DNA sequence alignement in native Python no biopython

Certainly! DNA sequence alignment is a crucial task in bioinformatics, and while Biopython provides comprehensive tools for this, it's also educational to understand the fundamentals and implement a simple sequence alignment algorithm in native Python. In this tutorial, I'll guide you through the process of pairwise sequence alignment using a basic dynamic programming approach.
Sequence alignment is the process of arranging DNA, RNA, or protein sequences to identify similarities and differences. One popular algorithm for pairwise sequence alignment is the Needleman-Wunsch algorithm, which uses dynamic programming to find the optimal alignment.
First, we need to define a scoring system. We'll use a simple scoring system: +1 for a match, -1 for a mismatch, and -2 for a gap.
Next, we initialize a matrix to store the alignment scores. The matrix dimensions will be the lengths of the two sequences plus one.
Now, we fill the matrix using the scoring system and dynamic programming.
After filling the matrix, we perform a traceback to find the optimal alignment.
Now, let's put everything together in a function for sequence alignment.
Let's test the implementation with two DNA sequences.
This should output the aligned sequences.
Congratulations! You've implemented a simple DNA sequence alignment algorithm in native Python. While this approach is basic, it provides a foundation for understanding more advanced algorithms used in bioinformatics.