The Reactome pathway Knowledgebase

By A. Fabregat, K. Sidiropoulos, Phani V. Garapati, M. Gillespie, Kerstin Hausmann, R. Haw, B. Jassal, S. Jupe, Florian Korninger, S. McKay and 12 more. 2015

1,977 citations recorded by the source corpus.

The Reactome Knowledgebase (www.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 pattern surveys or somatic mutation catalogues from tumour cells. Over the last two years we redeveloped major components of the Reactome web interface to improve usability, responsiveness and data visualization. A new pathway diagram viewer provides a faster, clearer interface and smooth zooming from the entire reaction network to the details of individual reactions. Tool performance for analysis of user datasets has been substantially improved, now generating detailed results for genome-wide expression datasets within seconds. The analysis module can now be accessed through a RESTFul interface, facilitating its inclusion in third party applications. A new overview module allows the visualization of analysis results on a genome-wide Reactome pathway hierarchy using a single screen page. The search interface now provides auto-completion as well as a faceted search to narrow result lists efficiently.

How researchers see this paper

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

Citations in the corpus

STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets (2018, methodology): “The database channel is based on manually curated interaction records assembled by expert curators, at KEGG (41), Reactome (50), BioCyc (51) and Gene Ontology (52), as well as legacy datasets from PID and BioCarta.”
Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets (2018, methodology): “Gene set enrichment analysis Gene set enrichment analysis was performed based on Reactome gene sets (Fabregat et al , 2015) as described above.”