Simon Jupp

Populous: a tool for building OWL ontologies from templates.

BackgroundOntologies are being developed for the life sciences to standardise the way we describe and interpret the wealth of data currently being generated. As more ontology based applications begin to emerge, tools are required that enable domain experts to contribute their knowledge to the growing pool of ontologies. There are many barriers that prevent domain experts engaging in the ontology development process and novel tools are needed to break down these barriers to engage a wider community of scientists.ResultsWe present Populous, a tool for gathering content with which to construct an ontology. Domain experts need to add content, that is often repetitive in its form, but without having to tackle the underlying ontological representation. Populous presents users with a table based form in which columns are constrained to take values from particular ontologies. Populated tables are mapped to patterns that can then be used to automatically generate the ontologys content. These forms can be exported as spreadsheets, providing an interface that is much more familiar to many biologists.ConclusionsPopulouss contribution is in the knowledge gathering stage of ontology development; it separates knowledge gathering from the conceptualisation and axiomatisation, as well as separating the user from the standard ontology authoring environments. Populous is by no means a replacement for standard ontology editing tools, but instead provides a useful platform for engaging a wider community of scientists in the mass production of ontology content.

Developing a Kidney and Urinary Pathway Knowledge Base

BackgroundChronic renal disease is a global health problem. The identification of suitable biomarkers could facilitate early detection and diagnosis and allow better understanding of the underlying pathology. One of the challenges in meeting this goal is the necessary integration of experimental results from multiple biological levels for further analysis by data mining. Data integration in the life science is still a struggle, and many groups are looking to the benefits promised by the Semantic Web for data integration.ResultsWe present a Semantic Web approach to developing a knowledge base that integrates data from high-throughput experiments on kidney and urine. A specialised KUP ontology is used to tie the various layers together, whilst background knowledge from external databases is incorporated by conversion into RDF. Using SPARQL as a query mechanism, we are able to query for proteins expressed in urine and place these back into the context of genes expressed in regions of the kidney.ConclusionsThe KUPKB gives KUP biologists the means to ask queries across many resources in order to aggregate knowledge that is necessary for answering biological questions. The Semantic Web technologies we use, together with the background knowledge from the domain’s ontologies, allows both rapid conversion and integration of this knowledge base. The KUPKB is still relatively small, but questions remain about scalability, maintenance and availability of the knowledge itself.AvailabilityThe KUPKB may be accessed via http://www.e-lico.eu/kupkb.

The KUPKB: a novel Web application to access multiomics data on kidney disease

The information gathered from the large number of omics experiments in renal biology is underexplored, as it is scattered over many publications or held in supplemental data. To address this, we have developed an open‐source Kidney and Urinary Pathway Knowledge Base (KUPKB) that facilitates simple exploration of these omics data. The KUPKB currently comprises 220 data sets (miRNA, mRNA, proteins, and metabolites) extracted from existing publications or databases. Researchers can explore the integrated data using the iKUP browser, and a simple template is provided to submit new omics data sets to the knowledge base. As an example of iKUPs use, we show how we identified, in silico, calreticulin as a protein induced in human interstitial fibrosis and tubular atrophy (IFTA) in chronic kidney transplant rejection; a link that would have been difficult to establish using existing Web‐based tools. Using immunohistochemistry, we validated in vivo this in silico result in human and rat biopsies of IFTA, thus identifying calreticulin as a potential new player in chronic kidney transplant rejection. The KUPKB provides a simple tool that enables users to quickly survey a wide range of omics data sets and has been shown to facilitate rapid hypothesis generation in the context of renal pathophysiology.—Klein, J., Jupp, S., Moulos, P., Fernandez, M., Buffin‐Meyer, B., Casemayou, A., Chaaya, R., Charonis, A., Bascands, J.‐L., Stevens, R., Schanstra, J. P. The KUPKB: a novel Web application to access multiomics data on kidney disease. FASEB J. 26, 2145‐2153 (2012). www.fasebj.org

Using Ontologies and Vocabularies for Dynamic Linking

Ontology-based linking offers a solution to some of the problems with static, restricted, and inflexible traditional Web linking. Conceptual hypermedia provides navigation between Web resources, supported by a conceptual model, in which an ontologys definitions and structure, together with the lexical labels, drive the consistency of link provision and the linkings dynamic aspects. Lightweight standard representations make it possible to use existing vocabularies to support Web navigation and browsing. In this way, the navigation and linking of diverse resources (including those not in our control) based on a community understanding of the domain can be consistently managed.

Process of Building a Vocabulary for the Infection Domain

The semantic Web vision relies on metadata and semantic annotation to be implemented on real world data. Ontologies and ontology-like artefacts are the key component providing necessary knowledge for Web document description. Domain ontology building is, however, a difficult and time consuming task. In this paper, we present our process of building an infection domain vocabulary for the national electronic library of infection. This paper describes the requirements for the vocabulary development process and the initial results.

