Hamming Distance

Foundational Python Foundations Hamming Distance Point Mutation Comparison

Significance:

The Hamming distance between two equal-length sequences counts point mutations, which is the
simplest possible molecular clock: more substitutions implies more evolutionary time since
divergence. It underpins SNP calling and read-error estimation, and it's the base case that
edit-distance and alignment algorithms generalise.

Statement

Given two DNA strings of equal length, count the positions at which their symbols differ.
This is the Hamming distance.

The two strings arrive on two separate lines. Print a single integer on one line.

Input — read from standard input

Variable	Type	Description
`s` line 1	str	The first DNA string 1 <= len(s) <= 1000, uppercase A, C, G, T only
`t` line 2	str	The second DNA string len(t) == len(s)

These variables are already read for you in the starter code on the right.

Output

int the number of positions at which s and t differ

Sample Cases

Sample 1

Input

GAGCCTACTAACGGGAT
CATCGTAATGACGGCCT

Expected Output

Seven of the seventeen positions carry different bases.

Sample 2

Input

ACGT
ACGT

Expected Output

Identical strings have distance 0.

Submit also runs your code against 4 hidden test cases. Hidden inputs are never shown — if one fails you'll get its number and a description of the mismatch, not the data.

Constraints

1 <= length(s) <= 1000
length(t) == length(s) — the strings are always the same length
Uppercase A, C, G, T only