-mourya There are lot of resources online that you might want to look at. Don't give up, keep reading!

Here is one resource that seems relevant to your question: https://open.oregonstate.education/appliedbioinformatics/chapter/chapter-3/

(University websites are always a great starting point, try searching for queries like smith-waterman site:edu to get results from universities.)

In the Needleman-Wunsch (global alignment) algorithm, we start from the bottom-right corner of the matrix, and we move upward and to the left, stopping in the top-left corner of the matrix. This ensures that we globally align all of the bases from the two sequences.

In the Smith-Waterman (local alignment) algorithm, we do not always start from the bottom-right corner of the matrix. Instead, we choose the maximum value from the bottom row or the right-most column. From that position, we proceed upward and to the left, but we can stop before we reach the top-left corner. This means we are interested in the local alignment of a subset of the first and second sequences, not the global alignment of the entirety of the two sequences.

I hope that helps! Good luck with your learning.