When to use Reactome
If you are interested in a particular pathway or protein, Reactome is a highly useful starting point for:
- identifying the full molecular details of the pathway;
- learning the functions of a protein.
You will most probably start by performing a simple text search, using a descriptive name (e.g. TP53) or the accession number (e.g. P04637) of a protein or compound. The structured results and filtering tools, plus links to many other external resources, should enable you to quickly gain a detailed understanding of your query subject.
- high-throughput datasets,
- lists of genes, proteins and/or componds,
- lists with associated quantitative data such as expression, concentration or frequency
can make use of Reactome's analytical tools. These lists can be mapped to Reactome pathways, or submitted for over-representation analysis, which will identify any significant enrichment for particular biological processes in the query set. Lists with associated numerical values, such as expression levels, can be submitted using the Analyse Expression Data tool, which will overlay a colour-scale indication onto Reactome pathways.
Biologists with an interest in:
- extending a pathway,
- studying the interactions of a pathway with other proteins or componds
When not to use Reactome
- Reactome is not a database of protein–protein interactions, although it does include tools that can make use of these data. If your interest is in raw protein–protein interaction data, rather than established molecular interactions and pathways, we recommend that you use one of the resources optimised for this purpose, such as IntAct. You may find our course IntAct: Molecular Interactions at the EBI useful.
- Reactome pathways can be extended using the Molecular Interactions tool, but Reactome is not intended as a viewer for large interaction networks. If this is your goal, you may wish to use a visualisation tool such as Cytoscape.
- Reactome does not include tools for Gene Ontology Enrichment Analysis (GSEA). Many tools exist for this purpose including the DAVID website. For a large selection, we recommend that you browse the list on the Gene Ontology website.