Enhanced Genetic Method for Optimizing Multiple Sequence Alignment

Mohammed K. Ibrahim (), Umi Kalsom Yusof, Taiseer Abdalla Elfadil Eisa and Maged Nasser
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Mohammed K. Ibrahim: School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Penang, Malaysia
Umi Kalsom Yusof: School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Penang, Malaysia
Taiseer Abdalla Elfadil Eisa: Department of Information Systems-Girls Section, King Khalid University, Mahayil 62529, Saudi Arabia
Maged Nasser: Computer & Information Sciences Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia

Mathematics, 2023, vol. 11, issue 22, 1-23

Abstract: In the realm of bioinformatics, Multiple Sequence Alignment (MSA) is a pivotal technique used to optimize the alignment of multiple biological sequences, guided by specific scoring criteria. Existing approaches addressing the MSA challenge tend to specialize in distinct biological features, leading to variability in alignment outcomes for the same set of sequences. Consequently, this paper proposes an enhanced evolutionary-based approach that simplifies the sequence alignment problem without considering the sequences in the non-dominated solution. Our method employs a multi-objective optimization technique that uniquely excludes non-dominated solution sets, effectively mitigating computational complexities. Utilizing the Sum of Pairs and the Total Conserved Column as primary objective functions, our approach offers a novel perspective. We adopt an integer coding approach to enhance the computational efficiency, representing chromosomes with sets of integers during the alignment process. Using the SABmark and BAliBASE datasets, extensive experimentation is conducted to compare our method with existing ones. The results affirm the superior solution quality achieved by our approach compared to its predecessors. Furthermore, via the Wilcoxon signed-rank test, a statistical analysis underscores the statistical significance of our model’s improvement ( p < 0.05). This comprehensive approach holds promise for advancing Multiple Sequence Alignment in bioinformatics.

Keywords: Multiple Sequence Alignment; evolutionary algorithm; genetic algorithm; bioinformatics; optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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