Reactome: a knowledge base of biologic pathways and processes

By Imre Vastrik, P. D’Eustachio, E. Schmidt, G. Joshi-Tope, Gopal R. Gopinath, David Croft, B. de Bono, M. Gillespie, B. Jassal, Suzanna Lewis and 4 more. Genome Biology, 2007

705 citations recorded by the source corpus.

Reactome http://www.reactome.org, an online curated resource for human pathway data, provides infrastructure for computation across the biologic reaction network. We use Reactome to infer equivalent reactions in multiple nonhuman species, and present data on the reliability of these inferred reactions for the distantly related eukaryote Saccharomyces cerevisiae. Finally, we describe the use of Reactome both as a learning resource and as a computational tool to aid in the interpretation of microarrays and similar large-scale datasets.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/90d0cfe5bd7803aaeb41d7938913aaf207cafa02

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Citations in the corpus

Biomedical text mining and its applications in cancer research (2013, neutral): “Reactome [160–162] Reactome is an open-source data analysis tools, as well as a manually curated and peer-reviewed database including interaction, reaction and”
KEGG Atlas mapping for global analysis of metabolic pathways (2008, background): “In KEGG and other pathway databases (2,3), experimental knowledge is organized and diagramed as smaller networks, and web interfaces and visualization tools have been developed to overview and analyze computationally generated global networks (3–6).”
MetaboLights—an open-access general-purpose repository for metabolomics studies and associated meta-data (2012, background): “In the future, such metabolite-centric views will be extended to show metabolites in the context of pathways, harnessing the Reactome database of biochemical pathways (20).”
The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function (2010, background): “Data sets are collected from publicly available databases, including co-expression data from Gene Expression Omnibus (GEO) (1); physical and genetic interaction data from BioGRID (2); predicted protein interaction data based on orthology from I2D (3); and pathway and molecular interaction data from ”
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).”
Phospho.ELM: a database of phosphorylation sites—update 2008 (2007, background): “For example, resources such as KEGG (32) and Reactome (33) that annotate cell signaling networks are increasing their pathway coverage and it will clearly become essential to provide links to such resources.”
ConsensusPathDB—a database for integrating human functional interaction networks (2008, background): “Currently, the database contains human functional interactions, including gene regulations, physical (protein–protein and protein-compound) interactions and biochemical (signaling and metabolic) reactions, obtained by integrating such data from 12 publicly accessible databases (referred to as source”
Babelomics: an integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling (2010, background): “com/), Interpro (4), reactome (5)], regulatory information [Transfac (6), Jaspar (7), ORegAnno (8), miRNA target genes from miRBase (35)], protein–protein interactions (9) and text-mining module definitions (10).”
BisoGenet: a new tool for gene network building, visualization and analysis (2010, methodology): “On the other hand databases like KEGG[9], Reactome[10], BioCyc[11], NCI Nature PID[12] and others provide information on both metabolic and signaling pathways.”
IFITM Proteins Mediate the Innate Immune Response to Influenza A H1N1 Virus, West Nile Virus and Dengue Virus (2009, methodology): “Genes were mapped to these keywords using a database that integrates annotation information from UniProt (Bairoch et al., 2005), KEGG (Kanehisa et al., 2004), Reactome (Vastrik et al., 2007), Gene Ontology (Ashburner et al., 2000), and NCBI GeneRIF (Mitchell et al., 2003); in addition, OMIM Human or”
A human functional protein interaction network and its application to cancer data analysis (2010, methodology): “Skypainter is a web application implemented in the Reac-tome web site for gene or protein over-representation analysis [23].”
Comparative Toxicogenomics Database: a knowledgebase and discovery tool for chemical–gene–disease networks (2008, background): “KEGG (21) and Reactome (22) map chemicals, genes and (in the case of KEGG) disease information to pathways, but the pathways and interactions are generically applied to orthologous proteins and all species, and it is not always clear which reference supports which pathway relationship.”