ArticleeLife2016
Quantitative perturbation-based analysis of gene expression predicts enhancer activity in early Drosophila embryo.
Article in eLife, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Enhancer binding kinetics explain transcription factor hub formation.Science advances · 2026Article
- UnlockingNAR genomics and bioinformatics · 2026Article
- Characterizing the regulatory logic of transcriptional control at the DNA sequence level by ensembles of thermodynamic models.Bioinformatics (Oxford, England) · 2025Article
- The proximal enhancer of the snail gene mediates negative autoregulatory feedback in Drosophila melanogaster.Genetics · 2025Article
- Experimental approaches to investigate biophysical interactions between homeodomain transcription factors and DNA.Biochimica et biophysica acta. Gene regulatory mechanisms · 2025Review
- Active learning of enhancers and silencers in the developing neural retina.Cell systems · 2025Article
- ChromBPNet: bias factorized, base-resolution deep learning models of chromatin accessibility reveal cis-regulatory sequence syntax, transcription factor footprints and regulatory variants.bioRxiv : the preprint server for biology · 2025Article
- Two coacting shadow enhancers regulate twin of eyeless expression during early Drosophila development.Genetics · 2025Article
- Optogenetic dissection of transcriptional repression in a multicellular organism.Nature communications · 2024Article
- Active learning of enhancer and silencer regulatory grammar in photoreceptors.bioRxiv : the preprint server for biology · 2023Article
- Minimal synthetic enhancers reveal control of the probability of transcriptional engagement and its timing by a morphogen gradient.Cell systems · 2023Article
- Article
- Concentration-dependent transcriptional switching through a collective action of cis-elements.Science advances · 2022Article
- DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers.Nature genetics · 2022Article
- Quantitative-enhancer-FACS-seq (QeFS) reveals epistatic interactions among motifs within transcriptional enhancers in developing Drosophila tissue.Genome biology · 2021Article
- Fitting thermodynamic-based models: Incorporating parameter sensitivity improves the performance of an evolutionary algorithm.Mathematical biosciences · 2021Article
- Quantifying the regulatory role of individual transcription factors in Escherichia coli.Cell reports · 2021Article
- Deciphering enhancer sequence using thermodynamics-based models and convolutional neural networks.Nucleic acids research · 2021Article
- Enhancer grammar in development, evolution, and disease: dependencies and interplay.Developmental cell · 2021Review
- Base-resolution models of transcription-factor binding reveal soft motif syntax.Nature genetics · 2021Article
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Authors and funding
5 authors.
Funding
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.