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Integrative approaches based on genomic techniques in the functional studies on enhancers
With the development of sequencing technology and the dramatic drop in sequencing cost, the functions of noncoding genes are being characterized in a wide variety of fields (e.g. biomedicine). Enhancers are noncoding DNA elements with vital transcription regulation functions. Tens of thousands of en...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10694556/ https://www.ncbi.nlm.nih.gov/pubmed/38048082 http://dx.doi.org/10.1093/bib/bbad442 |
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author | Wang, Qilin Zhang, Junyou Liu, Zhaoshuo Duan, Yingying Li, Chunyan |
author_facet | Wang, Qilin Zhang, Junyou Liu, Zhaoshuo Duan, Yingying Li, Chunyan |
author_sort | Wang, Qilin |
collection | PubMed |
description | With the development of sequencing technology and the dramatic drop in sequencing cost, the functions of noncoding genes are being characterized in a wide variety of fields (e.g. biomedicine). Enhancers are noncoding DNA elements with vital transcription regulation functions. Tens of thousands of enhancers have been identified in the human genome; however, the location, function, target genes and regulatory mechanisms of most enhancers have not been elucidated thus far. As high-throughput sequencing techniques have leapt forwards, omics approaches have been extensively employed in enhancer research. Multidimensional genomic data integration enables the full exploration of the data and provides novel perspectives for screening, identification and characterization of the function and regulatory mechanisms of unknown enhancers. However, multidimensional genomic data are still difficult to integrate genome wide due to complex varieties, massive amounts, high rarity, etc. To facilitate the appropriate methods for studying enhancers with high efficacy, we delineate the principles, data processing modes and progress of various omics approaches to study enhancers and summarize the applications of traditional machine learning and deep learning in multi-omics integration in the enhancer field. In addition, the challenges encountered during the integration of multiple omics data are addressed. Overall, this review provides a comprehensive foundation for enhancer analysis. |
format | Online Article Text |
id | pubmed-10694556 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-106945562023-12-05 Integrative approaches based on genomic techniques in the functional studies on enhancers Wang, Qilin Zhang, Junyou Liu, Zhaoshuo Duan, Yingying Li, Chunyan Brief Bioinform Review With the development of sequencing technology and the dramatic drop in sequencing cost, the functions of noncoding genes are being characterized in a wide variety of fields (e.g. biomedicine). Enhancers are noncoding DNA elements with vital transcription regulation functions. Tens of thousands of enhancers have been identified in the human genome; however, the location, function, target genes and regulatory mechanisms of most enhancers have not been elucidated thus far. As high-throughput sequencing techniques have leapt forwards, omics approaches have been extensively employed in enhancer research. Multidimensional genomic data integration enables the full exploration of the data and provides novel perspectives for screening, identification and characterization of the function and regulatory mechanisms of unknown enhancers. However, multidimensional genomic data are still difficult to integrate genome wide due to complex varieties, massive amounts, high rarity, etc. To facilitate the appropriate methods for studying enhancers with high efficacy, we delineate the principles, data processing modes and progress of various omics approaches to study enhancers and summarize the applications of traditional machine learning and deep learning in multi-omics integration in the enhancer field. In addition, the challenges encountered during the integration of multiple omics data are addressed. Overall, this review provides a comprehensive foundation for enhancer analysis. Oxford University Press 2023-12-02 /pmc/articles/PMC10694556/ /pubmed/38048082 http://dx.doi.org/10.1093/bib/bbad442 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Review Wang, Qilin Zhang, Junyou Liu, Zhaoshuo Duan, Yingying Li, Chunyan Integrative approaches based on genomic techniques in the functional studies on enhancers |
title | Integrative approaches based on genomic techniques in the functional studies on enhancers |
title_full | Integrative approaches based on genomic techniques in the functional studies on enhancers |
title_fullStr | Integrative approaches based on genomic techniques in the functional studies on enhancers |
title_full_unstemmed | Integrative approaches based on genomic techniques in the functional studies on enhancers |
title_short | Integrative approaches based on genomic techniques in the functional studies on enhancers |
title_sort | integrative approaches based on genomic techniques in the functional studies on enhancers |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10694556/ https://www.ncbi.nlm.nih.gov/pubmed/38048082 http://dx.doi.org/10.1093/bib/bbad442 |
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