Reactome: a database of reactions, pathways and biological processes

By David Croft, Gavin O'Kelly, Guanming Wu, R. Haw, M. Gillespie, L. Matthews, M. Caudy, Phani V. Garapati, Gopal R. Gopinath, B. Jassal and 12 more. Nucleic Acids Res., 2010

1,715 citations recorded by the source corpus.

Reactome (http://www.reactome.org) is a collaboration among groups at the Ontario Institute for Cancer Research, Cold Spring Harbor Laboratory, New York University School of Medicine and The European Bioinformatics Institute, to develop an open source curated bioinformatics database of human pathways and reactions. Recently, we developed a new web site with improved tools for pathway browsing and data analysis. The Pathway Browser is an Systems Biology Graphical Notation (SBGN)-based visualization system that supports zooming, scrolling and event highlighting. It exploits PSIQUIC web services to overlay our curated pathways with molecular interaction data from the Reactome Functional Interaction Network and external interaction databases such as IntAct, BioGRID, ChEMBL, iRefIndex, MINT and STRING. Our Pathway and Expression Analysis tools enable ID mapping, pathway assignment and overrepresentation analysis of user-supplied data sets. To support pathway annotation and analysis in other species, we continue to make orthology-based inferences of pathways in non-human species, applying Ensembl Compara to identify orthologs of curated human proteins in each of 20 other species. The resulting inferred pathway sets can be browsed and analyzed with our Species Comparison tool. Collaborations are also underway to create manually curated data sets on the Reactome framework for chicken, Drosophila and rice.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/f8dc73630864b4903f8f57b5c8d7b098eb300e4f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

In Spectroscopy

Citations in the corpus

The Comparative Toxicogenomics Database: update 2013 (2012, background): “Integration with external data sets from GO (12) and pathway annotations from KEGG (13) and Reactome (14) provides the basis for additional inferred relationships.”
STITCH 4: integration of protein–chemical interactions with user data (2013, background): “The second channel is made up of manually curated drug–target databases: DrugBank (12), GLIDA (13), Matador (14), TTD (15) and CTD (16); and pathway databases: KEGG (17), NCI/Nature Pathway Interaction Database (18), Reactome (19) and BioCyc (20) .”
Pathway and Network Analysis of Cancer Genomes (2015, neutral): “ReactomeFIViz (previously called Reactome FI Plugin) runs in Cytoscape and features a number of algorithms for clustering and annotating sets of interacting genes, and relating these clusters to tumor phenotype and patient clinical characteristics.”
PANTHER in 2013: modeling the evolution of gene function, and other gene attributes, in the context of phylogenetic trees (2012, background): “PANTHER Pathway supports BioPAX, and therefore is able to import pathways from any pathway resources that support this standard, such as WikiPathways (28), Reactome (29), Pathway Interaction Database (30) and BioCyc (31).”
WikiPathways: building research communities on biological pathways (2011, methodology): “The Reactome database is currently in the process of setting up this workflow using WikiPathways to improve their ability to collect community contributions (26).”
The Reactome pathway knowledgebase (2013, background): “As described previously (15), the physical entities and their interactions that”
Perturbation-response genes reveal signaling footprints in cancer gene expression (2016, neutral): “2005) on Gene Ontology categories (Gene Ontology Consortium 2004) or pathway resources such as Reactome (Croft et al. 2011).”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “Name Content Website References IPAVS Signaling pathway resources http://ipavs.cidms.org Sreenivasaiah et al., 2012 Reactome http://reactome.org Croft et al., 2011 NCI Nature–Pathway Interaction Database http://pid.nci.nih.gov Schaefer et al., 2009 NetPath http://netpath.org Kandasamy et al., 2010…”
Network-based in silico drug efficacy screening (2016, neutral): “10 (cid:2) 27 ) suggests that direct targeting of known disease proteins is a rare but important therapeutic component in disease treatment.”
KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases (2011, background): “Speciﬁcally, KEGG PATHWAY (3) and Reactome (29,30) are general pathway databases, whereas PID (27) and Panther (31) focus on signaling pathways and BioCyc (28) focuses on metabolic pathways.”
Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory (2016, methodology): “Pathways are annotated using DAVID and CPDB. Top-ranking pathways in REACTOME [39] and KEGG [40] with p -values of less than 0.05 are reported.”
Fast and Rigorous Computation of Gene and Pathway Scores from SNP-Based Summary Statistics (2016, methodology): “Genes are commonly assigned to pathways using wellestablished databases (such as Gene Ontology[6], KEGG[7], PANTHER[8], REACTOME[9], BIOCARTA[10]) or in-house annotation (based on co-expression[4], for example).”