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Evolutionary approach to construct robust codes for DNA-based data storage
DNA is a practical storage medium with high density, durability, and capacity to accommodate exponentially growing data volumes. A DNA sequence structure is a biocomputing problem that requires satisfying bioconstraints to design robust sequences. Existing evolutionary approaches to DNA sequences re...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10067891/ https://www.ncbi.nlm.nih.gov/pubmed/37021008 http://dx.doi.org/10.3389/fgene.2023.1158337 |
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author | Rasool, Abdur Jiang, Qingshan Wang, Yang Huang, Xiaoluo Qu, Qiang Dai, Junbiao |
author_facet | Rasool, Abdur Jiang, Qingshan Wang, Yang Huang, Xiaoluo Qu, Qiang Dai, Junbiao |
author_sort | Rasool, Abdur |
collection | PubMed |
description | DNA is a practical storage medium with high density, durability, and capacity to accommodate exponentially growing data volumes. A DNA sequence structure is a biocomputing problem that requires satisfying bioconstraints to design robust sequences. Existing evolutionary approaches to DNA sequences result in errors during the encoding process that reduces the lower bounds of DNA coding sets used for molecular hybridization. Additionally, the disordered DNA strand forms a secondary structure, which is susceptible to errors during decoding. This paper proposes a computational evolutionary approach based on a synergistic moth-flame optimizer by Levy flight and opposition-based learning mutation strategies to optimize these problems by constructing reverse-complement constraints. The MFOS aims to attain optimal global solutions with robust convergence and balanced search capabilities to improve DNA code lower bounds and coding rates for DNA storage. The ability of the MFOS to construct DNA coding sets is demonstrated through various experiments that use 19 state-of-the-art functions. Compared with the existing studies, the proposed approach with three different bioconstraints substantially improves the lower bounds of the DNA codes by 12–28% and significantly reduces errors. |
format | Online Article Text |
id | pubmed-10067891 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100678912023-04-04 Evolutionary approach to construct robust codes for DNA-based data storage Rasool, Abdur Jiang, Qingshan Wang, Yang Huang, Xiaoluo Qu, Qiang Dai, Junbiao Front Genet Genetics DNA is a practical storage medium with high density, durability, and capacity to accommodate exponentially growing data volumes. A DNA sequence structure is a biocomputing problem that requires satisfying bioconstraints to design robust sequences. Existing evolutionary approaches to DNA sequences result in errors during the encoding process that reduces the lower bounds of DNA coding sets used for molecular hybridization. Additionally, the disordered DNA strand forms a secondary structure, which is susceptible to errors during decoding. This paper proposes a computational evolutionary approach based on a synergistic moth-flame optimizer by Levy flight and opposition-based learning mutation strategies to optimize these problems by constructing reverse-complement constraints. The MFOS aims to attain optimal global solutions with robust convergence and balanced search capabilities to improve DNA code lower bounds and coding rates for DNA storage. The ability of the MFOS to construct DNA coding sets is demonstrated through various experiments that use 19 state-of-the-art functions. Compared with the existing studies, the proposed approach with three different bioconstraints substantially improves the lower bounds of the DNA codes by 12–28% and significantly reduces errors. Frontiers Media S.A. 2023-03-20 /pmc/articles/PMC10067891/ /pubmed/37021008 http://dx.doi.org/10.3389/fgene.2023.1158337 Text en Copyright © 2023 Rasool, Jiang, Wang, Huang, Qu and Dai. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Rasool, Abdur Jiang, Qingshan Wang, Yang Huang, Xiaoluo Qu, Qiang Dai, Junbiao Evolutionary approach to construct robust codes for DNA-based data storage |
title | Evolutionary approach to construct robust codes for DNA-based data storage |
title_full | Evolutionary approach to construct robust codes for DNA-based data storage |
title_fullStr | Evolutionary approach to construct robust codes for DNA-based data storage |
title_full_unstemmed | Evolutionary approach to construct robust codes for DNA-based data storage |
title_short | Evolutionary approach to construct robust codes for DNA-based data storage |
title_sort | evolutionary approach to construct robust codes for dna-based data storage |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10067891/ https://www.ncbi.nlm.nih.gov/pubmed/37021008 http://dx.doi.org/10.3389/fgene.2023.1158337 |
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