D692 Nucleic Acids Research, 2011, Vol. 39, Database issue all-inclusive resource of human pathways for basic research, genome analysis, pathway modeling, systems biology and education. In the past 2 years, the Reactome data set has nearly doubled in size and new tools for data aggregation and data analysis have become available. To support the continued development of the Reactome knowledgebase, we have redesigned the Reactome web site and data analysis software. Expanded pathway coverage Reactome’s recruitment of expert authors and curators has given us access to key aspects of human biology. The current release of Reactome (Version 34, September 2010) describes the roles of 5272 human proteins (26% of the 20 286 human SwissProt entries) and 3504 macromol- ecular complexes in 3847 reactions organized into 1057 pathways. Over the last year, we have added new higher order topics. Notable additions include the molecular anatomy of transcriptional regulation, a largely complete catalogue of receptors with known ligands involved in GPCR signaling (10), Toll-like Receptors, Chromosome maintenance, Olfactory Signaling, Myogenesis, N-Glycan biosynthesis and Metabolism of RNA. Reactome has prototyped additional types of annotations to support pathway curation. We have curated pathways relating to insulin signaling cycle to prototype pathway–disease anno- tations. We have also developed a framework for physio- logical process annotations, e.g. vesicle transport and glutamate-mediated neurotransmission. To support the creation of the new pathway diagrams, we deﬁned canon- ical pathways corresponding to 161 discrete biological domains. This enabled a simpliﬁcation of the event hier- archy in the new Pathway Browser and has minimized event sharing across different pathways. Visualization features were implemented in the Author and Curator Tool to enable the layout and editing of these new pathways diagrams as part of our curation process. The Reactome data model has been extended to the manual curation of pathways in model organisms. Gallus Reactome (http://gallus.reactome.org), an effort led by Carl Schmidt of the University of Delaware has been modeled after Reactome. The ﬁrst public release was in mid-2009 and now Gallus Reactome includes an- notations for 127 reactions involving 133 Gallus proteins in the domains of intermediary metabolism and DNA repair. A collaboration among Reactome, Michael Ashburner and Mark Williams at Cambridge University, similarly uses Reactome software and hardware to create and maintain a Drosophila pathway database (http://ﬂy. reactome.org/). Its third release went public in mid-2010, and includes data for Wingless, JAK/STAT, Imd, Toll, Hedgehog, Circadian Clock, Hippo/Warts and Planar Cell Polarity signaling pathways. Redesigned reactome web site The rapid growth in content has made the ‘starry sky’ reaction map display unwieldy as a navigation and visu- alization tool. At the same time, our outreach has grown to encompass diverse user groups interested in browsing a particular process or protein as a textbook, analyzing high-throughput expression data sets, data mining and data aggregation and online resources for education. Our front page has thus been redesigned to support quick, intuitive access to our data and tools as both features of the knowledgebase continue to grow. The new web site retains a comprehensive top menu bar that provides access to all of our tools and resources. It is now accompanied by a sidebar that provides access to basic, widely used tools for pathway browsing and data analysis, and panels that give a thumbnail overview of Reactome information, tutorials, recent news and a view of a recently added pathway of topical interest. Improved pathway visualization Visualization of full pathway information in a consistent format is vital to support the pathway-based analysis of complex experimental and computational data sets. To support such visual navigation and analysis of Reactome data we have developed, in collaboration with the ENFIN project (11), a new Pathway Browser based upon the Systems Biology Graphical Notation (SBGN) (12). SBGN is a standard graphical representation of biological pathway and network models, i.e. every molecule and reaction has a particular shape, color and cellular location. Our entire content has been organized into 161 canonical pathways, each displayed in this format. The Reactome Pathway Browser consists of four key elements. First, the ‘Search’ bar at the top of the page queries the entire Reactome database. Second, the ‘Pathways’ panel provides a scrolling display of the Reactome canonical pathway hierarchy. Third, clicking on the pathway name displays the corresponding pathway diagram in the ‘Visualization’ panel on the right side. The ‘Visualization’ panel offers interactive and dynamic pathway diagrams permitting zooming, scrolling and highlighting of events and molecules. Fourth, clicking on events and molecules in the pathway diagram uncovers a ‘Details’ panel below the pathway diagram with additional textual information about the events and molecules, respectively. Further functionality is provided in the form of context sensitive menus within the ‘Visualization’ panel (Figure 1). The precise features of the context sensitive menus are determined by the nature of the physical entity (small molecule, protein, complex): (i) a list of the other pathways in Reactome in which the selected entity participates; (ii) a display of the physical entities that contribute to the macromolecular complex; and, optionally (iii) a list of interactors of the entity from selected interaction databases (described later). The pathway diagrams are available for download as static PNG and PDF ﬁles. Dynamic pathway images compatible with third-party tools like Cytoscape (13) and CellDesigner (14) are currently being developed. Integrating molecular interactions and networks onto reactome pathway diagrams The Reactome data sets are a highly reliable platform for pathway-based data analysis but suffer from a low cover- age of human proteins. To increase protein coverage and associated protein annotations, we have integrated