Duncan Hull

Taverna: a tool for building and running workflows of services

Taverna is an application that eases the use and integration of the growing number of molecular biology tools and databases available on the web, especially web services. It allows bioinformaticians to construct workflows or pipelines of services to perform a range of different analyses, such as sequence analysis and genome annotation. These high-level workflows can integrate many different resources into a single analysis. Taverna is available freely under the terms of the GNU Lesser General Public License (LGPL) from .

Defrosting the Digital Library: Bibliographic Tools for the Next Generation Web

Many scientists now manage the bulk of their bibliographic information electronically, thereby organizing their publications and citation material from digital libraries. However, a library has been described as “thought in cold storage,” and unfortunately many digital libraries can be cold, impersonal, isolated, and inaccessible places. In this Review, we discuss the current chilly state of digital libraries for the computational biologist, including PubMed, IEEE Xplore, the ACM digital library, ISI Web of Knowledge, Scopus, Citeseer, arXiv, DBLP, and Google Scholar. We illustrate the current process of using these libraries with a typical workflow, and highlight problems with managing data and metadata using URIs. We then examine a range of new applications such as Zotero, Mendeley, Mekentosj Papers, MyNCBI, CiteULike, Connotea, and HubMed that exploit the Web to make these digital libraries more personal, sociable, integrated, and accessible places. We conclude with how these applications may begin to help achieve a digital defrost, and discuss some of the issues that will help or hinder this in terms of making libraries on the Web warmer places in the future, becoming resources that are considerably more useful to both humans and machines.

Applying semantic web services to bioinformatics: experiences gained, lessons learnt

We have seen an increasing amount of interest in the application of Semantic Web technologies to Web services. The aim is to support automated discovery and composition of the services allowing seamless and transparent interoperability. In this paper we discuss three projects that are applying such technologies to bioinformatics: Grid, MOBY-Services and Semantic-MOBY. Through an examination of the differences and similarities between the solutions produced, we highlight some of the practical difficulties in developing Semantic Web services and suggest that the experiences with these projects have implications for the development of Semantic Web services as a whole.

The my Grid ontology: bioinformatics service discovery

(my)Grid supports in silico experiments in the life sciences, enabling the design and enactment of workflows as well as providing components to assist service discovery, data and metadata management. The (my)Grid ontology is one component in a larger semantic discovery framework for the identification of the highly distributed and heterogeneous bioinformatics services in the public domain. From an initial model of formal OWL-DL semantics throughout, we now adopt a spectrum of expressivity and reasoning for different tasks in service annotation and discovery. Here, we discuss the development and use of the (my)Grid ontology and our experiences in semantic service discovery.

Further developments towards a genome-scale metabolic model of yeast

BackgroundTo date, several genome-scale network reconstructions have been used to describe the metabolism of the yeast Saccharomyces cerevisiae, each differing in scope and content. The recent community-driven reconstruction, while rigorously evidenced and well annotated, under-represented metabolite transport, lipid metabolism and other pathways, and was not amenable to constraint-based analyses because of lack of pathway connectivity.ResultsWe have expanded the yeast network reconstruction to incorporate many new reactions from the literature and represented these in a well-annotated and standards-compliant manner. The new reconstruction comprises 1102 unique metabolic reactions involving 924 unique metabolites - significantly larger in scope than any previous reconstruction. The representation of lipid metabolism in particular has improved, with 234 out of 268 enzymes linked to lipid metabolism now present in at least one reaction. Connectivity is emphatically improved, with more than 90% of metabolites now reachable from the growth medium constituents. The present updates allow constraint-based analyses to be performed; viability predictions of single knockouts are comparable to results from in vivo experiments and to those of previous reconstructions.ConclusionsWe report the development of the most complete reconstruction of yeast metabolism to date that is based upon reliable literature evidence and richly annotated according to MIRIAM standards. The reconstruction is available in the Systems Biology Markup Language (SBML) and via a publicly accessible database http://www.comp-sys-bio.org/yeastnet/.

Taverna/myGrid: Aligning a Workflow System with the Life Sciences Community

Bioinformatics is a discipline that uses computational and mathematical techniques to store, manage, and analyze biological data in order to answer biological questions. Bioinformatics has over 850 databases [154] and numerous tools that work over those databases and local data to produce even more data themselves. In order to perform an analysis, a bioinformatician uses one or more of these resources to gather, filter, and transform data to answer a question. Thus, bioinformatics is an in silico science.

