RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond

Socorro Gama‐Castro(Universidad Nacional Autónoma de México), Heladia Salgado(Universidad Nacional Autónoma de México), Alberto Santos-Zavaleta(Universidad Nacional Autónoma de México), Daniela Ledezma-Tejeida(Universidad Nacional Autónoma de México), Luis Muñiz-Rascado(Universidad Nacional Autónoma de México), Jair Santiago García-Sotelo(Universidad Nacional Autónoma de México), Kevin Alquicira‐Hernández(Universidad Nacional Autónoma de México), Irma Martínez‐Flores(Universidad Nacional Autónoma de México), Lucia Pannier(Universidad Nacional Autónoma de México), Jaime A. Castro-Mondragón(Inserm), Alejandra Medina-Rivera(Universidad Nacional Autónoma de México), Hilda Solano-Lira(Universidad Nacional Autónoma de México), César Bonavides-Martínez(Universidad Nacional Autónoma de México), Ernesto Pérez‐Rueda(Universidad Nacional Autónoma de México), Shirley Alquicira-Hernández(Universidad Nacional Autónoma de México), Liliana Porrón-Sotelo(Universidad Nacional Autónoma de México), Alejandra López-Fuentes(Universidad Nacional Autónoma de México), Anastasia Hernández-Koutoucheva(Universidad Nacional Autónoma de México), Víctor Del Moral-Chávez(Universidad Nacional Autónoma de México), Fabio Rinaldi(University of Zurich), Julio Collado‐Vides(Universidad Nacional Autónoma de México)
Nucleic Acids Research
November 2, 2015
Cited by 545Open Access
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Abstract

RegulonDB (http://regulondb.ccg.unam.mx) is one of the most useful and important resources on bacterial gene regulation,as it integrates the scattered scientific knowledge of the best-characterized organism, Escherichia coli K-12, in a database that organizes large amounts of data. Its electronic format enables researchers to compare their results with the legacy of previous knowledge and supports bioinformatics tools and model building. Here, we summarize our progress with RegulonDB since our last Nucleic Acids Research publication describing RegulonDB, in 2013. In addition to maintaining curation up-to-date, we report a collection of 232 interactions with small RNAs affecting 192 genes, and the complete repertoire of 189 Elementary Genetic Sensory-Response units (GENSOR units), integrating the signal, regulatory interactions, and metabolic pathways they govern. These additions represent major progress to a higher level of understanding of regulated processes. We have updated the computationally predicted transcription factors, which total 304 (184 with experimental evidence and 120 from computational predictions); we updated our position-weight matrices and have included tools for clustering them in evolutionary families. We describe our semiautomatic strategy to accelerate curation, including datasets from high-throughput experiments, a novel coexpression distance to search for 'neighborhood' genes to known operons and regulons, and computational developments.


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