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EMAGE (http://www.emouseatlas.org/emage/) is a freely available database of in situ gene expression patterns that allows users to perform online queries of mouse developmental gene expression. EMAGE is unique in providing both text-based descriptions of gene expression plus spatial maps of gene expression patterns. This mapping allows spatial queries to be accomplished alongside more traditional text-based queries. Here, we describe our recent progress in spatial mapping and data integration. EMAGE has developed a method of spatially mapping 3D embryo images captured using optical projection tomography, and through the use of an IIP3D viewer allows users to view arbitrary sections of raw and mapped 3D image data in the context of a web browser. EMAGE now includes enhancer data, and we have spatially mapped images from a comprehensive screen of transgenic reporter mice that detail the expression of mouse non-coding genomic DNA fragments with enhancer activity. We have integrated the eMouseAtlas anatomical atlas and the EMAGE database so that a user of the atlas can query the EMAGE database easily. In addition, we have extended the atlas framework to enable EMAGE to spatially cross-index EMBRYS whole mount in situ hybridization data. We additionally report on recent developments to the EMAGE web interface, including new query and analysis capabilities.

The EMAGE (Electronic Mouse Atlas of Gene Expression) database (http://www.emouseatlas.org/emage) allows users to perform on-line queries of mouse developmental gene expression. EMAGE data are represented spatially using a framework of 3D mouse embryo models, thus allowing uniquely spatial queries to be carried out alongside more traditional text-based queries. This spatial representation of the data also allows a comparison of spatial similarity between the expression patterns. The data are mapped to the models by a team of curators using bespoke mapping software, and the associated meta-data are curated for accuracy and completeness. The data contained in EMAGE are gathered from three main sources: from the published literature, through large-scale screens and collaborations, and via direct submissions from researchers. There are a variety of ways to query the EMAGE database via the on-line search interfaces, as well as via direct computational script-based queries. EMAGE is a free, on-line, community resource funded by the Medical Research Council, UK.

EMAGE (http://www.emouseatlas.org/emage) is a freely available online database of in situ gene expression patterns in the developing mouse embryo. Gene expression domains from raw images are extracted and integrated spatially into a set of standard 3D virtual mouse embryos at different stages of development, which allows data interrogation by spatial methods. An anatomy ontology is also used to describe sites of expression, which allows data to be queried using text-based methods. Here, we describe recent enhancements to EMAGE including: the release of a completely re-designed website, which offers integration of many different search functions in HTML web pages, improved user feedback and the ability to find similar expression patterns at the click of a button; back-end refactoring from an object oriented to relational architecture, allowing associated SQL access; and the provision of further access by standard formatted URLs and a Java API. We have also increased data coverage by sourcing from a greater selection of journals and developed automated methods for spatial data annotation that are being applied to spatially incorporate the genome-wide (approximately 19,000 gene) 'EURExpress' dataset into EMAGE.

EMAGE (http://genex.hgu.mrc.ac.uk/Emage/database) is a database of in situ gene expression patterns in the developing mouse embryo. Domains of expression from raw data images are spatially integrated into a set of standard 3D virtual mouse embryos at different stages of development, allowing data interrogation by spatial methods. Sites of expression are also described using an anatomy ontology and data can be queried using text-based methods. Here we describe recent enhancements to EMAGE which include advances in spatial search methods including: a refined local spatial similarity search algorithm, a method to allow global spatial comparison of patterns in EMAGE and subsequent hierarchical-clustering, and spatial searches across multiple stages of development. In addition, we have extended data access by the introduction of web services and new HTML-based search interfaces, which allow access to data that has not yet been spatially annotated. We have also started incorporating full 3D images of gene expression that have been generated using optical projection tomography (OPT).

EMAGE (http://genex.hgu.mrc.ac.uk/Emage/database) is a freely available, curated database of gene expression patterns generated by in situ techniques in the developing mouse embryo. It is unique in that it contains standardized spatial representations of the sites of gene expression for each gene, denoted against a set of virtual reference embryo models. As such, the data can be interrogated in a novel and abstract manner by using space to define a query. Accompanying the spatial representations of gene expression patterns are text descriptions of the sites of expression, which also allows searching of the data by more conventional text-based methods.