Motivation: Due to different experimental setups and various interpretations of results, the data contained in online bioinformatics resources can be inconsistent, therefore, making it more difficult for users of these resources to assess the suitability and correctness of the answers to their queries. This work investigates the role of argumentation systems to help users evaluate such answers. More specifically, it looks closely at a gene expression case study, creating an appropriate representation of the underlying data and series of rules that are used by a third-party argumentation engine to reason over the query results provided by the mouse gene expression database EMAGE.Results: A prototype using the ASPIC argumentation engine has been implemented and a preliminary evaluation carried out. This evaluation suggested that argumentation can be used to deal with inconsistent data in biological resources.Availability: The ASPIC argumentation engine is available from http://www.argumentation.org. EMAGE gene expression data can be obtained from http://genex.hgu.mrc.ac.uk. The argumentation rules for the gene expression example are available from the lead author upon request.Contact: kcm1@hw.ac.uk

BackgroundA key application area of semantic technologies is the fast-developing field of bioinformatics. Sealife was a project within this field with the aim of creating semantics-based web browsing capabilities for the Life Sciences. This includes meaningfully linking significant terms from the text of a web page to executable web services. It also involves the semantic mark-up of biological terms, linking them to biomedical ontologies, then discovering and executing services based on terms that interest the user.ResultsA system was produced which allows a user to identify terms of interest on a web page and subsequently connects these to a choice of web services which can make use of these inputs. Elements of Artificial Intelligence Planning build on this to present a choice of higher level goals, which can then be broken down to construct a workflow. An Argumentation System was implemented to evaluate the results produced by three different gene expression databases. An evaluation of these modules was carried out on users from a variety of backgrounds. Users with little knowledge of web services were able to achieve tasks that used several services in much less time than they would have taken to do this manually. The Argumentation System was also considered a useful resource and feedback was collected on the best way to present results.ConclusionOverall the system represents a move forward in helping users to both construct workflows and analyse results by incorporating specific domain knowledge into the software. It also provides a mechanism by which web pages can be linked to web services. However, this work covers a specific domain and much co-ordinated effort is needed to make all web services available for use in such a way, i.e. the integration of underlying knowledge is a difficult but essential task.

BackgroundHigh throughput imaging is now available to many groups and it is possible to generate a large quantity of high quality images quickly. Managing this data, consistently annotating it, or making it available to the community are all challenges that come with these methods.ResultsPhenoImageShare provides an ontology-enabled lightweight image data query, annotation service and a single point of access backed by a Solr server for programmatic access to an integrated image collection enabling improved community access. PhenoImageShare also provides an easy to use online image annotation tool with functionality to draw regions of interest on images and to annotate them with terms from an autosuggest-enabled ontology-lookup widget. The provenance of each image, and annotation, is kept and links to original resources are provided. The semantic and intuitive search interface is species and imaging technology neutral. PhenoImageShare now provides access to annotation for over 100,000 images for 2 species.ConclusionThe PhenoImageShare platform provides underlying infrastructure for both programmatic access and user-facing tools for biologists enabling the query and annotation of federated images. PhenoImageShare is accessible online at http://www.phenoimageshare.org.

The analysis of gene expression data is a complex task for biologists wishing to understand the role of genes in the formation of diseases such as cancer. Biologists need greater support when trying to discover, and comprehend, new relationships within their data. In this paper, we describe an approach to the analysis of gene expression data where overlapping groupings are generated by Formal Concept Analysis and interactively analyzed in a tool called CUBIST. The CUBIST workflow involves querying a semantic database and converting the result into a formal context, which can be simplified to make it manageable, before it is visualized as a concept lattice and associated charts.

Abstract. This paper develops some existing ideas in FCA to provide an analysis of a large data set of mouse embryo gene expressions. It develops new techniques for managing complexity and visualisation in FCA to identify and approximate large groups of co-expressed genes. This work has been carried out as part the European CUBIST Project: http://www.cubist-project.eu/