Bioinspired Algorithms for Multiple Sequence Alignment: A Systematic Review and Roadmap
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. Desp...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/6/2433 |
| _version_ | 1797242209185038336 |
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| author | Mohammed K. Ibrahim Umi Kalsom Yusof Taiseer Abdalla Elfadil Eisa Maged Nasser |
| author_facet | Mohammed K. Ibrahim Umi Kalsom Yusof Taiseer Abdalla Elfadil Eisa Maged Nasser |
| author_sort | Mohammed K. Ibrahim |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T18:35:35Z |
| format | Article |
| id | doaj.art-624d6e31e9b14530b49486887f3cae4b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-24T18:35:35Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-624d6e31e9b14530b49486887f3cae4b2024-03-27T13:19:41ZengMDPI AGApplied Sciences2076-34172024-03-01146243310.3390/app14062433Bioinspired Algorithms for Multiple Sequence Alignment: A Systematic Review and RoadmapMohammed K. Ibrahim0Umi Kalsom Yusof1Taiseer Abdalla Elfadil Eisa2Maged Nasser3School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Penang, MalaysiaSchool of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Penang, MalaysiaDepartment of Information Systems-Girls Section, King Khalid University, Mahayil 62529, Saudi ArabiaComputer & Information Sciences Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, MalaysiaMultiple 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.https://www.mdpi.com/2076-3417/14/6/2433bioinformaticsbioinspired algorithmsMultiple Sequence Alignmentsystematic review |
| spellingShingle | Mohammed K. Ibrahim Umi Kalsom Yusof Taiseer Abdalla Elfadil Eisa Maged Nasser Bioinspired Algorithms for Multiple Sequence Alignment: A Systematic Review and Roadmap Applied Sciences bioinformatics bioinspired algorithms Multiple Sequence Alignment systematic review |
| title | Bioinspired Algorithms for Multiple Sequence Alignment: A Systematic Review and Roadmap |
| title_full | Bioinspired Algorithms for Multiple Sequence Alignment: A Systematic Review and Roadmap |
| title_fullStr | Bioinspired Algorithms for Multiple Sequence Alignment: A Systematic Review and Roadmap |
| title_full_unstemmed | Bioinspired Algorithms for Multiple Sequence Alignment: A Systematic Review and Roadmap |
| title_short | Bioinspired Algorithms for Multiple Sequence Alignment: A Systematic Review and Roadmap |
| title_sort | bioinspired algorithms for multiple sequence alignment a systematic review and roadmap |
| topic | bioinformatics bioinspired algorithms Multiple Sequence Alignment systematic review |
| url | https://www.mdpi.com/2076-3417/14/6/2433 |
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