Robert Stevens

State of the nation in data integration for bioinformatics

Data integration is a perennial issue in bioinformatics, with many systems being developed and many technologies offered as a panacea for its resolution. The fact that it is still a problem indicates a persistence of underlying issues. Progress has been made, but we should ask what lessons have been learnt?, and what still needs to be done? Semantic Web and Web 2.0 technologies are the latest to find traction within bioinformatics data integration. Now we can ask whether the Semantic Web, mashups, or their combination, have the potential to help. This paper is based on the opening invited talk by Carole Goble given at the Health Care and Life Sciences Data Integration for the Semantic Web Workshop collocated with WWW2007. The paper expands on that talk. We attempt to place some perspective on past efforts, highlight the reasons for success and failure, and indicate some pointers to the future.

An ontology for bioinformatics applications

MOTIVATIONnAn ontology of biological terminology provides a model of biological concepts that can be used to form a semantic framework for many data storage, retrieval and analysis tasks. Such a semantic framework could be used to underpin a range of important bioinformatics tasks, such as the querying of heterogeneous bioinformatics sources or the systematic annotation of experimental results.nnnRESULTSnThis paper provides an overview of an ontology [the Transparent Access to Multiple Biological Information Sources (TAMBIS) ontology or TaO] that describes a wide range of bioinformatics concepts. The present paper describes the mechanisms used for delivering the ontology and discusses the ontologys design and organization, which are crucial for maintaining the coherence of a large collection of concepts and their relationships.nnnAVAILABILITYnThe TAMBIS system, which uses a subset of the TaO described here, is accessible over the Web via http://img.cs.man.ac.uk/tambis (although in the first instance, we will use a password mechanism to limit the load on our server). The complete model is also available on the Web at the above URL.

A classification of tasks in bioinformatics

MOTIVATIONnThis paper reports on a survey of bioinformatics tasks currently undertaken by working biologists. The aim was to find the range of tasks that need to be supported and the components needed to do this in a general query system. This enabled a set of evaluation criteria to be used to assess both the biology and mechanical nature of general query systems.nnnRESULTSnA classification of the biological content of the tasks gathered offers a checklist for those tasks (and their specialisations) that should be offered in a general bioinformatics query system. This semantic analysis was contrasted with a syntactic analysis that revealed the small number of components required to describe all bioinformatics questions. Both the range of biological tasks and syntactic task components can be seen to provide a set of bioinformatics requirements for general query systems. These requirements were used to evaluate two bioinformatics query systems.

Query processing in the TAMBIS bioinformatics source integration system

Conducting bioinformatic analyses involves biologists in expressing requests over a range of highly heterogeneous information sources and software tools. Such activities are laborious, and require detailed knowledge of the data structures and call interfaces of the different sources. The TAMBIS (Transparent Access to Multiple Bioinformatics Information Sources) project seeks to make the diversity in data structures, call interfaces and locations of bioinformatics sources transparent to users. In TAMBIS, queries are expressed in terms of an ontology implemented using a description logic, and queries over the ontology are rewritten to a middleware level for execution over the diverse sources. The paper describes query processing in TAMBIS, focusing in particular on the way source-independent concepts in the ontology are related to source-dependent middleware calls, and describing how the planner identifies efficient ways of evaluating user queries.

Using OWL to model biological knowledge

Much has been written of the facilities for ontology building and reasoning offered for ontologies expressed in the Web Ontology Language (OWL). Less has been written about how the modelling requirements of different areas of interest are met by OWL-DLs underlying model of the world. In this paper we use the disciplines of biology and bioinformatics to reveal the requirements of a community that both needs and uses ontologies. We use a case study of building an ontology of protein phosphatases to show how OWL-DLs model can capture a large proportion of the communitys needs. We demonstrate how Ontology Design Patterns (ODPs) can extend inherent limitations of this model. We give examples of relationships between more than two instances; lists and exceptions, and conclude by illustrating what OWL-DL and its underlying description logic either cannot handle in theory or because of lack of implementation. Finally, we present a research agenda that, if fulfilled, would help ensure OWLs wider take up in the life science community.

