# Smith-Waterman algorithm in Python

July 13, 2018 | 5 Minute Read

B ecause I am currently working with Local Sequence Alignment (LSA) in a project I decided to use the Smith-Waterman algorithm to find a partially matching substring $b$ in a longer substring $a$. Since I am coding in Python, I was sure there were dozens of implementations already, ready to be used. I found a few indeed, namely here and here. However, the first implementation is incomplete, because it only includes calculating the scoring matrix and not the backtracing. I did not test the second implementation because it seemed overly complicated and apparently contained bugs (judging from the comments).

I therefore went for my own implementation. What I wanted was some easy to use function with a slim signature, something like

start, end = smith_waterman(string_a, string_b)

to find out where some version of string_b (including gaps and deletions) starts and ends within string_a.

## Step 1: Scoring matrix

To find the local alignment of $b$ with $a$ the Smith-Waterman calculates a scoring matrix first. The following code calculates this matrix for two strings a and b with linear gap costs. For performance reasons I went for an implementation with NumPy arrays. Values for match scores and gap costs can be changed. The default values correspond to the example from Wikipedia:

import itertools
import numpy as np

def matrix(a, b, match_score=3, gap_cost=2):
H = np.zeros((len(a) + 1, len(b) + 1), np.int)

for i, j in itertools.product(range(1, H.shape[0]), range(1, H.shape[1])):
match = H[i - 1, j - 1] + (match_score if a[i - 1] == b[j - 1] else - match_score)
delete = H[i - 1, j] - gap_cost
insert = H[i, j - 1] - gap_cost
H[i, j] = max(match, delete, insert, 0)
return H


## Step 2: Backtracing

The second step is backtracing from the calculated scoring matrix to calculate the optimal alignment of b with a. Since b will not simply be a substring of a in most cases, some version of b that includes gaps and deletions needs to be calculated. I had to juggle with the NumPy arrays a bit because numpy.argmax() will only find the first indexes of a maximum value inside an array and backtracing involves starting with the last occurrence of the maximum value.

def traceback(H, b, b_='', old_i=0):
# flip H to get index of **last** occurrence of H.max() with np.argmax()
H_flip = np.flip(np.flip(H, 0), 1)
i_, j_ = np.unravel_index(H_flip.argmax(), H_flip.shape)
i, j = np.subtract(H.shape, (i_ + 1, j_ + 1))  # (i, j) are **last** indexes of H.max()
if H[i, j] == 0:
return b_, j
b_ = b[j - 1] + '-' + b_ if old_i - i > 1 else b[j - 1] + b_
return traceback(H[0:i, 0:j], b, b_, i)



## Step 3: Calculating start- and end-index

Finally, the implementation for the top-level function simply performs these two steps by calling above functions. Since the LSA should not be case sensitive in my project, I normalized the strings beforehand by converting them to uppercase. Start and end index of string b in a are calculated from the result of the backtracing step.

def smith_waterman(a, b, match_score=3, gap_cost=2):
a, b = a.upper(), b.upper()
H = matrix(a, b, match_score, gap_cost)
b_, pos = traceback(H, b)
return pos, pos + len(b_)


## Usage and tests

To see if everything worked I wrote a few test with printouts:

    # prints correct scoring matrix from Wikipedia example
print(matrix('GGTTGACTA', 'TGTTACGG'))

a, b = 'ggttgacta', 'tgttacgg'
H = matrix(a, b)
print(traceback(H, b)) # ('gtt-ac', 1)

a, b = 'GGTTGACTA', 'TGTTACGG'
start, end = smith_waterman(a, b)
print(a[start:end])     # GTTGAC


## Resources

For the implementation I found the Wikipedia article and an Online-Tool to interactively perform the calculations very useful.