Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Qilin, Zhang, Junyou, Liu, Zhaoshuo, Duan, Yingying, Li, Chunyan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
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
_version_ 1785153405419782144
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
work_keys_str_mv AT wangqilin integrativeapproachesbasedongenomictechniquesinthefunctionalstudiesonenhancers
AT zhangjunyou integrativeapproachesbasedongenomictechniquesinthefunctionalstudiesonenhancers
AT liuzhaoshuo integrativeapproachesbasedongenomictechniquesinthefunctionalstudiesonenhancers
AT duanyingying integrativeapproachesbasedongenomictechniquesinthefunctionalstudiesonenhancers
AT lichunyan integrativeapproachesbasedongenomictechniquesinthefunctionalstudiesonenhancers