CoChat
Sign Up
Cited by 1,107Biomedical Text Mining and OntologiesBioinformatics and Genomic NetworksHealth, Environment, Cognitive Aging

Comparative Toxicogenomics Database (CTD): update 2021

Open access
Davis, Allan Peter, Grondin, Cynthia J, Johnson, Robin J, Sciaky, Daniela, Wiegers, Jolene, Wiegers, Thomas C, Mattingly, Carolyn J·2021·Nucleic Acids Research
DOI 10.1093/nar/gkaa891PMC PMC7779006
Open this paper in CoChat — free accountOpen PDF

Abstract

The public Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) is an innovative digital ecosystem that relates toxicological information for chemicals, genes, phenotypes, diseases, and exposures to advance understanding about human health. Literature-based, manually curated interactions are integrated to create a knowledgebase that harmonizes cross-species heterogeneous data for chemical exposures and their biological repercussions. In this biennial update, we report a 20% increase in CTD curated content and now provide 45 million toxicogenomic relationships for over 16 300 chemicals, 51 300 genes, 5500 phenotypes, 7200 diseases and 163 000 exposure events, from 600 comparative species. Furthermore, we increase the functionality of chemical–phenotype content with new data-tabs on CTD Disease pages (to help fill in knowledge gaps for environmental health) and new phenotype search parameters (for Batch Query and Venn analysis tools). As well, we introduce new CTD Anatomy pages that allow users to uniquely explore and analyze chemical–phenotype interactions from an anatomical perspective. Finally, we have enhanced CTD Chemical pages with new literature-based chemical synonyms (to improve querying) and added 1600 amino acid-based compounds (to increase chemical landscape). Together, these updates continue to augment CTD as a powerful resource for generating testable hypotheses about the etiologies and molecular mechanisms underlying environmentally influenced diseases.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/nar/gkaa891

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • GO21 is a biological knowledge graph containing genes, proteins, drugs and diseases as entities, created based on several widely used biological databases.

    “GO21 is a biological knowledge graph containing genes, proteins, drugs and diseases as entities, created based on several widely used biological databases, including Gene Ontology [9], Disgenet [34], CTD [35], UMLS [36], DrugBank [37], ClassyFire [38], MeSH [39] and PPI [40].”

    From Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones (2021) · cites this paper as [35]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 27

