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. 2016 Jan 4;44(D1):D481-7.
doi: 10.1093/nar/gkv1351. Epub 2015 Dec 9.

The Reactome pathway Knowledgebase

Antonio Fabregat  1 , Konstantinos Sidiropoulos  1 , Phani Garapati  1 , Marc Gillespie  2 , Kerstin Hausmann  1 , Robin Haw  3 , Bijay Jassal  3 , Steven Jupe  1 , Florian Korninger  1 , Sheldon McKay  3 , Lisa Matthews  4 , Bruce May  3 , Marija Milacic  3 , Karen Rothfels  3 , Veronica Shamovsky  4 , Marissa Webber  3 , Joel Weiser  3 , Mark Williams  1 , Guanming Wu  3 , Lincoln Stein  5 , Henning Hermjakob  6 , Peter D'Eustachio  7
Affiliations

The Reactome pathway Knowledgebase

Antonio Fabregat et al. Nucleic Acids Res. .

Abstract

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.

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Figures

Figure 1.
Figure 1.
Pathway Overview. The entire pathways overview map (A). The RAF/MAP kinase cascade pathway is highlighted to show its involvement in multiple bursts (B). A zoomed-in view of the Metabolism burst showing individual subpathway groups (C).
Figure 2.
Figure 2.
Diagram viewer. The central panel shows details of reactions and participating molecules in the nine-step process of ubiquinol (ubiquinol-10, Q10H2) biosynthesis. Buttons around the panel support functions including panning and zooming (lower right), changing the view (upper left) and downloading a snapshot of the pathway (upper right).
Figure 3.
Figure 3.
Pathway browser view centred on the ‘gene expression’ top-level pathway. Access to subpathways is provided via the hierarchical display of events on the left and by clicking on event nodes in the pathway display (viewport). Details for the selected event are shown in the panel under the pathway display. Buttons at the right of the top bar show the current version of our software (3.0) with access to our Github software repository, and the current version of our data (release 54). A button in the top bar provide access to the analysis tools (see below, Figure 4). Clicking on the layout buttons closes and re-opens the hierarchical display and details panels. The ‘tour’ button provides access to a brief video tour of the main features of the web site. Clicking on the gearwheel icon in the upper right corner of the pathway diagram provides access to a tool to customize diagram colouring and to an ‘About …’ pop-up that briefly describes pathway diagram features and contains a link to the detailed users’ guide. (This guide is also accessible via the ‘documentation’ drop-down menu at the top of the home page).
Figure 4.
Figure 4.
Analysis tool user data submission interface, showing time-series data. Each row represents data for a different gene. Columns contain an identifier (probe set, gene name, etc.) on the left and expression values for four time points to the right, entered as tab-delimited text. UniProt identifiers, gene names and Affimetrix identifiers, among others, can be submitted. The ‘project to human’ box at the bottom of the form, which is selected by default, causes any non-human identifiers in the data to be replaced by their human equivalents and the latter to be used for the analysis. Instructions for formatting data and lists of acceptable identifiers are provided in the users’ guide (Figure 3).
Figure 5.
Figure 5.
Analysis results. Top panels, an analysis of a PRIDE dataset (assay 27 929—http://www.ebi.ac.uk/pride/ws/archive/protein/list/assay/27929.acc in project PXD000072—http://www.ebi.ac.uk/pride/archive/projects/PXD000072) to identify proteins over-expressed in activated human platelet releasate (5). Bottom panels, an expression analysis. Left panels show overlays on the pathways overview; right panels are an overlay of the data for a selected pathway on the pathway diagram. The details panel at the bottom lists results and statistics for each pathway, including numbers of identifiers in the submitted dataset that did not match anything in the Reactome dataset. A binomial test is used to calculate the probability shown for each result, and the P-values are corrected for the multiple testing (Benjamini–Hochberg procedure) that arises from evaluating the submitted list of identifiers against every pathway.
Figure 6.
Figure 6.
Redesigned search interface, showing term auto suggestion, grouping of results and highlighting of search terms in the results. The check boxes along the left side of the results page allow results to be further limited by species, data type, subcellular location and other parameters.
See this image and copyright information in PMC

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