Abstract As Semantic Web technologies mature and new releases of key elements, such as SPARQL 1.1 and OWL 2.0, become available, the Life Sciences continue to push the boundaries of these technologies with ever more sophisticated tools and applications. Unsurprisingly, therefore, interest in the SWAT4LS (Semantic Web Applications and Tools for the Life Sciences) activities have remained high, as was evident during the third international SWAT4LS workshop held in Berlin in December 2010. Contributors to this workshop were invited to submit extended versions of their papers, the best of which are now made available in the special supplement of BMC Bioinformatics. The papers reflect the wide range of work in this area, covering the storage and querying of Life Sciences data in RDF triple stores, tools for the development of biomedical ontologies and the semantics-based integration of Life Sciences as well as clinicial data.

BACKGROUND: In situ hybridisation gene expression information helps biologists identify where a gene is expressed. However, the databases that republish the experimental information online are often both incomplete and inconsistent. Non-monotonic reasoning can help resolve such difficulties - one such form of reasoning is computational argumentation. Essentially this involves asking a computer to debate (i.e. reason about) the validity of a particular statement. Arguments are produced for both sides - the statement is true and, the statement is false - then the most powerful argument is used. In this work the computer is asked to debate whether or not a gene is expressed in a particular mouse anatomical structure. The information generated during the debate can be passed to the biological end-user, enabling their own decision-making process. RESULTS: This paper examines the evolution of a system, Argudas, which tests using computational argumentation in an in situ gene hybridisation gene expression use case. Argudas reasons using information extracted from several different online resources that publish gene expression information for the mouse. The development and evaluation of two prototypes is discussed. Throughout a number of issues shall be raised including the appropriateness of computational argumentation in biology and the challenges faced when integrating apparently similar online biological databases. CONCLUSIONS: From the work described in this paper it is clear that for argumentation to be effective in the biological domain the argumentation community need to develop further the tools and resources they provide. Additionally, the biological community must tackle the incongruity between overlapping and adjacent resources, thus facilitating the integration and modelling of biological information. Finally, this work highlights both the importance of, and difficulty in creating, a good model of the domain.

Digital brain atlases are used in neuroscience to characterize the spatial organization of neuronal structures [1]–[3], for planning and guidance during neurosurgery [4], [5], and as a reference for interpreting other modalities such as gene expression or proteomic data [6]–[9]. The field of digital atlasing is extensive, and includes high quality brain atlases of the mouse [10], rat [11], rhesus macaque [12], human [13], [14], and several other model organisms. In addition to atlases based on histology, [11], [15], [16], magnetic resonance imaging [10], [17], and positron emission tomography [11], modern digital atlases often use probabilistic and multimodal techniques [18], [19], as well as sophisticated visualization software [20], [21]. Whether atlases involve detailed visualization of structures of a single or small group of specimens [6], [22], [23] or averages over larger populations [18], [24], much of the work in developing digital brain atlases is from the perspective of the user of a single resource. This is often due largely to the challenges of data generation, maintenance, and resources management [25], [26]. A more recent goal of many neuroscientists is to connect multiple and diverse resources to work in a collaborative manner using an atlas based framework [2], [19]. This vision is appealing as, ideally, researchers would be able to share their data and analyses with others, regardless of where they or the data are located. An important step in this direction is the specification of a common frame of reference across specimens and resources (either as coordinate, ontology, or region of interest) that is adopted by the community. In this perspective, we propose a collaborative digital atlasing framework for coordinating mouse brain research that allows access to data, tools, and analyses from multiple sources.

Background: Semantically-enriched browsing has enhanced the browsing experience by providing contextualised dynamically generated Web content, and quicker access to searched-for information. However, adoption of Semantic Web technologies is limited and user perception from the non-IT domain sceptical. Furthermore, little attention has been given to evaluating semantic browsers with real users to demonstrate the enhancements and obtain valuable feedback. The Sealife project investigates semantic browsing and its application to the life science domain. Sealife's main objective is to develop the notion of context-based information integration by extending three existing Semantic Web browsers (SWBs) to link the existing Web to the eScience infrastructure. Methods: This paper describes a user-centred evaluation framework that was developed to evaluate the Sealife SWBs that elicited feedback on users' perceptions on ease of use and information findability. Three sources of data: i) web server logs; ii) user questionnaires; and iii) semi-structured interviews were analysed and comparisons made between each browser and a control system. Results: It was found that the evaluation framework used successfully elicited users' perceptions of the three distinct SWBs. The results indicate that the browser with the most mature and polished interface was rated higher for usability, and semantic links were used by the users of all three browsers. Conclusion: Confirmation or contradiction of our original hypotheses with relation to SWBs is detailed along with observations of implementation issues. © 2009 Oliver et al; licensee BioMed Central Ltd.

BACKGROUND: A 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. RESULTS: A 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. CONCLUSION: Overall 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.

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.

MOTIVATION: During task composition, such as can be found in distributed query processing, workflow systems and AI planning, decisions have to be made by the system and possibly by users with respect to how a given problem should be solved. Although there is often more than one correct way of solving a given problem, these multiple solutions do not necessarily lead to the same result. Some researchers are addressing this problem by providing data provenance information. Others use expert advice encoded in a supporting knowledge-base. In this paper, we propose an approach that assesses the importance of such decisions with respect to the overall result. We present a way of measuring decision criticality and describe its potential use. RESULTS: A multi-agent bioinformatics integration system is used as the basis of a framework that facilitates such functionality. We propose an agent architecture, and a concrete bioinformatics example (prototype) is used to show how certain decisions may not be critical in the context of more complex tasks.

