Erick Antezana

Biological knowledge management: the emerging role of the Semantic Web technologies

New knowledge is produced at a continuously increasing speed, and the list of papers, databases and other knowledge sources that a researcher in the life sciences needs to cope with is actually turning into a problem rather than an asset. The adequate management of knowledge is therefore becoming fundamentally important for life scientists, especially if they work with approaches that thoroughly depend on knowledge integration, such as systems biology. Several initiatives to organize biological knowledge sources into a readily exploitable resourceome are presently being carried out. Ontologies and Semantic Web technologies revolutionize these efforts. Here, we review the benefits, trends, current possibilities, and the potential this holds for the biosciences.

Ontology Design Patterns for bio-ontologies: a case study on the Cell Cycle Ontology

BackgroundBio-ontologies are key elements of knowledge management in bioinformatics. Rich and rigorous bio-ontologies should represent biological knowledge with high fidelity and robustness. The richness in bio-ontologies is a prior condition for diverse and efficient reasoning, and hence querying and hypothesis validation. Rigour allows a more consistent maintenance. Modelling such bio-ontologies is, however, a difficult task for bio-ontologists, because the necessary richness and rigour is difficult to achieve without extensive training.ResultsAnalogous to design patterns in software engineering, Ontology Design Patterns are solutions to typical modelling problems that bio-ontologists can use when building bio-ontologies. They offer a means of creating rich and rigorous bio-ontologies with reduced effort. The concept of Ontology Design Patterns is described and documentation and application methodologies for Ontology Design Patterns are presented. Some real-world use cases of Ontology Design Patterns are provided and tested in the Cell Cycle Ontology. Ontology Design Patterns, including those tested in the Cell Cycle Ontology, can be explored in the Ontology Design Patterns public catalogue that has been created based on the documentation system presented (http://odps.sourceforge.net/).ConclusionsOntology Design Patterns provide a method for rich and rigorous modelling in bio-ontologies. They also offer advantages at different development levels (such as design, implementation and communication) enabling, if used, a more modular, well-founded and richer representation of the biological knowledge. This representation will produce a more efficient knowledge management in the long term.

BioGateway: a semantic systems biology tool for the life sciences.

BackgroundLife scientists need help in coping with the plethora of fast growing and scattered knowledge resources. Ideally, this knowledge should be integrated in a form that allows them to pose complex questions that address the properties of biological systems, independently from the origin of the knowledge. Semantic Web technologies prove to be well suited for knowledge integration, knowledge production (hypothesis formulation), knowledge querying and knowledge maintenance.ResultsWe implemented a semantically integrated resource named BioGateway, comprising the entire set of the OBO foundry candidate ontologies, the GO annotation files, the SWISS-PROT protein set, the NCBI taxonomy and several in-house ontologies. BioGateway provides a single entry point to query these resources through SPARQL. It constitutes a key component for a Semantic Systems Biology approach to generate new hypotheses concerning systems properties. In the course of developing BioGateway, we faced challenges that are common to other projects that involve large datasets in diverse representations. We present a detailed analysis of the obstacles that had to be overcome in creating BioGateway. We demonstrate the potential of a comprehensive application of Semantic Web technologies to global biomedical data.ConclusionThe time is ripe for launching a community effort aimed at a wider acceptance and application of Semantic Web technologies in the life sciences. We call for the creation of a forum that strives to implement a truly semantic life science foundation for Semantic Systems Biology.Access to the system and supplementary information (such as a listing of the data sources in RDF, and sample queries) can be found at http://www.semantic-systems-biology.org/biogateway.

The Cell Cycle Ontology: an application ontology for the representation and integrated analysis of the cell cycle process

The Cell Cycle Ontology (http://www.CellCycleOntology.org) is an application ontology that automatically captures and integrates detailed knowledge on the cell cycle process. Cell Cycle Ontology is enabled by semantic web technologies, and is accessible via the web for browsing, visualizing, advanced querying, and computational reasoning. Cell Cycle Ontology facilitates a detailed analysis of cell cycle-related molecular network components. Through querying and automated reasoning, it may provide new hypotheses to help steer a systems biology approach to biological network building.