Data curation + process curation=data integration + science

In bioinformatics, we are familiar with the idea of curated data as a prerequisite for data integration. We neglect, often to our cost, the curation and cataloguing of the processes that we use to integrate and analyse our data. Programmatic access to services, for data and processes, means that compositions of services can be made that represent the in silico experiments or processes that bioinformaticians perform. Data integration through workflows depends on being able to know what services exist and where to find those services. The large number of services and the operations they perform, their arbitrary naming and lack of documentation, however, mean that they can be difficult to use. The workflows themselves are composite processes that could be pooled and reused but only if they too can be found and understood. Thus appropriate curation, including semantic mark-up, would enable processes to be found, maintained and consequently used more easily. This broader view on semantic annotation is vital for full data integration that is necessary for the modern scientific analyses in biology. This article will brief the community on the current state of the art and the current challenges for process curation, both within and without the Life Sciences.

The Software Ontology (SWO): a resource for reproducibility in biomedical data analysis, curation and digital preservation

MotivationBiomedical ontologists to date have concentrated on ontological descriptions of biomedical entities such as gene products and their attributes, phenotypes and so on. Recently, effort has diversified to descriptions of the laboratory investigations by which these entities were produced. However, much biological insight is gained from the analysis of the data produced from these investigations, and there is a lack of adequate descriptions of the wide range of software that are central to bioinformatics. We need to describe how data are analyzed for discovery, audit trails, provenance and reproducibility.ResultsThe Software Ontology (SWO) is a description of software used to store, manage and analyze data. Input to the SWO has come from beyond the life sciences, but its main focus is the life sciences. We used agile techniques to gather input for the SWO and keep engagement with our users. The result is an ontology that meets the needs of a broad range of users by describing software, its information processing tasks, data inputs and outputs, data formats versions and so on. Recently, the SWO has incorporated EDAM, a vocabulary for describing data and related concepts in bioinformatics. The SWO is currently being used to describe software used in multiple biomedical applications.ConclusionThe SWO is another element of the biomedical ontology landscape that is necessary for the description of biomedical entities and how they were discovered. An ontology of software used to analyze data produced by investigations in the life sciences can be made in such a way that it covers the important features requested and prioritized by its users. The SWO thus fits into the landscape of biomedical ontologies and is produced using techniques designed to keep it in line with user’s needs.AvailabilityThe Software Ontology is available under an Apache 2.0 license at http://theswo.sourceforge.net/; the Software Ontology blog can be read at http://softwareontology.wordpress.com.

Knowledge discovery for biology with Taverna

Life Science research has extended beyond in vivo and in vitro bench-bound science to incorporate in silico knowledge discovery, using resources that have been developed over time by different teams for different purposes and in different forms. The myGrid project has developed a set of software components and a workbench, Taverna, for building, running and sharing workflows that link third party bioinformatics services, such as databases, analytic tools and applications. Intelligently discovering prior services, workflow or data is aided by a Semantic Web of annotations, as is the building of the workflows themselves. Metadata associated with the workflow experiments, the provenance of the data outcomes and the record of the experimental process need to be flexible and extensible. Semantic Web metadata technologies would seem to be well-suited to building a Semantic Web of provenance. We have the potential to integrate and aggregate workflow outcomes, and reason over provenance logs to identify new experimental insights, and to build and export a Semantic Web of experiments that contributes to Knowledge Discovery for Taverna users and for the scientific community as a whole.Life Science research has extended beyond in vivo and in vitro bench-bound science to incorporate in silico knowledge discovery, using resources that have been developed over time by different teams for different purposes and in different forms. The myGrid project has developed a set of software components and a workbench, Taverna, for building, running and sharing workflows that link third party bioinformatics services, such as databases, analytic tools and applications. Intelligently discovering prior services, workflow or data is aided by a Semantic Web of annotations, as is the building of the workflows themselves. Metadata associated with the workflow experiments, the provenance of the data outcomes and the record of the experimental process need to be flexible and extensible. Semantic Web metadata technologies would seem to be well-suited to building a Semantic Web of provenance. We have the potential to integrate and aggregate workflow outcomes, and reason over provenance logs to identify new experimental insights, and to build and export a Semantic Web of experiments that contributes to Knowledge Discovery for Taverna users and for the scientific community as a whole.

GO faster ChEBI with Reasonable Biochemistry

Many new ontologies have been developed in recent years with the aim of facilitating data integration in both the chemical and life sciences. One such ontology is Chemical Entities of Biological Interest (ChEBI) [4]. As the name suggests, this ontology describes biologically interesting chemical entities which includes small molecules such as aspirin. Currently, ChEBI does not make use of description logic but ongoing revisions to the ontology [2] have created new opportunities for more extensive reasoning over ChEBI in the future. This also raises some challenging problems which require attention. This paper describes some of these problems, structured as follows: Section 2 introduces and describes ChEBI in more detail. This is followed by a discussion of some of the issues ChEBI currently faces in its maintenance and development in section 3, which also outlines and discusses potential solutions. Finally, section 4 draws some conclusions and points to future work.