Evidence map›Paper›PMID 24923819›Full record

ArticleDatabase : the journal of biological databases and curation2014

Curation accuracy of model organism databases.

Ingrid M Keseler, Marek Skrzypek, Deepika Weerasinghe, Albert Y Chen, Carol Fulcher, Gene-Wei Li, Kimberly C Lemmer, Katherine M Mladinich, Edmond D Chow, Gavin Sherlock and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Database : the journal of biological databases and curation, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed.

  1. Article
  2. The EcoCyc database (2025).EcoSal Plus · 2025
    Review
  3. The EcoCyc Database (2023).EcoSal Plus · 2023
    Review
  4. Article
  5. Article
  6. Review
  7. Evaluating the predictive accuracy of curated biological pathways in a public knowledgebase.Database : the journal of biological databases and curation · 2022
    Article
  8. Article
  9. Article
  10. Leveraging Curation AmongFrontiers in microbiology · 2021
    Article
  11. Article
  12. Towards Knowledge Maintenance in Scientific Digital Libraries with the Keystone Framework.Proceedings of the ... ACM/IEEE Joint Conference on Digital Libraries. ACM/IEEE Joint Conference on Digital Libraries · 2020
    Article
  13. Constructing knowledge graphs and their biomedical applications.Computational and structural biotechnology journal · 2020
    Review
  14. Article
  15. Article
  16. The EcoCyc Database.EcoSal Plus · 2018
    Review
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Ingrid M KeselerBioinformatics Research Group, Artificial Intelligence Center, SRI International, CA, USA, Department of Genetics, Stanford University, CA 94305, USA, Department of Bacteriology, University of Wisconsin, WI 53706-1521, USA, Department of Cellular and Molecular Pharmacology, University of California at San Francisco, CA 94158-2140, USA, DOE Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, WI 53726, USA and Department of Medical Microbiology and Immunology, University of Wisconsin, WI 53706-1521, USA.
Marek SkrzypekBioinformatics Research Group, Artificial Intelligence Center, SRI International, CA, USA, Department of Genetics, Stanford University, CA 94305, USA, Department of Bacteriology, University of Wisconsin, WI 53706-1521, USA, Department of Cellular and Molecular Pharmacology, University of California at San Francisco, CA 94158-2140, USA, DOE Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, WI 53726, USA and Department of Medical Microbiology and Immunology, University of Wisconsin, WI 53706-1521, USA.
Deepika WeerasingheBioinformatics Research Group, Artificial Intelligence Center, SRI International, CA, USA, Department of Genetics, Stanford University, CA 94305, USA, Department of Bacteriology, University of Wisconsin, WI 53706-1521, USA, Department of Cellular and Molecular Pharmacology, University of California at San Francisco, CA 94158-2140, USA, DOE Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, WI 53726, USA and Department of Medical Microbiology and Immunology, University of Wisconsin, WI 53706-1521, USA.
Albert Y ChenBioinformatics Research Group, Artificial Intelligence Center, SRI International, CA, USA, Department of Genetics, Stanford University, CA 94305, USA, Department of Bacteriology, University of Wisconsin, WI 53706-1521, USA, Department of Cellular and Molecular Pharmacology, University of California at San Francisco, CA 94158-2140, USA, DOE Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, WI 53726, USA and Department of Medical Microbiology and Immunology, University of Wisconsin, WI 53706-1521, USA.
Carol FulcherBioinformatics Research Group, Artificial Intelligence Center, SRI International, CA, USA, Department of Genetics, Stanford University, CA 94305, USA, Department of Bacteriology, University of Wisconsin, WI 53706-1521, USA, Department of Cellular and Molecular Pharmacology, University of California at San Francisco, CA 94158-2140, USA, DOE Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, WI 53726, USA and Department of Medical Microbiology and Immunology, University of Wisconsin, WI 53706-1521, USA.
Gene-Wei LiBioinformatics Research Group, Artificial Intelligence Center, SRI International, CA, USA, Department of Genetics, Stanford University, CA 94305, USA, Department of Bacteriology, University of Wisconsin, WI 53706-1521, USA, Department of Cellular and Molecular Pharmacology, University of California at San Francisco, CA 94158-2140, USA, DOE Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, WI 53726, USA and Department of Medical Microbiology and Immunology, University of Wisconsin, WI 53706-1521, USA.
Kimberly C LemmerBioinformatics Research Group, Artificial Intelligence Center, SRI International, CA, USA, Department of Genetics, Stanford University, CA 94305, USA, Department of Bacteriology, University of Wisconsin, WI 53706-1521, USA, Department of Cellular and Molecular Pharmacology, University of California at San Francisco, CA 94158-2140, USA, DOE Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, WI 53726, USA and Department of Medical Microbiology and Immunology, University of Wisconsin, WI 53706-1521, USA.
Katherine M MladinichBioinformatics Research Group, Artificial Intelligence Center, SRI International, CA, USA, Department of Genetics, Stanford University, CA 94305, USA, Department of Bacteriology, University of Wisconsin, WI 53706-1521, USA, Department of Cellular and Molecular Pharmacology, University of California at San Francisco, CA 94158-2140, USA, DOE Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, WI 53726, USA and Department of Medical Microbiology and Immunology, University of Wisconsin, WI 53706-1521, USA.
Edmond D ChowBioinformatics Research Group, Artificial Intelligence Center, SRI International, CA, USA, Department of Genetics, Stanford University, CA 94305, USA, Department of Bacteriology, University of Wisconsin, WI 53706-1521, USA, Department of Cellular and Molecular Pharmacology, University of California at San Francisco, CA 94158-2140, USA, DOE Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, WI 53726, USA and Department of Medical Microbiology and Immunology, University of Wisconsin, WI 53706-1521, USA.
Gavin SherlockBioinformatics Research Group, Artificial Intelligence Center, SRI International, CA, USA, Department of Genetics, Stanford University, CA 94305, USA, Department of Bacteriology, University of Wisconsin, WI 53706-1521, USA, Department of Cellular and Molecular Pharmacology, University of California at San Francisco, CA 94158-2140, USA, DOE Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, WI 53726, USA and Department of Medical Microbiology and Immunology, University of Wisconsin, WI 53706-1521, USA.
Peter D KarpBioinformatics Research Group, Artificial Intelligence Center, SRI International, CA, USA, Department of Genetics, Stanford University, CA 94305, USA, Department of Bacteriology, University of Wisconsin, WI 53706-1521, USA, Department of Cellular and Molecular Pharmacology, University of California at San Francisco, CA 94158-2140, USA, DOE Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, WI 53726, USA and Department of Medical Microbiology and Immunology, University of Wisconsin, WI 53706-1521, USA pkarp@ai.sri.com.

