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Computational Identification of Active Enhancers in Model Organisms
As a class of cis-regulatory elements, enhancers were first identified as the genomic regions that are able to markedly increase the transcription of genes nearly 30 years ago. Enhancers can regulate gene expression in a cell-type specific and developmental stage specific manner. Although experiment...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4357786/ https://www.ncbi.nlm.nih.gov/pubmed/23685394 http://dx.doi.org/10.1016/j.gpb.2013.04.002 |
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author | Wang, Chengqi Zhang, Michael Q. Zhang, Zhihua |
author_facet | Wang, Chengqi Zhang, Michael Q. Zhang, Zhihua |
author_sort | Wang, Chengqi |
collection | PubMed |
description | As a class of cis-regulatory elements, enhancers were first identified as the genomic regions that are able to markedly increase the transcription of genes nearly 30 years ago. Enhancers can regulate gene expression in a cell-type specific and developmental stage specific manner. Although experimental technologies have been developed to identify enhancers genome-wide, the design principle of the regulatory elements and the way they rewire the transcriptional regulatory network tempo-spatially are far from clear. At present, developing predictive methods for enhancers, particularly for the cell-type specific activity of enhancers, is central to computational biology. In this review, we survey the current computational approaches for active enhancer prediction and discuss future directions. |
format | Online Article Text |
id | pubmed-4357786 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-43577862015-05-06 Computational Identification of Active Enhancers in Model Organisms Wang, Chengqi Zhang, Michael Q. Zhang, Zhihua Genomics Proteomics Bioinformatics Review As a class of cis-regulatory elements, enhancers were first identified as the genomic regions that are able to markedly increase the transcription of genes nearly 30 years ago. Enhancers can regulate gene expression in a cell-type specific and developmental stage specific manner. Although experimental technologies have been developed to identify enhancers genome-wide, the design principle of the regulatory elements and the way they rewire the transcriptional regulatory network tempo-spatially are far from clear. At present, developing predictive methods for enhancers, particularly for the cell-type specific activity of enhancers, is central to computational biology. In this review, we survey the current computational approaches for active enhancer prediction and discuss future directions. Elsevier 2013-06 2013-05-17 /pmc/articles/PMC4357786/ /pubmed/23685394 http://dx.doi.org/10.1016/j.gpb.2013.04.002 Text en © 2013 Beijing Institute of Genomics, Chinese Academy of Sciences and Genetics Society of China. Production and hosting by Elsevier B.V. All rights reserved. http://creativecommons.org/licenses/by-nc-sa/3.0/ This is an open access article under the CC BY-NC-SA license (http://creativecommons.org/licenses/by-nc-sa/3.0/). |
spellingShingle | Review Wang, Chengqi Zhang, Michael Q. Zhang, Zhihua Computational Identification of Active Enhancers in Model Organisms |
title | Computational Identification of Active Enhancers in Model Organisms |
title_full | Computational Identification of Active Enhancers in Model Organisms |
title_fullStr | Computational Identification of Active Enhancers in Model Organisms |
title_full_unstemmed | Computational Identification of Active Enhancers in Model Organisms |
title_short | Computational Identification of Active Enhancers in Model Organisms |
title_sort | computational identification of active enhancers in model organisms |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4357786/ https://www.ncbi.nlm.nih.gov/pubmed/23685394 http://dx.doi.org/10.1016/j.gpb.2013.04.002 |
work_keys_str_mv | AT wangchengqi computationalidentificationofactiveenhancersinmodelorganisms AT zhangmichaelq computationalidentificationofactiveenhancersinmodelorganisms AT zhangzhihua computationalidentificationofactiveenhancersinmodelorganisms |