Integrating partonomic hierarchies in anatomy ontologies

<p>Abstract</p> <p>Background</p> <p>Anatomy ontologies play an increasingly important role in developing integrated bioinformatics applications. One of the primary relationships between anatomical tissues represented in such ontologies is <it>part-of</it>. As there are a number of ways to divide up the anatomical structure of an organism, each may be represented by more than one valid partonomic (part-of) hierarchy. This raises the issue of how to represent and integrate multiple such hierarchies.</p> <p>Results</p> <p>In this paper we describe a solution that is based on our work on an anatomy ontology for mouse embryo development, which is part of the Edinburgh Mouse Atlas Project (EMAP). The paper describes the basic conceptual aspects of our approach and discusses strengths and limitations of the proposed solution. A prototype was implemented in Prolog for evaluation purposes.</p> <p>Conclusion</p> <p>With the proposed name set approach, rather than having to standardise hierarchies, it is sufficient to agree on a suitable set of basic tissue terms and their meaning in order to facilitate the integration of multiple partonomic hierarchies.</p

EMAGE: a spatial database of gene expression patterns during mouse embryo development

EMAGE () 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

EMAGE: a spatial database of gene expression patterns during mouse embryo development

EMAGE () 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

The BioMart interface to the eMouseAtlas gene expression database EMAGE

Here, we describe the BioMart interface to the eMouseAtlas gene expression database EMAGE. EMAGE is a spatiotemporal database of in situ gene expression patterns in the developing mouse embryo. BioMart provides a generic web query interface and programmable access using web services. The BioMart interface extends access to EMAGE via a powerful method of structuring complex queries and one with which users may already be familiar with from other BioMart implementations. The interface is structured into several data sets providing the user with comprehensive query access to the EMAGE data. The federated nature of BioMart allows scope for integration and cross querying of EMAGE with other similar BioMarts

eMouseAtlas, EMAGE, and the spatial dimension of the transcriptome

Abstract eMouseAtlas (www.emouseatlas.org) is a com-prehensive online resource to visualise mouse development and investigate gene expression in the mouse embryo. We have recently deployed a completely redesigned Mouse Anatomy Atlas website (www.emouseatlas.org/emap/ema) that allows users to view 3D embryo reconstructions, delineated anatomy, and high-resolution histological sec-tions. A new feature of the website is the IIP3D web tool that allows a user to view arbitrary sections of 3D embryo reconstructions using a web browser. This feature provides interactive access to very high-volume 3D images via a tiled pan-and-zoom style interface and circumvents the need to download large image files for visualisation. eMouseAtla

EMAGE—Edinburgh Mouse Atlas of Gene Expression: 2008 update

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 mouse embryo spatial gene expression database: 2010 update

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 (∼19 000 gene) ‘EURExpress’ dataset into EMAGE