ArticleNucleic acids research2010
Comprehensive reanalysis of transcription factor knockout expression data in Saccharomyces cerevisiae reveals many new targets.
Article in Nucleic acids research, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 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
65 citing papers in PubMed.
- Transcriptomic shift in ethanol and amino acid metabolic genes regulated by Med15 during alcoholic fermentation.Microbial genomics · 2026Article
- Replication associated nuclear DNA mismatch repair across kingdoms.Biochemical Society transactions · 2026Review
- Signing protein-protein interaction networks.Bioinformatics (Oxford, England) · 2026Article
- Decoding yeast transcriptional regulation via a data-and mechanism-driven distributed large-scale network model.Synthetic and systems biotechnology · 2025Article
- A high copy suppressor screen identifies factors enhancing the allotopic production of subunit II of cytochrome c oxidase.G3 (Bethesda, Md.) · 2025Article
- Transcriptomic and proteomic effects of gene deletion are not evolutionarily conserved.Genome research · 2025Article
- Bayesian inference of sample-specific coexpression networks.Genome research · 2024Article
- Spontaneous Attenuation of Alcoholic Fermentation via the Dysfunction of Cyc8p inInternational journal of molecular sciences · 2023Article
- Bayesian Optimized sample-specific Networks Obtained By Omics data (BONOBO).bioRxiv : the preprint server for biology · 2023Article
- Towards universal synthetic heterotrophy using a metabolic coordinator.Metabolic engineering · 2023Article
- Inferring gene regulatory networks using transcriptional profiles as dynamical attractors.PLoS computational biology · 2023Article
- SIR telomere silencing depends on nuclear envelope lipids and modulates sensitivity to a lysolipid.The Journal of cell biology · 2023Article
- NetREX-CF integrates incomplete transcription factor data with gene expression to reconstruct gene regulatory networks.Communications biology · 2022Article
- Systematic inference of indirect transcriptional regulation by protein kinases and phosphatases.PLoS computational biology · 2022Article
- Adaptive Response ofJournal of fungi (Basel, Switzerland) · 2022Article
- Set2 histone methyltransferase regulates transcription coupled-nucleotide excision repair in yeast.PLoS genetics · 2022Article
- Article
- Gene by Environment Interactions reveal new regulatory aspects of signaling network plasticity.PLoS genetics · 2022Article
- Capturing hidden regulation based on noise change of gene expression level from single cell RNA-seq in yeast.Scientific reports · 2021Article
- Plastic Rewiring of Sef1 Transcriptional Networks and the Potential of Nonfunctional Transcription Factor Binding in Facilitating Adaptive Evolution.Molecular biology and evolution · 2021Article
5 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Transcription factor (TF) perturbation experiments give valuable insights into gene regulation. Genome-scale evidence from microarray measurements may be used to identify regulatory interactions between TFs and targets. Recently, Hu and colleagues published a comprehensive study covering 269 TF knockout mutants for the yeast Saccharomyces cerevisiae. However, the information that can be extracted from this valuable dataset is limited by the method employed to process the microarray data. Here, we present a reanalysis of the original data using improved statistical techniques freely available from the BioConductor project. We identify over 100,000 differentially expressed genes-nine times the total reported by Hu et al. We validate the biological significance of these genes by assessing their functions, the occurrence of upstream TF-binding sites, and the prevalence of protein-protein interactions. The reanalysed dataset outperforms the original across all measures, indicating that we have uncovered a vastly expanded list of relevant targets. In summary, this work presents a high-quality reanalysis that maximizes the information contained in the Hu et al. compendium. The dataset is available from ArrayExpress (accession: E-MTAB-109) and it will be invaluable to any scientist interested in the yeast transcriptional regulatory system.
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.