ArticleeLife2016
Inference of gene regulation functions from dynamic transcriptome data.
Article in eLife, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Integrating massive RNA-seq data to elucidate transcriptome dynamics in Drosophila melanogaster.Briefings in bioinformatics · 2023Article
- The cell-cycle transcriptional network generates and transmits a pulse of transcription once each cell cycle.Cell cycle (Georgetown, Tex.) · 2019Article
- Layers of regulation of cell-cycle gene expression in the budding yeast Saccharomyces cerevisiae.Molecular biology of the cell · 2018Article
- Reconciling conflicting models for global control of cell-cycle transcription.Cell cycle (Georgetown, Tex.) · 2017Article
- Connecting virulence pathways to cell-cycle progression in the fungal pathogen Cryptococcus neoformans.Current genetics · 2017Review
- Reverse engineering highlights potential principles of large gene regulatory network design and learning.NPJ systems biology and applications · 2017Article
- Elucidation of the sequential transcriptional activity in Escherichia coli using time-series RNA-seq data.Bioinformation · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To quantify gene regulation, a function is required that relates transcription factor binding to DNA (input) to the rate of mRNA synthesis from a target gene (output). Such a 'gene regulation function' (GRF) generally cannot be measured because the experimental titration of inputs and simultaneous readout of outputs is difficult. Here we show that GRFs may instead be inferred from natural changes in cellular gene expression, as exemplified for the cell cycle in the yeast
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.