RegulonDB (version 6.0): gene regulation model of Escherichia coli K-12 beyond transcription, active (experimental) annotated promoters and Textpresso navigation

By S. Gama-Castro, V. Jiménez-Jacinto, Martín Peralta-Gil, Alberto Santos-Zavaleta, Mónica I. Peñaloza-Spínola, Bruno Contreras-Moreira, J. Segura-Salazar, Luis Muñiz-Rascado, Irma Martínez-Flores, Heladia Salgado and 9 more. Nucleic Acids Res., 2007

529 citations recorded by the source corpus.

RegulonDB (http://regulondb.ccg.unam.mx/) is the primary reference database offering curated knowledge of the transcriptional regulatory network of Escherichia coli K12, currently the best-known electronically encoded database of the genetic regulatory network of any free-living organism. This paper summarizes the improvements, new biology and new features available in version 6.0. Curation of original literature is, from now on, up to date for every new release. All the objects are supported by their corresponding evidences, now classified as strong or weak. Transcription factors are classified by origin of their effectors and by gene ontology class. We have now computational predictions for σ54 and five different promoter types of the σ70 family, as well as their corresponding −10 and −35 boxes. In addition to those curated from the literature, we added about 300 experimentally mapped promoters coming from our own high-throughput mapping efforts. RegulonDB v.6.0 now expands beyond transcription initiation, including RNA regulatory elements, specifically riboswitches, attenuators and small RNAs, with their known associated targets. The data can be accessed through overviews of correlations about gene regulation. RegulonDB associated original literature, together with more than 4000 curation notes, can now be searched with the Textpresso text mining engine.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0af4dd1a1ea4b7357b327b63bbd7cf0f513a6f18

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Citations in the corpus

EcoCyc: a comprehensive database of Escherichia coli biology (2010, methodology): “EcoCyc knowledge of the transcriptional regulatory network is obtained from the literature, and is continually updated by the RegulonDB biocurator team (2).”
DOOR: a database for prokaryotic operons (2008, background): “coli only (4), collected from the published literature.”
EcoCyc: A comprehensive view of Escherichia coli biology (2008, background): “A collection of 259 new transcription start sites, which resulted from a high-throughput experimental modified RACE approach, was added (6).”
From sequence to dynamics: the effects of transcription factor and polymerase concentration changes on activated and repressed promoters (2009, unknown): “At the same time, we obtained the PWMs representing E. coli TF binding sites (and all the information on regulatory sites) from RegulonDB, release 6.2 [11], and scored the binding site variants from ProNIT using the Patser program included within the Consensus package [33,34].”
RegPrecise 3.0 – A resource for genome-scale exploration of transcriptional regulation in bacteria (2013, methodology): “We are planning to incorporate supportive experimental evidences for reconstructed regulons and effectors using information from literature and other databases on microbial regulation including RegTransBase [25], RegulonDB [26], DBTBS [27], and CoryneRegNet [24].”
Global Functional Atlas of Escherichia coli Encompassing Previously Uncharacterized Proteins (2009, background): “These include a genome-wide collection of single-gene deletion strains [2] along with extensive knowledge of regulatory circuits [3,5,7,9] and metabolic pathways [6,10,11].”
Gene Ontology annotation of sequence-specific DNA binding transcription factors: setting the stage for a large-scale curation effort (2013, background): “…uniform standard for evaluation of experimental evidence for DbTF annotations, we surveyed several relevant resources defining experimental assays that can document TF function, including ORegAnno (21), TRRD (22), RegulonDB (23) and the PSI-MI controlled vocabulary for molecular interactions (20).”
Using MCL to extract clusters from networks. (2012, background): “coli is the wealth of available annotation, both at the level of gene function and regulation [22, 23].”
Inferring Regulatory Networks from Expression Data Using Tree-Based Methods (2010, methodology): “To validate the network predictions we used 3433 experimentally confirmed regulatory interactions among 1471 genes that have been curated in RegulonDB version 6.4 [40].”
RegulonDB version 7.0: transcriptional regulation of Escherichia coli K-12 integrated within genetic sensory response units (Gensor Units) (2010, background): “The accumulated work along 3 years, since publication of the previous RegulonDB paper (16), show considerable progress.”
RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond (2015, background): “Strong confidence essentially requires physical evidence for the existence of the object or interaction (21).”
BioPAX – A community standard for pathway data sharing (2010, unknown): “• Gene regulatory networks involve transcription and translation events and their control 12,14 .”
COBRApy: COnstraints-Based Reconstruction and Analysis for Python (2013, background): “These constraints include compartmentalization, mass conservation, molecular crowding [11], thermodynamic directionality [12], and transcription factor activity [13].”
New methods for separating causes from effects in genomics data (2012, methodology): “To overcome these challenges we focused on transcription factor-gene regulatory interactions that have been discovered on the genome wide level and experimentally verified in model organisms [20,21] and more recently in human cell lines [22].”