Issues in learning an ontology from text

Ontology construction for any domain is a labour intensive and complex process. Any methodology that can reduce the cost and increase efficiency has the potential to make a major impact in the life sciences. This paper describes an experiment in ontology construction from text for the animal behaviour domain. Our objective was to see how much could be done in a simple and relatively rapid manner using a corpus of journal papers. We used a sequence of pre-existing text processing steps, and here describe the different choices made to clean the input, to derive a set of terms and to structure those terms in a number of hierarchies. We describe some of the challenges, especially that of focusing the ontology appropriately given a starting point of a heterogeneous corpus.ResultsUsing mainly automated techniques, we were able to construct an 18055 term ontology-like structure with 73% recall of animal behaviour terms, but a precision of only 26%. We were able to clean unwanted terms from the nascent ontology using lexico-syntactic patterns that tested the validity of term inclusion within the ontology. We used the same technique to test for subsumption relationships between the remaining terms to add structure to the initially broad and shallow structure we generated. All outputs are available at http://thirlmere.aston.ac.uk/~kiffer/animalbehaviour/.ConclusionWe present a systematic method for the initial steps of ontology or structured vocabulary construction for scientific domains that requires limited human effort and can make a contribution both to ontology learning and maintenance. The method is useful both for the exploration of a scientific domain and as a stepping stone towards formally rigourous ontologies. The filtering of recognised terms from a heterogeneous corpus to focus upon those that are the topic of the ontology is identified to be one of the main challenges for research in ontology learning.

A flexible API and editor for SKOS

We present a programmatic interface (SKOS API) and editor for working with the Simple Knowledge Organisation System SKOS. The SKOS API has been designed to work with SKOS models at a high level of abstraction to aid developers of applications that use SKOS. We describe a SKOS editor (SKOSEd) that is built on the Protege 4 framework using the OWL and SKOS API. As well as exploring the benets of the principled extensibility afforded by this approach, we also explore the limitations placed upon SKOS by restricting SKOSEd to OWL-DL.

The KUPNetViz: a biological network viewer for multiple -omics datasets in kidney diseases

BackgroundConstant technological advances have allowed scientists in biology to migrate from conventional single-omics to multi-omics experimental approaches, challenging bioinformatics to bridge this multi-tiered information. Ongoing research in renal biology is no exception. The results of large-scale and/or high throughput experiments, presenting a wealth of information on kidney disease are scattered across the web. To tackle this problem, we recently presented the KUPKB, a multi-omics data repository for renal diseases.ResultsIn this article, we describe KUPNetViz, a biological graph exploration tool allowing the exploration of KUPKB data through the visualization of biomolecule interactions. KUPNetViz enables the integration of multi-layered experimental data over different species, renal locations and renal diseases to protein-protein interaction networks and allows association with biological functions, biochemical pathways and other functional elements such as miRNAs. KUPNetViz focuses on the simplicity of its usage and the clarity of resulting networks by reducing and/or automating advanced functionalities present in other biological network visualization packages. In addition, it allows the extrapolation of biomolecule interactions across different species, leading to the formulations of new plausible hypotheses, adequate experiment design and to the suggestion of novel biological mechanisms. We demonstrate the value of KUPNetViz by two usage examples: the integration of calreticulin as a key player in a larger interaction network in renal graft rejection and the novel observation of the strong association of interleukin-6 with polycystic kidney disease.ConclusionsThe KUPNetViz is an interactive and flexible biological network visualization and exploration tool. It provides renal biologists with biological network snapshots of the complex integrated data of the KUPKB allowing the formulation of new hypotheses in a user friendly manner.

A user-centred evaluation framework for the Sealife semantic web browsers

BackgroundSemantically-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. Sealifes 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.MethodsThis 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.ResultsIt 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.ConclusionConfirmation or contradiction of our original hypotheses with relation to SWBs is detailed along with observations of implementation issues.

Using semantic web technologies to manage complexity and change in biomedical data

Data in biomedicine are characterised by their complexity, volatility and heterogeneity. It is these characteristics, rather than size of the data, that make managing these data an issue for their analysis. Any significant data analysis task requires gathering data from many places, organising the relationships between the datas entities and overcoming the issues of recognising the nature of each entity such that this organisation can take place. It is the inter-relationship of these data and the semantic confusion inherent in the data that make the data complex. On top of this we have volatility in the domains data, knowledge and experimental techniques that make the processing of data from the domain a distinct challenge, even before those data are organised. In this article we describe these challenges with respect to a project that is using data mining techniques to analyse data from the kidney and urinary pathway (KUP) domain. We are using Semantic Web technologies to manage the complexity and change in our data and we report on our experiences in this project.