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Good BM, Van Auken K, Hill DP, et al. Reactome and the Gene Ontology: Digital convergence of data resources. Bioinformatics. 2021;doi: 10.1093/bioinformatics/btab325.
Good, B. M., Van Auken, K., Hill, D. P., Mi, H., Carbon, S., Balhoff, J. P., Albou, L. P., Thomas, P. D., Mungall, C. J., Blake, J. A., & D'Eustachio, P. (2021). Reactome and the Gene Ontology: Digital convergence of data resources. Bioinformatics (Oxford, England), . https://doi.org/10.1093/bioinformatics/btab325
Good, Benjamin M, et al. "Reactome and the Gene Ontology: Digital convergence of data resources." Bioinformatics (Oxford, England) vol. (2021). doi: https://doi.org/10.1093/bioinformatics/btab325
Good BM, Van Auken K, Hill DP, Mi H, Carbon S, Balhoff JP, Albou LP, Thomas PD, Mungall CJ, Blake JA, D'Eustachio P. Reactome and the Gene Ontology: Digital convergence of data resources. Bioinformatics. 2021 May 08; doi: 10.1093/bioinformatics/btab325. Epub 2021 May 08. PMID: 33964129; PMCID: PMC8504636.
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Bioinformatics. 2021 May 08; doi: 10.1093/bioinformatics/btab325. Epub 2021 May 08.

Reactome and the Gene Ontology: Digital convergence of data resources.

Bioinformatics (Oxford, England)

Benjamin M Good, Kimberly Van Auken, David P Hill, Huaiyu Mi, Seth Carbon, James P Balhoff, Laurent-Philippe Albou, Paul D Thomas, Christopher J Mungall, Judith A Blake, Peter D'Eustachio

Affiliations

  1. Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley CA 94720 USA.
  2. Division of Biology and Biological Engineering, California Institute of Technology, Pasadena CA 91125 USA.
  3. The Jackson Laboratory, Bar Harbor ME 04609 USA.
  4. Division of Bioinformatics, Department of Preventive Medicine, University of Southern California, Los Angeles CA 90033 USA.
  5. Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27517 USA.
  6. Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York NY 10016 USA.

PMID: 33964129 PMCID: PMC8504636 DOI: 10.1093/bioinformatics/btab325

Abstract

MOTIVATION: GO Causal Activity Models (GO-CAMs) assemble individual associations of gene products with cellular components, molecular functions, and biological processes into causally linked activity flow models. Pathway databases such as the Reactome Knowledgebase create detailed molecular process descriptions of reactions and assemble them, based on sharing of entities between individual reactions into pathway descriptions.

RESULTS: To convert the rich content of Reactome into GO-CAMs, we have developed a software tool, Pathways2GO, to convert the entire set of normal human Reactome pathways into GO-CAMs. This conversion yields standard GO annotations from Reactome content and supports enhanced quality control for both Reactome and GO, yielding a nearly seamless conversion between these two resources for the bioinformatics community.

SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.

© The Author(s) 2021. Published by Oxford University Press.

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