1a:I[76865,["/_next/static/chunks/d07a36b34c614bac.js","/_next/static/chunks/a439560253821848.js","/_next/static/chunks/992d80515d7f780d.js","/_next/static/chunks/3adfb55b15c8d19c.js","/_next/static/chunks/b642b3e65e2c6231.js","/_next/static/chunks/fc50eca1ad01dc11.js","/_next/static/chunks/afc6bdd59b458409.js","/_next/static/chunks/6448cccc5d968256.js"],"Grid"] 1b:I[17291,["/_next/static/chunks/d07a36b34c614bac.js","/_next/static/chunks/a439560253821848.js","/_next/static/chunks/992d80515d7f780d.js","/_next/static/chunks/3adfb55b15c8d19c.js","/_next/static/chunks/b642b3e65e2c6231.js","/_next/static/chunks/fc50eca1ad01dc11.js","/_next/static/chunks/afc6bdd59b458409.js","/_next/static/chunks/6448cccc5d968256.js"],"GridCol"] 1c:I[34616,["/_next/static/chunks/d07a36b34c614bac.js","/_next/static/chunks/a439560253821848.js","/_next/static/chunks/992d80515d7f780d.js","/_next/static/chunks/3adfb55b15c8d19c.js","/_next/static/chunks/b642b3e65e2c6231.js","/_next/static/chunks/fc50eca1ad01dc11.js","/_next/static/chunks/afc6bdd59b458409.js","/_next/static/chunks/6448cccc5d968256.js"],"Avatar"] 1d:I[32972,["/_next/static/chunks/d07a36b34c614bac.js","/_next/static/chunks/a439560253821848.js","/_next/static/chunks/992d80515d7f780d.js","/_next/static/chunks/3adfb55b15c8d19c.js","/_next/static/chunks/b642b3e65e2c6231.js","/_next/static/chunks/fc50eca1ad01dc11.js","/_next/static/chunks/afc6bdd59b458409.js","/_next/static/chunks/6448cccc5d968256.js"],"default"] 1e:T4e0,Network-based systems biology has become an important method for analyzing high-throughput gene expression data and gene function mining. Escherichia coli (E. coli) has long been a popular model organism for basic biological research. In this paper, weighted gene co-expression network analysis (WGCNA) algorithm was applied to construct gene co-expression networks in E. coli. Thirty-one gene co-expression modules were detected from 1391 microarrays of E. coli data. Further characterization of these modules with the database for annotation, visualization, and integrated discovery (DAVID) tool showed that these modules are associated with several kinds of biological processes, such as carbohydrate catabolism, fatty acid metabolism, amino acid metabolism, transportation, translation, and ncRNA metabolism. Hub genes were also screened by intra-modular connectivity. Genes with unknown functions were annotated by guilt-by-association. Comparison with a previous prediction tool, EcoliNet, suggests that our dataset can expand gene predictions. In summary, 31 functional modules were identified in E. coli, 24 of which were functionally annotated. The analysis provides a resource for future gene discovery.

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