Reactome: a knowledgebase of biological pathways

By Lincoln D. Stein, Peterd ' Eustachio, Gopal Gopinathrao, M. Gillespie, L. Matthews, Guanming Wu, Imre Vastrik, E. Schmidt, B. de Bono, B. Jassal and 3 more. Nucleic Acids Res., 2004

1,422 citations recorded by the source corpus.

Reactome, located at http://www.reactome.org is a curated, peer-reviewed resource of human biological processes. Given the genetic makeup of an organism, the complete set of possible reactions constitutes its reactome. The basic unit of the Reactome database is a reaction; reactions are then grouped into causal chains to form pathways. The Reactome data model allows us to represent many diverse processes in the human system, including the pathways of intermediary metabolism, regulatory pathways, and signal transduction, and high-level processes, such as the cell cycle. Reactome provides a qualitative framework, on which quantitative data can be superimposed. Tools have been developed to facilitate custom data entry and annotation by expert biologists, and to allow visualization and exploration of the finished dataset as an interactive process map. Although our primary curational domain is pathways from Homo sapiens, we regularly create electronic projections of human pathways onto other organisms via putative orthologs, thus making Reactome relevant to model organism research communities. The database is publicly available under open source terms, which allows both its content and its software infrastructure to be freely used and redistributed.

Subfields: Molecular Biology

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Citations in the corpus

NetPath: a public resource of curated signal transduction pathways (2010, methodology): “Reactome [16] Yes No Yes BioPAX, SBML, PDF, SVG, Protégé, MySQL database dump Yes Also contains computationally inferred pathway reactions”
Improved human disease candidate gene prioritization using mouse phenotype (2007, methodology): “Pathway: Gene-pathway annotations were compiled by combining data from KEGG [35], BioCarta [36], BioCyc [37], Reactome [38], GenMAPP [39], and MSigDb [40,3].”
PROGgeneV2: enhancements on the existing database (2014, background): “These signatures include those from curated databases such as Gene Ontology [3], KEGG [4], Biocarta and Reactome [5] and from published studies.”
The Reactome Pathway Knowledgebase 2024 (2023, background): “…was envisioned as building a comprehensive parts list of all reactions enabled by human proteins, onto which a user could overlay a list of proteins expressed in a tissue of interest under physiological conditions of interest to infer a tissue-and condition-specific reaction network ( 7 ,8 ).”
BIOLOGICAL NETWORK EXPLORATION WITH CYTOSCAPE 3 (2014, background): “…above, there are many other Cytoscape 3 apps that enables data importing from data repositories such as Reactome (Joshi-Tope and Gillespie, 2005), KEGG (Kanehisa, 2002), WikiPathways (Kelder et al. , 2012), MetScape (Karnovsky et al. , 2012), Agilent Literature Search (Vailaya et al. , 2005).”
The Universal Protein Resource (UniProt): an expanding universe of protein information (2005, background): “Reactome ( 17 )]. A document listing all databases cross-referenced in UniProt (http://www.uniprot.org/support/docs/dbxref.html) is available and contains, for each database, a short description and the server URL.”
Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool (2013, methodology): “Finally, to demonstrate how Enrichr can be applied globally to obtain a regulatory picture of cancer cell lines and their corresponding normal tissues, we used nine gene sets from the CCLE gene-set library and matching nine gene sets from the Human Gene Atlas library to perform enrichment analysis u”
MuSiC: Identifying mutational significance in cancer genomes (2012, methodology): “PathScan treats pathways as groups of genes defined by databases such as KEGG (Kanehisa and Goto 2000), BioCarta (Nishimura 2001), and Reactome (Joshi-Tope et al. 2005), with the KEGG definitions currently set as the default implementation.”
Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation (2010, background): “Gene-sets can be defined based on participation in a metabolic or signaling pathway (e.g. KEGG [14], Reactome [15]), targeting by gene expression regulators (e.g. microRNA, transcription factors), protein features such as domains, chromosomal location and association to specific diseases, stimuli,…”
Biomedical ontologies: a functional perspective (2007, methodology): “Currently, leading pathway resources such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) [32], BioCyc [33] and Reactome [34] make their data available in BioPAX format and BioPAX viewers are available as additional modules in pathway analysis tools such as PATIKA [35] and Cytoscape [36].”
FlyMine: an integrated database for Drosophila and Anopheles genomics (2007, methodology): “While it will not always make sense to integrate tools closely with FlyMine, it is easy to generate different tabular data formats, and we aim to make it as easy as possible to export data for use in other applications, or to render sets of objects (for example, genes) through other web resources su”
Large-scale integration of small molecule-induced genome-wide transcriptional responses, Kinome-wide binding affinities and cell-growth inhibition profiles reveal global trends characterizing systems-level drug action (2014, methodology): “We identiﬁed and downloaded 213 proteins (including cellular localization variation) from PI3K/AKT/mTOR pathway from Reactome (Joshi-Tope et al., 2005; Vastrik et al., 2007).”