BACKGROUND: Improvements in technology have been accompanied by the generation of large amounts of complex data. This same technology must be harnessed effectively if the knowledge stored within the data is to be retrieved. Storing data in ontologies aids its management; ontologies serve as controlled vocabularies that promote data exchange and re-use, improving analysis. The Edinburgh Mouse Atlas Project stores the developmental stages of the mouse embryo in anatomy ontologies. This project is looking at the use of visual data overviews for intuitive analysis of the ontology data. RESULTS: A prototype has been developed that visualises the ontologies using directed acyclic graphs in two dimensions, with the ability to study detail in regions of interest in isolation or within the context of the overview. This is followed by the development of a technique that layers individual anatomy ontologies in three-dimensional space, so that relationships across multiple data sets may be mapped using physical links drawn along the third axis. CONCLUSION: Usability evaluations of the applications confirmed advantages in visual analysis of complex data. This project will look next at data input from multiple sources, and continue to develop the techniques presented to provide intuitive identification of relationships that span multiple ontologies.

MOTIVATION: The Edinburgh Mouse Atlas and Gene Expression Database project has developed a digital atlas of mouse development to provide a spatio-temporal framework for spatially mapped data such as in situ gene expression and cell lineage. As part of this database, a mouse embryo anatomy ontology has been created. A formalization of this anatomy is required to document its precise semantics and how it is used in the context of the Mouse Atlas. RESULTS: The paper describes the existing anatomy ontology and formalizes aspects of it using a predicate logic based approach. It therefore provides a guide for users of the current version of the ontology, as well as the basis for a description of the anatomy using an ontology language, such as OWL, thus enabling future work on reasoning about the Mouse Atlas in the context of an intelligent gene expression bioinformatics workflow system. The logic has been implemented in a Prolog prototype. AVAILABILITY: The Mouse Atlas is available on-line at http://genex.hgu.mrc.ac.uk

BACKGROUND: 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 part-of. 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. RESULTS: 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. CONCLUSION: 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.

The Edinburgh Mouse Atlas is a spatial-temporal framework to store and analyze biological data including three-dimensional (3-D) images that relate to mouse embryo development. The purpose of the system is the analysis and querying of complex spatial patterns, in particular the patterns of gene activity during embryo development. The framework holds large 3-D gray level images and is implemented in part as an object-oriented database. In this paper, we propose a dynamic layered architecture, based on the mediator approach, for the design of a transparent and scalable distributed system which can process objects that can exceed 1 GB in size. The system's data are distributed and/or declustered across a number of image servers and are processed by specialized mediators.

Whether for astrophysics, bioinformatics or any other discipline, developing a scientific database application in a distributed computing environment remains a challenging task. Part of this challenge lies in having to make critical choices with respect to the underlying computing technologies and the roles they play in such projects. Emerging Grid technologies are adding further to the choices available to developers. Although the Grid promises significant advances in support of e-Science applications, much of the envisaged infrastructure, including data management, is still under development. The panel will discuss the role of data management in the e-Science process and the integration of database services into a Grid necessary to enable the realisation of this role.

The Edinburgh Mouse Atlas Project (EMAP) has developed a digital atlas of mouse development which provides a bioinformatics framework to spatially reference biological data. The EMAP core database contains 3D grey-level reconstructions of the mouse embryo at various stages of development, a systematic nomenclature of the embryo anatomy, and defined 3D regions (domains) of the embryo. The reconstructions define a spatial framework for mapping data. Data front an in-situ gene-expression database is spatially mapped onto the atlas allowing the users to query gene-expression patterns using the 3D embryo model as a reference. The mouse atlas and gene-expression databases are publicly accessible through a set of Web-based tools. The system consists of a set of tools and databases, some of which reside locally on the Mouse Atlas hosts, others are remote. The middleware layer is primarily CORBA-based, but also makes use of Java servlets. Data are primarily stored in an object-oriented database system (ObjectStore). As a public bioinformatics resource, the Mouse Atlas system must be easily accessible to researchers all over the world, hence interoperability is a key issue. The Mouse Atlas is an on-going research and development project at the Medical Research Council, Human Genetics Unit, in Edinburgh. Access to its databases and further information is available through its Web site.

The authors present a performance study of two main memory database recovery algorithms, one using a shadow approach and the other update-in-place. The results show that in main memory databases, shadow approach performs better than update-in-place. The authors introduce minor improvements to the shadow scheme and show that the modified algorithm does show further performance improvement.>

The performance of two main-memory database system (MDBS) algorithms is studied. M.H. Eich's (Proc. of the Fifth Int. Workshop on Database Machines., Oct. 1987) and T.J. Lehman's (Ph. D. Thesis, Univ. of Wisconsin-Madison, Aug 1986) algorithms are selected for this study. The investigation indicates that the shadow approach, as used by Eich, has some advantages over the update in-place strategy of Lehman. The shadow approach has faster response for normal transactions even though transaction commit is slower compared to update in-place approach. It is concluded that irrespective of the recovery algorithm an efficient load balancing plays an important part in the performance of the system. It is suggested that processors should be allocated to any kind of activity on a demand basis. It is discovered that individual recovery of lockable units as used in Lehman's algorithm is a better choice since it increases the system availability after a system failure. It also allows background recovery to go in parallel to normal transaction processing.>