EcoCyc: fusing model organism databases with systems biology

By I. Keseler, A. Mackie, Martín Peralta-Gil, Alberto Santos-Zavaleta, S. Gama-Castro, César Bonavides-Martínez, Carol A. Fulcher, Araceli M. Huerta, Anamika Kothari, Markus Krummenacker and 14 more. Nucleic Acids Res., 2012

614 citations recorded by the source corpus.

EcoCyc (http://EcoCyc.org) is a model organism database built on the genome sequence of Escherichia coli K-12 MG1655. Expert manual curation of the functions of individual E. coli gene products in EcoCyc has been based on information found in the experimental literature for E. coli K-12-derived strains. Updates to EcoCyc content continue to improve the comprehensive picture of E. coli biology. The utility of EcoCyc is enhanced by new tools available on the EcoCyc web site, and the development of EcoCyc as a teaching tool is increasing the impact of the knowledge collected in EcoCyc.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f8e85603cab04e77072a53826a3afac5022c1ad7

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Citations in the corpus

The EcoCyc database: reflecting new knowledge about Escherichia coli K-12 (2016, background): “Since our last publication on EcoCyc in the 2013 NAR Database Issue (2), many improvements to EcoCyc, the Pathway Tools software, the BioCyc family of websites and the display and analysis tools available there have been described elsewhere (3–6).”
Quantifying absolute protein synthesis rates reveals principles underlying allocation of cellular resources. (2014, positive): “of L-homocysteine to L-Met is a bottleneck step that operates at maximal velocity with saturating substrate concentration. Given that Met biosynthesis by MetE is limiting the overall rate of protein synthesis, why do cells not simply make more MetE protein? MetE is a large and slow enzyme, whose pro”
Protein Complexes in Bacteria (2015, methodology): “coli complex datasets ([5]; EcoCyc [13]) and oneM.”
Computational protein design enables a novel one-carbon assimilation pathway (2015, methodology): “Formyl-CoA is not produced or hydrolyzed in any known primary metabolic pathway of E. coli (11).”
Global Mapping of Small RNA-Target Interactions in Bacteria (2016, methodology): “…Cell 63 , 884–897, September 1, 2016 895 RIL-seq results, we compiled an updated dataset of 154experimentally deter-minedsRNA-targetinteractions(TableS3)basedonourpreviouscompilations (Peer and Margalit, 2011, 2014) and information in EcoCyc (http://ecocyc.org) version 19.0 (Keseler et al., 2013).”
A Deep Learning Approach to Antibiotic Discovery. (2020, positive): “Transcript cluster enrichment was performed using EcoCyc Pathway Tools (Karp, 2001; Karp et al., 2016; Keseler et al., 2013).”
SMPDB 2.0: Big Improvements to the Small Molecule Pathway Database (2013, background): “As a result, there is now a plethora of very popular, very high-quality web-accessible pathway databases such as KEGG (1), the ‘Cyc’ databases (2,3), the Reactome database (4), WikiPathways (5) and PharmGKB (6).”
Using Genome-Scale Models to Predict Biological Capabilities (2015, neutral): “Several tools for reactome assembly and curation exist, including the COBRA Toolbox (Ebrahim et al., 2013; Schellenberger et al., 2011b), KEGG (Kanehisa et al., 2014), EcoCyc (Keseler et al., 2013), ModelSeed (Henry et al., 2010b), BiGG (Schellenberger et al., 2010), Rbionet(Thorleifssonand Thiele, ”
Untangling the Genetic Basis of Fibrolytic Specialization by Lachnospiraceae and Ruminococcaceae in Diverse Gut Communities (2013, background): “Metabolic pathways characterized in Ecocyc [35] for each member of the Lachnospiraceae, Clostridiaceae, and Ruminococcaceae in the Biocyc database system [36] (Table S1) were tabulated for each pathway class.”
The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST) (2013, background): “(7) Any remaining gene candidates that do not significantly overlap an existing gene call are included.”
RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond (2015, background): “Our curation work also feeds the EcoCyc database (3), which together with RegulonDB are the major sources of organized information for the best-known bacterial genome model organism.”
Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction (2013, neutral): “…functions of 1541 unique proteincoding open reading frames (ORFs) and 109 RNA genes; it thus accounts for B35% of the 4420 protein-coding ORFs, B65% of the functionally well-annotated ORFs (Riley et al, 2006), and 53.7% of the non-coding RNA genes identified in E. coli K-12 (Keseler et al, 2013).”
The healthy human microbiome (2016, background): “coli K12 contains some 18 % of gene products with no reported function [125], with appreciably more at the E.”
COFACTOR: improved protein function prediction by combining structure, sequence and protein–protein interaction information (2017, result): “(B) GO prediction results in three aspects of molecular function (MF), biological process (BP) and cellular component (CC), which are consistent with UniProt annotation of ysgA as a putative carboxymethylene butenolidase and EcoCyc (41) annotation as a predicted hydrolase.”