RegulonDB (version 5.0): Escherichia coli K-12 transcriptional regulatory network, operon organization, and growth conditions

By Heladia Salgado, S. Gama-Castro, Martín Peralta-Gil, Edgar Díaz-Peredo, Fabiola Sánchez-Solano, Alberto Santos-Zavaleta, Irma Martínez-Flores, V. Jiménez-Jacinto, César Bonavides-Martínez, J. Segura-Salazar and 2 more. Nucleic Acids Res., 2005

387 citations recorded by the source corpus.

RegulonDB is the internationally recognized reference database of Escherichia coli K-12 offering curated knowledge of the regulatory network and operon organization. It is currently the largest electronically-encoded database of the regulatory network of any free-living organism. We present here the recently launched RegulonDB version 5.0 radically different in content, interface design and capabilities. Continuous curation of original scientific literature provides the evidence behind every single object and feature. This knowledge is complemented with comprehensive computational predictions across the complete genome. Literature-based and predicted data are clearly distinguished in the database. Starting with this version, RegulonDB public releases are synchronized with those of EcoCyc since our curation supports both databases. The complex biology of regulation is simplified in a navigation scheme based on three major streams: genes, operons and regulons. Regulatory knowledge is directly available in every navigation step. Displays combine graphic and textual information and are organized allowing different levels of detail and biological context. This knowledge is the backbone of an integrated system for the graphic display of the network, graphic and tabular microarray comparisons with curated and predicted objects, as well as predictions across bacterial genomes, and predicted networks of functionally related gene products. Access RegulonDB at .

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0c57b6eaaf5543619c344b13b0e89568e4dc039a

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Citations in the corpus

Introduction to systems biology. (2007, background): “For simple organisms, such as Escherichia coli or yeast, large parts of the metabolic network structure, the operon structure and their transcriptional regulators are now known [1–3].”
RegulonDB (version 6.0): gene regulation model of Escherichia coli K-12 beyond transcription, active (experimental) annotated promoters and Textpresso navigation (2007, background): “RegulonDB is currently one of the largest database offering curated knowledge of the transcriptional regulatory network of any free-living organism (1).”
Graph theory and networks in Biology. (2006, background): “cerevisiae [24, 53–55] and are maintained in databases such as RegulonDB [24] and EcoCyc [55].”
Centrality Analysis Methods for Biological Networks and Their Application to Gene Regulatory Networks (2008, methodology): “As GRNs are directed graphs that are not necessarily strong connected only the centralities degree, shortest-path betweenness, integration, radiality, Katz status index, PageRank and the different motif-based centralities can be applied.”
Comparing Statistical Methods for Constructing Large Scale Gene Networks (2012, methodology): “Overall, the performance in real data seemed to be worse than that in the simulation study, and there are several possible reasons: (1) the real biological network is much more complex than the simulation study; (2) many true connections in this network are still unknown; (3) some of the connections”
Gene co-expression analysis for functional classification and gene–disease predictions (2017, result): “A comparison between these tools and others, including WGCNA, showed that WGCNA and ARACNE perform best at defining the network structure of Escherichia coli [130], for which a well-defined regulatory network was used as a gold standard [131].”
Using graph theory to analyze biological networks (2011, methodology): “Network maps have been constructed for the transcriptional regulatory networks of E. coli and S. cerevisiae and are maintained in databases [26,157,158].”
Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles (2007, background): “To score our results, we compared our predicted interactions with the set of 3,216 interactions in the RegulonDB database [27].”
A bagging SVM to learn from positive and unlabeled examples (2010, methodology): “We extracted the list of known regulated genes for each TF from RegulonDB (Salgado et al., 2006).”