ArticleNature genetics2016
Quantitatively predictable control of Drosophila transcriptional enhancers in vivo with engineered transcription factors.
Article in Nature genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 42 citations in OpenAlex.
- Elastic enhancer network tunes equilibrium thermodynamics of liquid liquid phase separation in super enhancers.iScience · 2026Article
- Interpreting cis-regulatory interactions from large-scale deep neural networks.Nature genetics · 2024Article
- TF-High-Evolutionary: In Vivo Mutagenesis of Gene Regulatory Networks for the Study of the Genetics and Evolution of the Drosophila Regulatory Genome.Molecular biology and evolution · 2024Article
- Rapid response of fly populations to gene dosage across development and generations.Nature communications · 2024Article
- Mechanistic regulation of planarian shape during growth and degrowth.Development (Cambridge, England) · 2024Article
- InterpretingbioRxiv : the preprint server for biology · 2024Article
- Enhancer architecture and chromatin accessibility constrain phenotypic space during Drosophila development.Developmental cell · 2023Article
- Article
- DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers.Nature genetics · 2022Article
- Trading bits in the readout from a genetic network.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Deciphering enhancer sequence using thermodynamics-based models and convolutional neural networks.Nucleic acids research · 2021Article
- Transcriptional enhancers and their communication with gene promoters.Cellular and molecular life sciences : CMLS · 2021Review
- Seven myths of how transcription factors read the cis-regulatory code.Current opinion in systems biology · 2020Article
- Multimodal transcriptional control of pattern formation in embryonic development.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Reprogramming of regulatory network using expression uncovers sex-specific gene regulation in Drosophila.Nature communications · 2018Article
- Accurate and sensitive quantification of protein-DNA binding affinity.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Article
- Pervasive Behavioral Effects of MicroRNA Regulation inGenetics · 2017Article
- Is a super-enhancer greater than the sum of its parts?Nature genetics · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
Genes are regulated by transcription factors that bind to regions of genomic DNA called enhancers. Considerable effort is focused on identifying transcription factor binding sites, with the goal of predicting gene expression from DNA sequence. Despite this effort, general, predictive models of enhancer function are currently lacking. Here we combine quantitative models of enhancer function with manipulations using engineered transcription factors to examine the extent to which enhancer function can be controlled in a quantitatively predictable manner. Our models, which incorporate few free parameters, can accurately predict the contributions of ectopic transcription factor inputs. These models allow the predictable 'tuning' of enhancers, providing a framework for the quantitative control of enhancers with engineered transcription factors.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.