Nucleic Acids Research, 2011, Vol. 39, Database issue D695 Figure 3. The pathway and expression analysis tools. (A) The results table for the ‘ID mapping and pathway assignment’. The sortable table contains one row for each Reactome pathway and four additional columns: repeats the user-supplied IDs, the corresponding UniProt ID, the species name and names of pathways in which this ID can be found. The names/IDs in the last two columns are clickable links that take the user to a diagram of the named pathway. (B) The results for the ‘overrepresentation analysis’, presented as a list of clickable links of enriched events. The warmer the color, the higher the level of overrepresentation in the given pathway. Clicking on the ‘+’ next to the pathway name gives access to the user-supplied identiﬁers that are found in the pathway, along with the corresponding UniProt IDs. (C) The results table for the expression analysis. The sortable table contains one row for each Reactome pathway and ﬁve additional columns: name of the pathway, Species of presented results, total number of proteins in pathway, number of proteins in the user-supplied data that fall into the pathway, graphical representation of the ratio of these two values and a ‘View’ button that creates a pathway diagram. (D) The pathway browser displaying the colored physical entities that correspond to expression values of the experimental data. The nodes in this diagram have a special color-coding: gray, no match; black, a (multicomponent) complex entity; and other colors represent expression levels. If the numerical data are a time series, the grey bar at the bottom of the colored pathway diagram allows the user to step through time points and visualize changes in expression levels with time of the individual genes involved in the pathway. those of human to ﬁnd reactions and pathways common to a selected species and human (Figure 2). Upgraded pathway and expression analysis Biologists are generating large amounts of functional data through gene expression, copy-number variation, protein– protein, protein–DNA and protein–RNA interactions, protein and metabolite abundance and large-scale DNA-sequencing experiments. Integrating this experimen- tal information with the published literature and biologic- al databases, including pathway databases, is vital to efﬁcient and effective data analysis. Previously, Reactome provided the Skypainter tool for this level of functional data analysis (1). However, with the retirement of the ‘starry-sky’ reaction map and user requests to provide an expanded suite of bioinformatics tools, we re- developed our data analysis suite to offer powerful and complementary tools. The Pathway Analysis tool analyzes user-supplied lists of genes, proteins and small molecules and provides ID mapping, pathway assignment and overrepresentation analysis (Figure 3). As with Skypainter, the pathway and expression analysis tools accept gene and protein accession numbers and identiﬁers that are associated with popular commercial platforms (e.g. Illumina, Agilent and Affymetrix). By default, the simplest of these analyses, ID mapping and pathway as- signment, is selected. This analysis takes a set of identiﬁers and maps them to Reactome pathways. The results are presented in a tabular format (Figure 3). The overrepre- sentation analysis is based upon the previously reported Skypainter tools. Both pathway analysis results also link to the new Pathway Browser. The expression analysis tool is similar in design to pathway analysis tool, but it will accept numerical values (e.g. expression, abundance, fold