ArticleNucleic acids research2023
A multi-scale expression and regulation knowledge base for Escherichia coli.
Article in Nucleic acids research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it, 58 citations in OpenAlex.
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- Dissecting host stress responses for predictable heterologous gene expression in E. coli.Nucleic acids research · 2026Article
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- The transcriptional regulatory network of the Escherichia coli MG1655 reference strain.Nucleic acids research · 2026Article
- Predicting input signals of transcription factors in Escherichia coli.Molecular systems biology · 2025Article
- Laboratory Evolution Reveals Transcriptional Mechanisms Underlying Thermal Adaptation of Escherichia coli.Genome biology and evolution · 2025Article
- A transcriptional plasticity-aware framework for RNA-seq differential expression analysis.Briefings in bioinformatics · 2025Article
- PGBTR: a powerful and general method for inferring bacterial transcriptional regulatory networks.BMC genomics · 2025Article
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- Improving prediction of bacterial sRNA regulatory targets with expression data.NAR genomics and bioinformatics · 2025Article
- The Environment-Dependent Regulatory Landscape of thebioRxiv : the preprint server for biology · 2025Article
- Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Transcriptomic data is accumulating rapidly; thus, scalable methods for extracting knowledge from this data are critical. Here, we assembled a top-down expression and regulation knowledge base for Escherichia coli. The expression component is a 1035-sample, high-quality RNA-seq compendium consisting of data generated in our lab using a single experimental protocol. The compendium contains diverse growth conditions, including: 9 media; 39 supplements, including antibiotics; 42 heterologous proteins; and 76 gene knockouts. Using this resource, we elucidated global expression patterns. We used machine learning to extract 201 modules that account for 86% of known regulatory interactions, creating the regulatory component. With these modules, we identified two novel regulons and quantified systems-level regulatory responses. We also integrated 1675 curated, publicly-available transcriptomes into the resource. We demonstrated workflows for analyzing new data against this knowledge base via deconstruction of regulation during aerobic transition. This resource illuminates the E. coli transcriptome at scale and provides a blueprint for top-down transcriptomic analysis of non-model organisms.
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