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Quantitative perturbation-based analysis of gene expression predicts enhancer activity in early Drosophila embryo
Enhancers constitute one of the major components of regulatory machinery of metazoans. Although several genome-wide studies have focused on finding and locating enhancers in the genomes, the fundamental principles governing their internal architecture and cis-regulatory grammar remain elusive. Here,...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4859806/ https://www.ncbi.nlm.nih.gov/pubmed/27152947 http://dx.doi.org/10.7554/eLife.08445 |
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author | Sayal, Rupinder Dresch, Jacqueline M Pushel, Irina Taylor, Benjamin R Arnosti, David N |
author_facet | Sayal, Rupinder Dresch, Jacqueline M Pushel, Irina Taylor, Benjamin R Arnosti, David N |
author_sort | Sayal, Rupinder |
collection | PubMed |
description | Enhancers constitute one of the major components of regulatory machinery of metazoans. Although several genome-wide studies have focused on finding and locating enhancers in the genomes, the fundamental principles governing their internal architecture and cis-regulatory grammar remain elusive. Here, we describe an extensive, quantitative perturbation analysis targeting the dorsal-ventral patterning gene regulatory network (GRN) controlled by Drosophila NF-κB homolog Dorsal. To understand transcription factor interactions on enhancers, we employed an ensemble of mathematical models, testing effects of cooperativity, repression, and factor potency. Models trained on the dataset correctly predict activity of evolutionarily divergent regulatory regions, providing insights into spatial relationships between repressor and activator binding sites. Importantly, the collective predictions of sets of models were effective at novel enhancer identification and characterization. Our study demonstrates how experimental dataset and modeling can be effectively combined to provide quantitative insights into cis-regulatory information on a genome-wide scale. DOI: http://dx.doi.org/10.7554/eLife.08445.001 |
format | Online Article Text |
id | pubmed-4859806 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-48598062016-05-10 Quantitative perturbation-based analysis of gene expression predicts enhancer activity in early Drosophila embryo Sayal, Rupinder Dresch, Jacqueline M Pushel, Irina Taylor, Benjamin R Arnosti, David N eLife Computational and Systems Biology Enhancers constitute one of the major components of regulatory machinery of metazoans. Although several genome-wide studies have focused on finding and locating enhancers in the genomes, the fundamental principles governing their internal architecture and cis-regulatory grammar remain elusive. Here, we describe an extensive, quantitative perturbation analysis targeting the dorsal-ventral patterning gene regulatory network (GRN) controlled by Drosophila NF-κB homolog Dorsal. To understand transcription factor interactions on enhancers, we employed an ensemble of mathematical models, testing effects of cooperativity, repression, and factor potency. Models trained on the dataset correctly predict activity of evolutionarily divergent regulatory regions, providing insights into spatial relationships between repressor and activator binding sites. Importantly, the collective predictions of sets of models were effective at novel enhancer identification and characterization. Our study demonstrates how experimental dataset and modeling can be effectively combined to provide quantitative insights into cis-regulatory information on a genome-wide scale. DOI: http://dx.doi.org/10.7554/eLife.08445.001 eLife Sciences Publications, Ltd 2016-05-06 /pmc/articles/PMC4859806/ /pubmed/27152947 http://dx.doi.org/10.7554/eLife.08445 Text en © 2016, Sayal et al http://creativecommons.org/licenses/by/4.0/ This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Computational and Systems Biology Sayal, Rupinder Dresch, Jacqueline M Pushel, Irina Taylor, Benjamin R Arnosti, David N Quantitative perturbation-based analysis of gene expression predicts enhancer activity in early Drosophila embryo |
title | Quantitative perturbation-based analysis of gene expression predicts enhancer activity in early Drosophila embryo |
title_full | Quantitative perturbation-based analysis of gene expression predicts enhancer activity in early Drosophila embryo |
title_fullStr | Quantitative perturbation-based analysis of gene expression predicts enhancer activity in early Drosophila embryo |
title_full_unstemmed | Quantitative perturbation-based analysis of gene expression predicts enhancer activity in early Drosophila embryo |
title_short | Quantitative perturbation-based analysis of gene expression predicts enhancer activity in early Drosophila embryo |
title_sort | quantitative perturbation-based analysis of gene expression predicts enhancer activity in early drosophila embryo |
topic | Computational and Systems Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4859806/ https://www.ncbi.nlm.nih.gov/pubmed/27152947 http://dx.doi.org/10.7554/eLife.08445 |
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