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Recent Advances on the Machine Learning Methods in Identifying DNA Replication Origins in Eukaryotic Genomics
The initiate site of DNA replication is called origins of replication (ORI) which is regulated by a set of regulatory proteins and plays important roles in the basic biochemical process during cell growth and division in all living organisms. Therefore, the study of ORIs is essential for understandi...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6295579/ https://www.ncbi.nlm.nih.gov/pubmed/30619452 http://dx.doi.org/10.3389/fgene.2018.00613 |
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author | Dao, Fu-Ying Lv, Hao Wang, Fang Ding, Hui |
author_facet | Dao, Fu-Ying Lv, Hao Wang, Fang Ding, Hui |
author_sort | Dao, Fu-Ying |
collection | PubMed |
description | The initiate site of DNA replication is called origins of replication (ORI) which is regulated by a set of regulatory proteins and plays important roles in the basic biochemical process during cell growth and division in all living organisms. Therefore, the study of ORIs is essential for understanding the cell-division cycle and gene expression regulation so that scholars can develop a new strategy against genetic diseases by using the knowledge of DNA replication. Thus, the accurate identification of ORIs will provide key clues for DNA replication research and clinical medicine. Although, the conventional experiments could provide accurate results, they are time-consuming and cost ineffective. On the contrary, bioinformatics-based methods can overcome these shortcomings. Especially, with the emergence of DNA sequences in the post-genomic era, it is highly expected to develop high throughput tools to identify ORIs based on sequence information. In this review, we will summarize the current progress in computational prediction of eukaryotic ORIs including the collection of benchmark dataset, the application of machine learning-based techniques, the results obtained by these methods, and the construction of web servers. Finally, we gave the future perspectives on ORIs prediction. The review provided readers with a whole background of ORIs prediction based on machine learning methods, which will be helpful for researchers to study DNA replication in-depth and drug therapy of genetic defect. |
format | Online Article Text |
id | pubmed-6295579 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-62955792019-01-07 Recent Advances on the Machine Learning Methods in Identifying DNA Replication Origins in Eukaryotic Genomics Dao, Fu-Ying Lv, Hao Wang, Fang Ding, Hui Front Genet Genetics The initiate site of DNA replication is called origins of replication (ORI) which is regulated by a set of regulatory proteins and plays important roles in the basic biochemical process during cell growth and division in all living organisms. Therefore, the study of ORIs is essential for understanding the cell-division cycle and gene expression regulation so that scholars can develop a new strategy against genetic diseases by using the knowledge of DNA replication. Thus, the accurate identification of ORIs will provide key clues for DNA replication research and clinical medicine. Although, the conventional experiments could provide accurate results, they are time-consuming and cost ineffective. On the contrary, bioinformatics-based methods can overcome these shortcomings. Especially, with the emergence of DNA sequences in the post-genomic era, it is highly expected to develop high throughput tools to identify ORIs based on sequence information. In this review, we will summarize the current progress in computational prediction of eukaryotic ORIs including the collection of benchmark dataset, the application of machine learning-based techniques, the results obtained by these methods, and the construction of web servers. Finally, we gave the future perspectives on ORIs prediction. The review provided readers with a whole background of ORIs prediction based on machine learning methods, which will be helpful for researchers to study DNA replication in-depth and drug therapy of genetic defect. Frontiers Media S.A. 2018-12-10 /pmc/articles/PMC6295579/ /pubmed/30619452 http://dx.doi.org/10.3389/fgene.2018.00613 Text en Copyright © 2018 Dao, Lv, Wang and Ding. http://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 Dao, Fu-Ying Lv, Hao Wang, Fang Ding, Hui Recent Advances on the Machine Learning Methods in Identifying DNA Replication Origins in Eukaryotic Genomics |
title | Recent Advances on the Machine Learning Methods in Identifying DNA Replication Origins in Eukaryotic Genomics |
title_full | Recent Advances on the Machine Learning Methods in Identifying DNA Replication Origins in Eukaryotic Genomics |
title_fullStr | Recent Advances on the Machine Learning Methods in Identifying DNA Replication Origins in Eukaryotic Genomics |
title_full_unstemmed | Recent Advances on the Machine Learning Methods in Identifying DNA Replication Origins in Eukaryotic Genomics |
title_short | Recent Advances on the Machine Learning Methods in Identifying DNA Replication Origins in Eukaryotic Genomics |
title_sort | recent advances on the machine learning methods in identifying dna replication origins in eukaryotic genomics |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6295579/ https://www.ncbi.nlm.nih.gov/pubmed/30619452 http://dx.doi.org/10.3389/fgene.2018.00613 |
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