Ontology-Based Queries over Cancer Data

The ever-increasing amount of data in biomedical research, and in cancer research in particular, needs to be managed to support efficient data access, exchange and integration. Existing software infrastructures, such as caGrid, support access to distributed information annotated with a domain ontology. However, caGrid's current querying functionality depends on the structure of individual data resources without exploiting the semantic annotations. In this paper, we present the design and development of an ontology-based querying functionality that consists of: the generation of OWL2 ontologies from the underlying data resources’ metadata and a query rewriting and translation process based on reasoning, which converts a query at the domain ontology level into queries at the software infrastructure level. We present a detailed analysis of our approach as well as an extensive performance evaluation. While the implementation and evaluation was performed for the caGrid infrastructure, the approach could be applicable to other model and metadata-driven environments for data sharing

Federated ontology-based queries over cancer data

Background Personalised medicine provides patients with treatments that are specific to their genetic profiles. It requires efficient data sharing of disparate data types across a variety of scientific disciplines, such as molecular biology, pathology, radiology and clinical practice. Personalised medicine aims to offer the safest and most effective therapeutic strategy based on the gene variations of each subject. In particular, this is valid in oncology, where knowledge about genetic mutations has already led to new therapies. Current molecular biology techniques (microarrays, proteomics, epigenetic technology and improved DNA sequencing technology) enable better characterisation of cancer tumours. The vast amounts of data, however, coupled with the use of different terms - or semantic heterogeneity - in each discipline makes the retrieval and integration of information difficult. Results Existing software infrastructures for data-sharing in the cancer domain, such as caGrid, support access to distributed information. caGrid follows a service-oriented model-driven architecture. Each data source in caGrid is associated with metadata at increasing levels of abstraction, including syntactic, structural, reference and domain metadata. The domain metadata consists of ontology-based annotations associated with the structural information of each data source. However, caGrid's current querying functionality is given at the structural metadata level, without capitalising on the ontology-based annotations. This paper presents the design of and theoretical foundations for distributed ontology-based queries over cancer research data. Concept-based queries are reformulated to the target query language, where join conditions between multiple data sources are found by exploiting the semantic annotations. The system has been implemented, as a proof of concept, over the caGrid infrastructure. The approach is applicable to other model-driven architectures. A graphical user interface has been developed, supporting ontology-based queries over caGrid data sources. An extensive evaluation of the query reformulation technique is included. Conclusions To support personalised medicine in oncology, it is crucial to retrieve and integrate molecular, pathology, radiology and clinical data in an efficient manner. The semantic heterogeneity of the data makes this a challenging task. Ontologies provide a formal framework to support querying and integration. This paper provides an ontology-based solution for querying distributed databases over service-oriented, model-driven infrastructures

The emergence of openness in open source projects : the case of openEHR

The meaning of openness in open source is both intrinsically unstable and dynamic, and tends to fluctuate with time and context. We draw on a very particular open-source project primarily concerned with building rigorous clinical concepts to be used in electronic health records called openEHR. openEHR explains how openness is a concept that is purposely engaged with, and how, in this process of engagement, the very meaning of open matures and evolves within the project. Drawing on rich longitudinal data related to openEHR we theorise the evolving nature of openness and how this idea emerges through two intertwined processes of maturation and metamorphosis. While metamorphosis allows us to trace and interrogate the mutational evolution in openness, maturation analyses the small, careful changes crafted to build a very particular understanding of openness. Metamorphosis is less managed and controlled, whereas maturation is representative of highly precise work carried out in controlled form. Both processes work together in open-source projects and reinforce each other. Our study reveals that openness emerges and evolves in open-source projects where it can be understood to mean rigour; ability to participate; open implementation; and an open process. Our work contributes to a deepening in the theorisation of what it means to be an open-source project. The multiple and co-existing meanings of ‘open’ imply that open-source projects evolve in nonlinear ways where each critical meaning of openness causes a reflective questioning by the community of its continued status and existence

Data integration strategies for informing computational design in synthetic biology

PhD ThesisThe potential design space for biological systems is complex, vast and multidimensional. Therefore, effective large-scale synthetic biology requires computational design and simulation. By constraining this design space, the time- and cost-efficient design of biological systems can be facilitated. One way in which a tractable design space can be achieved is to use the extensive and growing amount of biological data available to inform the design process. By using existing knowledge design efforts can be focused on biologically plausible areas of design space. However, biological data is large, incomplete, heterogeneous, and noisy. Data must be integrated in a systematic fashion in order to maximise its benefit. To date, data integration has not been widely applied to design in synthetic biology. The aim of this project is to apply data integration techniques to facilitate the efficient design of novel biological systems. The specific focus is on the development and application of integration techniques for the design of genetic regulatory networks in the model bacterium Bacillus subtilis. A dataset was constructed by integrating data from a range of sources in order to capture existing knowledge about B. subtilis 168. The dataset is represented as a computationally-accessible, semantically-rich network which includes information concerning biological entities and their relationships. Also included are sequence-based features mined from the B. subtilis genome, which are a useful source of parts for synthetic biology. In addition, information about the interactions of these parts has been captured, in order to facilitate the construction of circuits with desired behaviours. This dataset was also modelled in the form of an ontology, providing a formal specification of parts and their interactions. The ontology is a major step towards the unification of the data required for modelling with a range of part catalogues specifically designed for synthetic biology. The data from the ontology is available to existing reasoners for implicit knowledge extraction. The ontology was applied to the automated identification of promoters, operators and coding sequences. Information from the ontology was also used to generate dynamic models of parts. The work described here contributed to the development of a formalism called Standard Virtual Parts (SVPs), which aims to represent models of biological parts in a standardised manner. SVPs comprise a mapping between biological parts and modular computational models. A genetic circuit designed at a part-level abstraction can be investigated in detail by analysing a circuit model composed of SVPs. The ontology was used to construct SVPs in the form of standard Systems Biology Markup Language models. These models are publicly available from a computationally-accessible repository, and include metadata which facilitates the computational composition of SVPs in order to create models of larger biological systems. To test a genetic circuit in vitro or in vivo, the genetics elements necessary to encode the enitites in the in silico model, and their associated behaviour, must be derived. Ultimately, this process results in the specification for synthesisable DNA sequence. For large models, particularly those that are produced computationally, the transformation process is challenging. To automate this process, a model-to-sequence conversion algorithm was developed. The algorithm was implemented as a Java application called MoSeC. Using MoSeC, both CellML and SBML models built with SVPs can be converted into DNA sequences ready to synthesise. Selection of the host bacterial cell for a synthetic genetic circuit is very important. In order not to interfere with the existing cellular machinery, orthogonal parts from other species are used since these parts are less likely to have undesired interactions with the host. In order to find orthogonal transcription factors (OTFs), and their target binding sequences, a subset of the data from the integrated B. subtilis dataset was used. B. subtilis gene regulatory networks were used to re-construct regulatory networks in closely related Bacillus species. The system, called BacillusRegNet, stores both experimental data for B. subtilis and homology predictions in other species. BacillusRegNet was mined to extract OTFs and their binding sequences, in order to facilitate the engineering of novel regulatory networks in other Bacillus species. Although the techniques presented here were demonstrated using B. subtilis, they can be applied to any other organism. The approaches and tools developed as part of this project demonstrate the utility of this novel integrated approach to synthetic biology.EPSRC: NSF: The Newcastle University School of Computing Science