  1. [1]Davis A.P., Murphy C.G., Saraceni-Richards C.A., Rosenstein M.C., Wiegers T.C., Mattingly C.J. Comparative Toxicogenomics Database: a knowledgebase and discovery tool for chemical–gene–disease networks. Nucleic Acids Res. 2009; 37: D786– D792. 18782832 PMC2686584
  2. [2]Davis A.P., King B.L., Mockus S., Murphy C.G., Saraceni-Richards C., Rosenstein M., Wiegers T., Mattingly C.J. The Comparative Toxicogenomics Database: update 2011. Nucleic Acids Res. 2011; 39: D1067– D1072. 20864448 PMC3013756
  3. [3]Davis A.P., Murphy C.G., Johnson R., Lay J.M., Lennon-Hopkins K., Saraceni-Richards C., Sciaky D., King B.L., Rosenstein M.C., Wiegers T.C., Mattingly C.J. The Comparative Toxicogenomics Database: update 2013. Nucleic Acids Res. 2013; 41: D1104– D1114. 23093600 PMC3531134
  4. [4]Davis A.P., Grondin C.J., Lennon-Hopkins K., Saraceni-Richards C., Sciaky D., King B.L., Wiegers T.C., Mattingly C.J. The Comparative Toxicogenomics Database's 10th year anniversary: update 2015. Nucleic Acids Res. 2015; 43: D914– D920. 25326323 PMC4384013
  5. [5]Davis A.P., Grondin C.J., Johnson R.J., Sciaky D., King B.L., McMorran R., Wiegers J., Wiegers T.C., Mattingly C.J. The Comparative Toxicogenomics Database: update 2017. Nucleic Acids Res. 2017; 45: D972– D978. 27651457 PMC5210612
  6. [6]Davis A.P., Grondin C.J., Johnson R.J., Sciaky D., McMorran R., Wiegers J., Wiegers T.C., Mattingly C.J. The Comparative Toxicogenomics Database: update 2019. Nucleic Acids Res. 2019; 47: D948– D954. 30247620 PMC6323936
  7. [7]Wiegers T.C., Davis A.P., Cohen K.B., Hirschman L., Mattingly C.J. Text mining and manual curation of chemical–gene–disease networks for the Comparative Toxicogenomics Database (CTD). BMC Bioinformatics. 2009; 10: 326. 19814812 PMC2768719
  8. [8]Davis A.P., Wiegers T.C., Johnson R.J., Lay J.M., Lennon-Hopkins K., Saraceni-Richards C., Sciaky D., Murphy C.G., Mattingly C.J. Text mining effectively scores and ranks the literature for improving chemical–gene–disease curation at the Comparative Toxicogenomics Database. PLoS One. 2013; 8: e58201. 23613709 PMC3629079
  9. [9]Davis A.P., Wiegers T.C., Rosenstein M.C., Murphy C.G., Mattingly C.J. The curation paradigm and application tool used for manual curation of the scientific literature at the Comparative Toxicogenomics Database. Database. 2011; 2011: bar034. 21933848 PMC3176677
  10. [10]Davis A.P., Wiegers T.C., Rosenstein M.C., Mattingly C.J. MEDIC: a practical disease vocabulary used at the Comparative Toxicogenomics Database. Database. 2012; 2012: bar065. 22434833 PMC3308155
  11. [11]Mattingly C.J., McKone T.E., Callahan M.A., Blake J.A., Hubal E.A. Providing the missing link: the exposure science ontology ExO. Environ. Sci. Technol. 2012; 46: 3046– 3053. 22324457 PMC3314380
  12. [12]Davis A.P., Wiegers T.C., Roberts P.M., King B.L., Lay J.M., Lennon-Hopkins K., Sciaky D., Johnson R., Keating H., Greene N. et al. A CTD-Pfizer collaboration: manual curation of 88,000 scientific articles text mined for drug-disease and drug-phenotype interactions. Database. 2013; 2013: bat080. 24288140 PMC3842776
  13. [13]Grondin C.J., Davis A.P., Wiegers T.C., King B.L., Wiegers J.A., Reif D.M., Hoppin J.A., Mattingly C.J. Advancing exposure science through chemical data curation and integration in the Comparative Toxicogenomics Database. Environ. Health Perspect. 2016; 124: 1592– 1599. 27170236 PMC5047769
  14. [14]Davis A.P., Wiegers T.C., Wiegers J., Johnson R.J., Sciaky D., Grondin C.J., Mattingly C.J. Chemical-induced phenotypes at CTD help to inform the pre-disease state and construct adverse outcome pathways. Toxicol. Sci. 2018; 165: 145– 156. 29846728 PMC6111787
  15. [15]Grondin C.J., Davis A.P., Wiegers T.C., Wiegers J.A., Mattingly C.J. Accessing an expanded exposure science module at the Comparative Toxicogenomics Database. Environ. Health Perspect. 2018; 126: 014501. 29351546 PMC6014688
  16. [16]Davis A.P., Johnson R.J., Lennon-Hopkins K., Sciaky D., Rosenstein M.C., Wiegers T.C., Mattingly C.J. Targeted journal curation as a method to improve data currency at the Comparative Toxicogenomics Database. Database. 2012; 2012: bas051. 23221299 PMC3515863
  17. [17]Wiegers T.C., Davis A.P., Mattingly C.J. Web services-based text-mining demonstrates broad impacts for interoperability and process simplification. Database. 2014; 2014: bau050. 24919658 PMC4207221
  18. [19]Sansone S.A., McQuilton P., Rocca-Serra P., Gonzalez-Beltran A., Izzo M., Lister A.L., Thurston M. FAIRsharing as a community approach to standards, repositories and policies. Nat. Biotechnol. 2019; 37: 358– 367. 30940948 PMC6785156
  19. [20]King B.L., Davis A.P., Rosenstein M.C., Wiegers T.C., Mattingly C.J. Ranking transitive chemical–disease inferences using local network topology in the Comparative Toxicogenomics Database. PLoS One. 2012; 7: e46524. 23144783 PMC3492369
  20. [22]Kanehisa M., Sato Y., Furumichi M., Morishima K., Tanabe M. New approach for understanding genome variations in KEGG. Nucleic Acids Res. 2019; 47: D590– D595. 30321428 PMC6324070
  21. [23]Jassal B., Matthews L., Viteri G., Gong C., Lorente P., Fabregat A., Sidiropoulos K., Cook J., Gillespie M., Haw R. et al. The Reactome pathway knowledgebase. Nucleic Acids Res. 2020; 48: D498– D503. 31691815 PMC7145712
  22. [24]Oughtred R., Stark C., Breitkreutz B-J., Rust J., Boucher L., Chang C., Kolas N., O’Donnell L., Leung G. et al. The BioGRID interaction database: 2019 update. Nucleic Acids Res. 2019; 47: D529– D541. 30476227 PMC6324058
  23. [25]Davis A.P., Wiegers T.C., King B.L., Wiegers J., Grondin C.J., Sciaky D., Johnson R.J., Mattingly C.J. Generating Gene Ontology-disease inferences to explore mechanisms of human disease at the Comparative Toxicogenomics Database. PLoS One. 2016; 11: e0155530. 27171405 PMC4865041
  24. [26]Davis A.P., Wiegers T.C., Grondin C.J., Johnson R.J., Sciaky D., Wiegers J., Mattingly C.J. Leveraging the Comparative Toxicogenomics Database to fill in knowledge gaps for environmental health: a test case for air pollution-induced cardiovascular disease. Toxicol. Sci. 2020; kfaa113. PMC7548289 32663284
  25. [27]Lipscomb C.E. Medical Subject Headings (MeSH). Bull. Med. Libr. Assoc. 2000; 88: 265– 266. 10928714 PMC35238
  26. [28]Coletti M.H., Bleich H.L. Medical subject headings used to search the biomedical literature. J. Am. Med. Inform. Assoc. 2001; 8: 317– 323. 11418538 PMC130076
  27. [30]Diehl A.D., Meehan T.F., Bradford Y.M., Brush M.H., Dahdul W.M., Dougall D.S., He Y., Osumi-Sutherland D., Ruttenberg A., Sarntivijai S. et al. The Cell Ontology 2016: enhanced content, modularization, and ontology interoperability. J. Biomed. Semantics. 2016; 7: 44. 27377652 PMC4932724