Funding

The EcoCyc Model Organism Database for Escherichia coli [SRI Proposal ECU 14-631]U24GM077678 · NIGMS · SRI INTERNATIONAL · PI KARP, PETER D · 2006 to 2018
$12.3M
Genomic Database for Candida AlbicansR01DE015873 · NIDCR · STANFORD UNIVERSITY · PI Gavin J Sherlock · 2004 to 2026
$11.7M
Probing the function of translational pausing in bacterial protein synthesisR00GM105913 · NIGMS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI LI, GENE-WEI · 2015 to 2017
$747k
NIDCR NIH HHS R01DE015873NIGMS NIH HHS R00 GM105913NIGMS NIH HHS U24 GM077678NIGMS NIH HHS U24GM077678
6 · The paper itself

Abstract

Manual extraction of information from the biomedical literature-or biocuration-is the central methodology used to construct many biological databases. For example, the UniProt protein database, the EcoCyc Escherichia coli database and the Candida Genome Database (CGD) are all based on biocuration. Biological databases are used extensively by life science researchers, as online encyclopedias, as aids in the interpretation of new experimental data and as golden standards for the development of new bioinformatics algorithms. Although manual curation has been assumed to be highly accurate, we are aware of only one previous study of biocuration accuracy. We assessed the accuracy of EcoCyc and CGD by manually selecting curated assertions within randomly chosen EcoCyc and CGD gene pages and by then validating that the data found in the referenced publications supported those assertions. A database assertion is considered to be in error if that assertion could not be found in the publication cited for that assertion. We identified 10 errors in the 633 facts that we validated across the two databases, for an overall error rate of 1.58%, and individual error rates of 1.82% for CGD and 1.40% for EcoCyc. These data suggest that manual curation of the experimental literature by Ph.D-level scientists is highly accurate. Database URL: http://ecocyc.org/, http://www.candidagenome.org//

Indexed as

Databases, GeneticDatabases, ProteinCandidaData MiningEscherichia coliReproducibility of Results

Identifiers

PMID24923819
PMCPMC4207230

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.