Summary The rapid growth of bacterial gene expression databases has enabled computational inference of transcriptional regulatory networks (TRNs), yet it remains unclear why mathematically simple models often capture their apparent complexity. Using a 1035-sample E. coli expression database, we identify two transcriptome principles that support successful TRN inference. First, regulons defined from measured binding sites show limited overlap in gene membership, consistent with statistical independence exhibited by many successful inference methods. Second, 21% of genes, or 877 genes, exhibit regulator “dominance,” in which expression strongly correlates with a single regulator activity and receives minimal contributions from other regulators under most conditions. We formalize these properties with quantitative metrics and provide a reference catalog of dominantly regulated E. coli genes. Regulator dominance explains differences between expression-inferred and binding site-defined regulons, and removing dominated genes sharply reduces inference performance, suggesting that simply regulated promoter subsets are central to effective TRN inference.
Gaoyuan Li, Joshua T. Burrows, Xuwen A. Lou et al.· iScience· 0 citations
ABSTRACT The accelerating deposition of RNAseq data over the past decade has motivated the development of advanced transcriptomic data analytics that can operate on a large number of samples. One successful approach is to apply independent component analysis (ICA) to large prokaryotic transcriptomic compendia to decompose them into independently modulated gene sets, called iModulons. Here, we review the data science principles underlying ICA-based transcriptome decomposition, computational workflows that support its routine application, and iModulonDB infrastructure that hosts and disseminates the resulting decompositions. We present iModulonDB 3.0 that contains 53 species and 71 ICA decompositions across 33,062 RNA-seq samples, with several well-sampled species (e.g., Escherichia coli, Bacillus subtilis, Staphylococcus aureus, Pseudomonas aeruginosa) represented by more than one compendium. With 71 standardized decompositions, we demonstrate systematic cross-species comparison of species-specific iModulon structures. This comparison identifies a shared “regulatory toolkit” of 13 modules conserved across distantly related bacteria, alongside a long tail of lineage-specific programs. We assess the design principles and limitations governing iModulon reconstruction and computation. Together, these advances position the iModulon framework as an accessible, community-driven approach for reading accumulating public transcriptomes as reusable regulatory programs, enabling biological discovery and module-level design in synthetic biology.
Kangsan Kim, E. Catoiu, Yongjae Lee et al.· mSystems· 0 citations
The results demonstrate that iModulons provide a genome-scale framework for comparing transcriptional regulation across closely related organisms, revealing regulatory innovations that are not apparent from genome comparisons alone.
Heera Bajpe, Ying Hefner, R. Szubin et al.· bioRxiv· 0 citations