The travails of visually impaired web travellers

This paper proposes the inclusion of travel and mobility in the usability metrics of web design. Hypertext design and usability has traditionally concentrated upon navigation and/or orientation. The notion of travel extends navigation and orientation to include environment, mobility and the purpose of the travel task. The presence of travel aids are important for all users, but particularly so for those with a visual impairment. This paper presents the ground work for including travel into web design and usability metrics by presenting a framework for identifying travel objects and registering them as either cues to aid travel or obstacles that hinder travel for visually impaired users. The aim is to maximise cues and minimise obstacles to give high mobility; measured by the mobility index. This framework is based upon a model of real world travel by both sighted and visually impaired people, where travel objects are used for orientation, navigation, route planning and survey knowledge. Knowledge of the differences in travel between visually impaired and sighted people will enable the model to be used in assisting the design of better user agents and web content for visually impaired and other users.

Building a bioinformatics ontology using OIL

This paper describes the initial stages of building an ontology of bioinformatics and molecular biology. The conceptualization is encoded using the ontology inference layer (OIL), a knowledge representation language that combines the modeling style of frame-based systems with the expressiveness and reasoning power of description logics (DLs). This paper is the second of a pair in this special issue. The first described the core of the OIL language and the need to use ontologies to deliver semantic bioinformatics resources. In this paper, the early stages of building an ontology component of a bioinformatics resource querying application are described. This ontology (TaO) holds the information about molecular biology represented in bioinformatics resources and the bioinformatics tasks performed over these resources. It, therefore, represents the metadata of the resources the application can query. It also manages the terminologies used in constructing the query plans used to retrieve instances from those external resources. The methodology used in this task capitalizes upon features of OIL-The conceptualization afforded by the frame-based view of OILs syntax; the expressive power and reasoning of the logical formalism; and the ability to encode both handcrafted, hierarchies of concepts, as well as defining concepts in terms of their properties, which can then be used to establish a classification and infer relationships not encoded by the ontologist. This ability forms the basis of the methodology described here: For each portion of the TaO, a basic framework of concepts is asserted by the ontologist. Then, the properties of these concepts are defined by the ontologist and the logics reasoning power used to reclassify and infer further relationships. This cycle of elaboration and refinement is iterated on each portion of the ontology until a satisfactory ontology has been created.

On the use of agents in a BioInformatics grid

My Grid is an e-Science Grid project that aims to help biologists and bioinformaticians to perform workflow-based in silico experiments, and help them to automate the management of such workflows through personalisation, notification of change and publication of experiments. In this paper, we describe the architecture of my Grid and how it will be used by the scientist. We then show how my Grid can benefit from agents technologies. We have identified three key uses of agent technologies in my Grid: user agents, able to customize and personalise data, agent communication languages offering a generic and portable communication medium, and negotiation allowing multiple distributed entities to reach service level agreements.

Applying Ontology Design Patterns in Bio-ontologies

Biological knowledge has been, to date, coded by biologists in axiomatically lean bio-ontologies. To facilitate axiomatic enrichment, complex semantics can be encapsulated as Ontology Design Patterns (ODPs). These can be applied across an ontology to make the domain knowledge explicit and therefore available for computational inference. The same ODP is often required in many different parts of the same ontology and the manual construction of often complex ODP semantics is loaded with the possibility of slips, inconsistencies and other errors. To address this issue we present the Ontology PreProcessor Language (OPPL), an axiom-based language for selecting and transforming portions of OWL ontologies, offering a means for applying ODPs. Example ODPs for the common need to represent modifiers of independent entities are presented and one of them is used as a demonstration of how to use OPPL to apply it.

Guiding the user: an ontology driven interface

We describe a novel query interface allowing the construction and manipulation of description logic expressions. The construction process is driven by the content of a conceptual model, guiding the user towards appropriate choices and providing a lucid interface.