Developing new genetic algorithm based on integer programming for multiple sequence alignment

Molecular biology advances in the past few decades have contributed to the rapid increase in genome sequencing of various organisms; sequence alignment is usually considered the first step in understanding a sequence's molecular function. Understanding such a function is made possible by aligning an unknown sequence with known sequences based on evolution. An optimal alignment adjusts two or more sequences in a way that it could compare the maximum number of identical or similar residues. The two sequence alignments types are Pairwise Sequence Alignment (PSA) and Multiple Sequence Alignment (MSA). The MSA enjoys a higher advantage than PSA since it predicts the similarity in a family of similar sequences, providing more biological data. Moreover, while the dynamic programming (DP) technique is used in PSA to provide the optimal method, it will lead to more complexity if used in MSA. So, the MSA mainly uses heuristic and approximation methods. Such methods include progressive alignment, iterative alignment, Hidden Markov Model (HMM), and Metaheuristic algorithms. This paper presents a genetic algorithm and chromosome design to solve a mathematical model for MSA. This model uses a basis for an optimal solution in different ways, and an X-mediated matrix with binary elements is used to model the sequence. Then, the model is implemented using the Genetic Algorithm method on the web, and the final results indicate the success of GA for the MSA approach.