Cited by

Sorted by citations

Authors
Davis, A. P., Grondin, C. J., Johnson, R. J., Sciaky, D., Wiegers, J., Wiegers, T. C., & Mattingly, C. J.
Affiliations
North Carolina State University (US)
Corresponding author
Davis, Allan Peter
ORCID
Venue
Nucleic Acids Research
Publisher
Oxford University Press (OUP)
Published
2020-10-17
Volume
49
Issue
D1
Pages
D1138-D1143
ISSN
  • 0305-1048 (ISSN-L)
  • 1362-4962
DOI
10.1093/nar/gkaa891
PMC
PMC7779006
OpenAlex
W3093194543
Cited by
1,107
References
3
Citations by year
2026: 98 · 2025: 174 · 2024: 208 · 2023: 248 · 2022: 266 · 2021: 111 · 2020: 1
FWCI
53.05
Type
journal article
Language
en
Open access
Licence
Locations
  • Nucleic Acids Research (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • National Institute of Environmental Health Sciences (R01 ES014065, R01 ES023788, P30 ES025128)
  • National Institutes of Health
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
Keywords
Comparative Toxicogenomics Database, toxicogenomics, chemical exposure, gene-disease association, cross-species data integration, knowledge base, environmental health, chemical synonyms, hypothesis generation
MeSH terms
Genotype, Humans, Organ Specificity, Phenotype, Software, Xenobiotics, Genome, Human, Databases, Factual, Internet, Genomics, Databases, Genetic, Toxicogenetics, Knowledge Bases, Prescription Drugs, Gene-Environment Interaction, Databases, Chemical

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review