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Lightweight Pattern Matching Method for DNA Sequencing in Internet of Medical Things

An area of medical science, that is, gaining prominence, is DNA sequencing. Genetic mutations responsible for the disease have been detected using DNA sequencing. The research is focusing on pattern identification methodologies for dealing with DNA-sequencing problems relating to various application...

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Autores principales: Rexie, J. A. M., Raimond, Kumudha, Murugaaboopathy, Mythily, Brindha, D., Mulugeta, Henock
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9477578/
https://www.ncbi.nlm.nih.gov/pubmed/36120669
http://dx.doi.org/10.1155/2022/6980335
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author Rexie, J. A. M.
Raimond, Kumudha
Murugaaboopathy, Mythily
Brindha, D.
Mulugeta, Henock
author_facet Rexie, J. A. M.
Raimond, Kumudha
Murugaaboopathy, Mythily
Brindha, D.
Mulugeta, Henock
author_sort Rexie, J. A. M.
collection PubMed
description An area of medical science, that is, gaining prominence, is DNA sequencing. Genetic mutations responsible for the disease have been detected using DNA sequencing. The research is focusing on pattern identification methodologies for dealing with DNA-sequencing problems relating to various applications. A few examples of such problems are alignment and assembly of short reads from next generation sequencing (NGS), comparing DNA sequences, and determining the frequency of a pattern in a sequence. The approximate matching of DNA sequences is also well suited for many applications equivalent to the exact matching of the sequence since the DNA sequences are often subject to mutation. Consequently, recognizing pattern similarity becomes necessary. Furthermore, it can also be used in virtually every application that calls for pattern matching, for example, spell-checking, spam filtering, and search engines. According to the traditional approach, finding a similar pattern in the case where the sequence length is l(s) and the pattern length is l(p) occurs in O (l(s)∗l(p)). This heavy processing is caused by comparing every character of the sequence repeatedly with the pattern. The research intended to reduce the time complexity of the pattern matching by introducing an approach named “optimized pattern similarity identification” (OPSI). This methodology constructs a table, entitled “shift beyond for avoiding redundant comparison” (SBARC), to bypass the characters in the texts that are already compared with the pattern. The table pertains to the information about the character distance to be skipped in the matching. OPSI discovers at most spots of similar patterns occur in the sequence (by ignoring è mismatches). The experiment resulted in the time complexity identified as O (l(s). è). In comparison to the size of the pattern, the allowed number of mismatches will be much smaller. Aspects such as scalability, generalizability, and performance of the OPSI algorithm are discussed. In comparison with the hamming distance-based approximate pattern matching algorithm, the proposed algorithm is found to be 69% more efficient.
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spelling pubmed-94775782022-09-16 Lightweight Pattern Matching Method for DNA Sequencing in Internet of Medical Things Rexie, J. A. M. Raimond, Kumudha Murugaaboopathy, Mythily Brindha, D. Mulugeta, Henock Comput Intell Neurosci Research Article An area of medical science, that is, gaining prominence, is DNA sequencing. Genetic mutations responsible for the disease have been detected using DNA sequencing. The research is focusing on pattern identification methodologies for dealing with DNA-sequencing problems relating to various applications. A few examples of such problems are alignment and assembly of short reads from next generation sequencing (NGS), comparing DNA sequences, and determining the frequency of a pattern in a sequence. The approximate matching of DNA sequences is also well suited for many applications equivalent to the exact matching of the sequence since the DNA sequences are often subject to mutation. Consequently, recognizing pattern similarity becomes necessary. Furthermore, it can also be used in virtually every application that calls for pattern matching, for example, spell-checking, spam filtering, and search engines. According to the traditional approach, finding a similar pattern in the case where the sequence length is l(s) and the pattern length is l(p) occurs in O (l(s)∗l(p)). This heavy processing is caused by comparing every character of the sequence repeatedly with the pattern. The research intended to reduce the time complexity of the pattern matching by introducing an approach named “optimized pattern similarity identification” (OPSI). This methodology constructs a table, entitled “shift beyond for avoiding redundant comparison” (SBARC), to bypass the characters in the texts that are already compared with the pattern. The table pertains to the information about the character distance to be skipped in the matching. OPSI discovers at most spots of similar patterns occur in the sequence (by ignoring è mismatches). The experiment resulted in the time complexity identified as O (l(s). è). In comparison to the size of the pattern, the allowed number of mismatches will be much smaller. Aspects such as scalability, generalizability, and performance of the OPSI algorithm are discussed. In comparison with the hamming distance-based approximate pattern matching algorithm, the proposed algorithm is found to be 69% more efficient. Hindawi 2022-09-08 /pmc/articles/PMC9477578/ /pubmed/36120669 http://dx.doi.org/10.1155/2022/6980335 Text en Copyright © 2022 J. A. M. Rexie et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Rexie, J. A. M.
Raimond, Kumudha
Murugaaboopathy, Mythily
Brindha, D.
Mulugeta, Henock
Lightweight Pattern Matching Method for DNA Sequencing in Internet of Medical Things
title Lightweight Pattern Matching Method for DNA Sequencing in Internet of Medical Things
title_full Lightweight Pattern Matching Method for DNA Sequencing in Internet of Medical Things
title_fullStr Lightweight Pattern Matching Method for DNA Sequencing in Internet of Medical Things
title_full_unstemmed Lightweight Pattern Matching Method for DNA Sequencing in Internet of Medical Things
title_short Lightweight Pattern Matching Method for DNA Sequencing in Internet of Medical Things
title_sort lightweight pattern matching method for dna sequencing in internet of medical things
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9477578/
https://www.ncbi.nlm.nih.gov/pubmed/36120669
http://dx.doi.org/10.1155/2022/6980335
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