ArticleNature communications2020
Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 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
49 citing papers in PubMed, 92 citations in OpenAlex.
- Transcriptional regulatory network differences between six industrialMetabolic engineering communications · 2026Article
- Review
- Targeting Quorum Sensing to Combat Foodborne Pathogens: A Dual Strategy Against Spoilage and Pathogenesis.Foods (Basel, Switzerland) · 2026Review
- Revealing transcriptomic responses inmSystems · 2026Article
- Transcriptional regulatory networks of the human gut symbiont Bacteroides thetaiotaomicron are uncovered using machine learning.Nucleic acids research · 2025Article
- PGBTR: a powerful and general method for inferring bacterial transcriptional regulatory networks.BMC genomics · 2025Article
- Revealing systematic changes in the transcriptome during the transition from exponential growth to stationary phase.mSystems · 2025Article
- iModulonDB 2.0: dynamic tools to facilitate knowledge-mining and user-enabled analyses of curated transcriptomic datasets.Nucleic acids research · 2025Article
- Detection of environmental pollutants using transcription factor-based whole-cell biosensors.Biotechnology for the environment · 2025Review
- Deciphering the proteome ofComputational and structural biotechnology journal · 2025Article
- New insights into the efficient secretion of foreign protein in Bacillus subtilis via Ribo-seq and RNA-seq integrative analyses.BMC microbiology · 2024Article
- Current models in bacterial hemicellulase-encoding gene regulation.Applied microbiology and biotechnology · 2024Review
- A Multi-Omics, Machine Learning-Aware, Genome-Wide Metabolic Model of Bacillus Subtilis Refines the Gene Expression and Cell Growth Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Combined oxygen and glucose oscillations distinctly change the transcriptional and physiological state of Escherichia coli.Microbial biotechnology · 2024Article
- Modulating bacterial function utilizing A knowledge base of transcriptional regulatory modules.Nucleic acids research · 2024Article
- iModulonMiner and PyModulon: Software for unsupervised mining of gene expression compendia.PLoS computational biology · 2024Article
- Machine learning reveals the transcriptional regulatory network and circadian dynamics ofProceedings of the National Academy of Sciences of the United States of America · 2024Article
- Translation in Bacillus subtilis is spatially and temporally coordinated during sporulation.Nature communications · 2024Article
- Proteome allocation is linked to transcriptional regulation through a modularized transcriptome.Nature communications · 2024Article
- Multi-omic analysis tools for microbial metabolites prediction.Briefings in bioinformatics · 2024Review
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
No grant is acknowledged in the PubMed record.
Abstract
The transcriptional regulatory network (TRN) of Bacillus subtilis coordinates cellular functions of fundamental interest, including metabolism, biofilm formation, and sporulation. Here, we use unsupervised machine learning to modularize the transcriptome and quantitatively describe regulatory activity under diverse conditions, creating an unbiased summary of gene expression. We obtain 83 independently modulated gene sets that explain most of the variance in expression and demonstrate that 76% of them represent the effects of known regulators. The TRN structure and its condition-dependent activity uncover putative or recently discovered roles for at least five regulons, such as a relationship between histidine utilization and quorum sensing. The TRN also facilitates quantification of population-level sporulation states. As this TRN covers the majority of the transcriptome and concisely characterizes the global expression state, it could inform research on nearly every aspect of transcriptional regulation in B. subtilis.
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.