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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...

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Detalles Bibliográficos
Autores principales: Wang, Chengqi, Zhang, Michael Q., Zhang, Zhihua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2013
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.
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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
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