Curation accuracy of model organism databases

Keseler, Ingrid M.; Skrzypek, Marek S.; Weerasinghe, Deepika; Chen, Albert Y.; Fulcher, Carol A.; Li, Gene-Wei; Lemmer, Kimberly C.; Mladinich, Katherine M.; Chow, Edmond D.; Sherlock, Gavin; Karp, Peter D.

doi:10.1093/database/bau058

Cited by 29 publications

(21 citation statements)

References 20 publications

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“…This benefitted CTD in many ways. Instead of relying on co-mentioned terms from an abstract, CTD had Ph.D.-level scientists reading the primary literature and coding the authors’ detailed results in a computable format, increasing the accuracy and reliability of the information ( 10 , 11 ). In 2006, we produced MEDIC ( 12 ), a resource of merged OMIM ( 13 ) and MeSH ( 14 ) disease terms, allowing biocurators to additionally capture chemical–disease (C–D) and gene–disease (G–D) relationships using a robust and hierarchical controlled vocabulary.…”

Section: Ctd's 10th Year Anniversarymentioning

confidence: 99%

The Comparative Toxicogenomics Database's 10th year anniversary: update 2015

Davis

Grondin

Lennon-Hopkins

et al. 2014

Nucleic Acids Research

343

300

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Ten years ago, the Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) was developed out of a need to formalize, harmonize and centralize the information on numerous genes and proteins responding to environmental toxic agents across diverse species. CTD's initial approach was to facilitate comparisons of nucleotide and protein sequences of toxicologically significant genes by curating these sequences and electronically annotating them with chemical terms from their associated references. Since then, however, CTD has vastly expanded its scope to robustly represent a triad of chemical–gene, chemical–disease and gene–disease interactions that are manually curated from the scientific literature by professional biocurators using controlled vocabularies, ontologies and structured notation. Today, CTD includes 24 million toxicogenomic connections relating chemicals/drugs, genes/proteins, diseases, taxa, phenotypes, Gene Ontology annotations, pathways and interaction modules. In this 10th year anniversary update, we outline the evolution of CTD, including our increased data content, new ‘Pathway View’ visualization tool, enhanced curation practices, pilot chemical–phenotype results and impending exposure data set. The prototype database originally described in our first report has transformed into a sophisticated resource used actively today to help scientists develop and test hypotheses about the etiologies of environmentally influenced diseases.

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Section: Ctd's 10th Year Anniversarymentioning

confidence: 99%

The Comparative Toxicogenomics Database's 10th year anniversary: update 2015

Davis

Grondin

Lennon-Hopkins

et al. 2014

Nucleic Acids Research

343

300

View full text Add to dashboard Cite

show abstract

“…In life sciences, manual expert curation plays a fundamental role in the creation of high quality knowledgebases. Manual curation is acknowledged to be highly accurate 1 , 2 , but criticism is often raised about the necessity for such a time- (and cost-) consuming activity as opposed to the use of programs for automated or semi-automated information extraction (Information-Extraction programs—IE programs). In reality, current IE programs are not able to extract the large amount of information or compare data with the same accuracy as professional curators do, but they can be extremely useful for identifying mentions of single entities in the scientific publications, using for instance name-entity recognition tools 1 .…”

Section: Introductionmentioning

confidence: 99%

Funding knowledgebases: Towards a sustainable funding model for the UniProt use case

2018

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Millions of life scientists across the world rely on bioinformatics data resources for their research projects. Data resources can be very expensive, especially those with a high added value as the expert-curated knowledgebases. Despite the increasing need for such highly accurate and reliable sources of scientific information, most of them do not have secured funding over the near future and often depend on short-term grants that are much shorter than their planning horizon. Additionally, they are often evaluated as research projects rather than as research infrastructure components. In this work, twelve funding models for data resources are described and applied on the case study of the Universal Protein Resource (UniProt), a key resource for protein sequences and functional information knowledge. We show that most of the models present inconsistencies with open access or equity policies, and that while some models do not allow to cover the total costs, they could potentially be used as a complementary income source. We propose the Infrastructure Model as a sustainable and equitable model for all core data resources in the life sciences. With this model, funding agencies would set aside a fixed percentage of their research grant volumes, which would subsequently be redistributed to core data resources according to well-defined selection criteria. This model, compatible with the principles of open science, is in agreement with several international initiatives such as the Human Frontiers Science Program Organisation (HFSPO) and the OECD Global Science Forum (GSF) project. Here, we have estimated that less than 1% of the total amount dedicated to research grants in the life sciences would be sufficient to cover the costs of the core data resources worldwide, including both knowledgebases and deposition databases.

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“…In contrast, a recent study of ours has shown the accuracy of manual curation to be very high (1.4% error rate for EcoCyc, 1.8% error rate for Candida Genome Database) (6). Thus, the 33 IEPs for recognizing single relations surveyed by (4, 5) have error rates that are 14–42 times higher than the error rates of manual curation.…”

Section: Text Mining As An Alternative To Professional Curationmentioning

confidence: 67%

Can we replace curation with information extraction software?

Karp

2016

Database

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Can we use programs for automated or semi-automated information extraction from scientific texts as practical alternatives to professional curation? I show that error rates of current information extraction programs are too high to replace professional curation today. Furthermore, current IEP programs extract single narrow slivers of information, such as individual protein interactions; they cannot extract the large breadth of information extracted by professional curators for databases such as EcoCyc. They also cannot arbitrate among conflicting statements in the literature as curators can. Therefore, funding agencies should not hobble the curation efforts of existing databases on the assumption that a problem that has stymied Artificial Intelligence researchers for more than 60 years will be solved tomorrow. Semi-automated extraction techniques appear to have significantly more potential based on a review of recent tools that enhance curator productivity. But a full cost-benefit analysis for these tools is lacking. Without such analysis it is possible to expend significant effort developing information-extraction tools that automate small parts of the overall curation workflow without achieving a significant decrease in curation costs.Database URL:

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Curation accuracy of model organism databases

Cited by 29 publications

References 20 publications

The Comparative Toxicogenomics Database's 10th year anniversary: update 2015

The Comparative Toxicogenomics Database's 10th year anniversary: update 2015

Funding knowledgebases: Towards a sustainable funding model for the UniProt use case

Can we replace curation with information extraction software?

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