Optimizing Multiple Sequence Alignment using Multi-Objective Genetic Algorithms

Yadav, Sohan Kumar; Jha, Sudhanshu Kumar; Singh, Sudhakar; Dixit, Pratibha; Prakash, Shiv; Singh, Astha

doi:10.1109/dasa54658.2022.9765131

Cited by 3 publications

(1 citation statement)

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“…Dynamic programming effectively resolves Multiple Sequence Alignment (MSA) to obtain optimal alignments [19]. Moreover, dynamic programming employs a scoring function encompassing numerous domains.…”

Section: Classical Methodsmentioning

confidence: 99%

Bioinspired Algorithms for Multiple Sequence Alignment: A Systematic Review and Roadmap

Ibrahim,

Yusof,

Eisa

et al. 2024

Applied Sciences

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Multiple Sequence Alignment (MSA) plays a pivotal role in bioinformatics, facilitating various critical biological analyses, including the prediction of unknown protein structures and functions. While numerous methods are available for MSA, bioinspired algorithms stand out for their efficiency. Despite the growing research interest in addressing the MSA challenge, only a handful of comprehensive reviews have been undertaken in this domain. To bridge this gap, this study conducts a thorough analysis of bioinspired-based methods for MSA through a systematic literature review (SLR). By focusing on publications from 2010 to 2024, we aim to offer the most current insights into this field. Through rigorous eligibility criteria and quality standards, we identified 45 relevant papers for review. Our analysis predominantly concentrates on bioinspired-based techniques within the context of MSA. Notably, our findings highlight Genetic Algorithm and Memetic Optimization as the most commonly utilized algorithms for MSA. Furthermore, benchmark datasets such as BAliBASE and SABmark are frequently employed in evaluating MSA solutions. Structural-based methods emerge as the preferred approach for assessing MSA solutions, as revealed by our systematic literature review. Additionally, this study explores current trends, challenges, and unresolved issues in the realm of bioinspired algorithms for MSA, offering practitioners and researchers valuable insights and comprehensive understanding of the field.

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Section: Classical Methodsmentioning

confidence: 99%