The Reactome Pathway Knowledgebase

By A. Fabregat, S. Jupe, L. Matthews, K. Sidiropoulos, M. Gillespie, Phani V. Garapati, R. Haw, B. Jassal, Florian Korninger, Bruce May and 13 more. 2017

1,895 citations recorded by the source corpus.

Abstract The Reactome Knowledgebase (https://reactome.org) provides molecular details of signal transduction, transport, DNA replication, metabolism, and other cellular processes as an ordered network of molecular transformations—an extended version of a classic metabolic map, in a single consistent data model. Reactome functions both as an archive of biological processes and as a tool for discovering unexpected functional relationships in data such as gene expression profiles or somatic mutation catalogues from tumor cells. To support the continued brisk growth in the size and complexity of Reactome, we have implemented a graph database, improved performance of data analysis tools, and designed new data structures and strategies to boost diagram viewer performance. To make our website more accessible to human users, we have improved pathway display and navigation by implementing interactive Enhanced High Level Diagrams (EHLDs) with an associated icon library, and subpathway highlighting and zooming, in a simplified and reorganized web site with adaptive design. To encourage re-use of our content, we have enabled export of pathway diagrams as ‘PowerPoint’ files.

Subfields: Molecular Biology

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Citations in the corpus

The Gene Ontology Resource: 20 years and still GOing strong (2018, background): “GO also maintains cross-references between terms and multiple widelyused external resources, including Reactome (21), The Annotated Reactions Database (Rhea) (22), Enzyme Commission (EC; http://www.”
Metabolomics for Investigating Physiological and Pathophysiological Processes. (2019, methodology): “When combined with additional biological knowledge found in pathway databases (143, 219, 225) or when processed using tools such as metabolite set enrichment analysis (523, 524) and biological network modeling (272), these data can be used to gain key insights into how certain metabolites contribute”
Deregulation of the Histone Lysine-Specific Demethylase 1 Is Involved in Human Hepatocellular Carcinoma (2019, methodology): “Then, we conducted gene set enrichment analysis and pathway analysis using Reactome in order to identify the signal pathways regulated by LSD1 in HCC [39].”
Predicting transcriptional outcomes of novel multigene perturbations with GEARS (2023, positive): “GEARS functionalizes this graph-based inductive bias using a graph neural network (GNN) architecture 33 .”
Current best practices in single‐cell RNA‐seq analysis: a tutorial (2019, methodology): “Biological process labels are stored in databases such as MSigDB (Liberzon et al , 2011), the Gene Ontology (Ashburner et al , 2000; The Gene Ontology Consortium, 2017), or the pathway databases KEGG (Kanehisa et al , 2017) and Reactome (Fabregat et al , 2018).”
PDBe: improved findability of macromolecular structure data in the PDB (2019, neutral): “PDBe has worked with UniProt, Complex Portal (42), Reactome (43) and Ensembl (15) to support integration of LiteMol on their respective websites, placing the macromolecular structure within a context that is familiar to users of these resources.”
SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics (2019, methodology): “We also extracted LR pairs, with respective participants annotated as ligand or receptor in GO (31), from Reactome (32) pathways.”
CancerSEA: a cancer single-cell state atlas (2018, background): “Users can input the gene list of their interest, or select pre-specified lists (including cancer hallmark-related functions/pathways from GO (8,23,24), MSigDB (18), KEGG and Reactome (25,26)).”
Complex Portal 2018: extended content and enhanced visualization tools for macromolecular complexes (2018, background): “It captures complex composition, topology and function and links out to a large range of domain-specific resources that hold more detailed data, such as PDB or Reactome.”
The Comparative Toxicogenomics Database: update 2019 (2018, background): “External integration of CTD content with annotations from Gene Ontology (29), KEGG (30), Reactome (31) and BioGRID (32) produces an additional 9.4 million inferences (Table 1).”