New Sequence Alignment Algorithm using AI Rules and Dynamic Seeds

Abstract

DNA sequence alignment is important today as it is usually the first step in finding gene mutation, evolutionary similarities, protein structure, drug development and cancer treatment. Covid-19 is one recent example. There are many sequencing algorithms developed over the past decades but the sequence alignment using expert systems is quite new. To find DNA sequence alignment, dynamic programming was used initially. Later faster algorithms used small DNA sequence length of fixed size to find regions of similarity, and then build the final alignment using these regions. Such systems were not sensitive but were fast. To improve the sensitivity, we propose a new algorithm which is based on finding maximal matches between two sequences, find seeds between them, employ rules to find more seeds of varying length, and then employ a new stitching algorithm, and weighted seeds to solve the problem