BioHackathon series in 2011 and 2012: penetration of ontology and linked data in life science domains

The application of semantic technologies to the integration of biological data and the interoperability of bioinformatics analysis and visualization tools has been the common theme of a series of annual BioHackathons hosted in Japan for the past five years. Here we provide a review of the activities and outcomes from the BioHackathons held in 2011 in Kyoto and 2012 in Toyama. In order to efficiently implement semantic technologies in the life sciences, participants formed various sub-groups and worked on the following topics: Resource Description Framework (RDF) models for specific domains, text mining of the literature, ontology development, essential metadata for biological databases, platforms to enable efficient Semantic Web technology development and interoperability, and the development of applications for Semantic Web data. In this review, we briefly introduce the themes covered by these sub-groups. The observations made, conclusions drawn, and software development projects that emerged from these activities are discussed.

Reasoning with bio-ontologies

MOTIVATION Ontologies have become indispensable in the Life Sciences for managing large amounts of knowledge. The use of logics in ontologies ranges from sound modelling to practical querying of that knowledge, thus adding a considerable value. We conceive reasoning on bio-ontologies as a semi-automated process in three steps: (i) defining a logic-based representation language; (ii) building a consistent ontology using that language; and (iii) exploiting the ontology through querying. RESULTS Here, we report on how we have implemented this approach to reasoning on the OBO Foundry ontologies within BioGateway, a biological Resource Description Framework knowledge base. By separating the three steps in a manual curation effort on Metarel, a vocabulary that specifies relation semantics, we were able to apply reasoning on a large scale. Starting from an initial 401 million triples, we inferred about 158 million knowledge statements that allow for a myriad of prospective queries, potentially leading to new hypotheses about for instance gene products, processes, interactions or diseases. AVAILABILITY SPARUL code, a query end point and curated relation types in OBO Format, RDF and OWL 2 DL are freely available at http://www.semantic-systems-biology.org/metarel.

Gauging triple stores with actual biological data

BackgroundSemantic Web technologies have been developed to overcome the limitations of the current Web and conventional data integration solutions. The Semantic Web is expected to link all the data present on the Internet instead of linking just documents. One of the foundations of the Semantic Web technologies is the knowledge representation language Resource Description Framework (RDF). Knowledge expressed in RDF is typically stored in so-called triple stores (also known as RDF stores), from which it can be retrieved with SPARQL, a language designed for querying RDF-based models. The Semantic Web technologies should allow federated queries over multiple triple stores. In this paper we compare the efficiency of a set of biologically relevant queries as applied to a number of different triple store implementations.ResultsPreviously we developed a library of queries to guide the use of our knowledge base Cell Cycle Ontology implemented as a triple store. We have now compared the performance of these queries on five non-commercial triple stores: OpenLink Virtuoso (Open-Source Edition), Jena SDB, Jena TDB, SwiftOWLIM and 4Store. We examined three performance aspects: the data uploading time, the query execution time and the scalability. The queries we had chosen addressed diverse ontological or biological questions, and we found that individual store performance was quite query-specific. We identified three groups of queries displaying similar behaviour across the different stores: 1) relatively short response time queries, 2) moderate response time queries and 3) relatively long response time queries. SwiftOWLIM proved to be a winner in the first group, 4Store in the second one and Virtuoso in the third one.ConclusionsOur analysis showed that some queries behaved idiosyncratically, in a triple store specific manner, mainly with SwiftOWLIM and 4Store. Virtuoso, as expected, displayed a very balanced performance - its load time and its response time for all the tested queries were better than average among the selected stores; it showed a very good scalability and a reasonable run-to-run reproducibility. Jena SDB and Jena TDB were consistently slower than the other three implementations. Our analysis demonstrated that most queries developed for Virtuoso could be successfully used for other implementations.

A cell-cycle knowledge integration framework

The goal of the EU FP6 project DIAMONDS is to build a computational platform for studying the cell-cycle regulation process in several different (model) organisms (S. cerevisiae, S. pombe, A. thaliana and human). This platform will enable wet-lab biologists to use a systems biology approach encompassing data integration, modeling and simulation, thereby supporting analysis and interpretation of biochemical pathways involved in the cell cycle. To facilitate the computational handling of cell-cycle specific knowledge a detailed cell-cycle ontology is essential. The currently existing cell-cycle branch of the Gene Ontology (GO) provides only a static view and it is not rich enough to support in-depth cell-cycle studies. In this work, an enhanced Cell-Cycle Ontology (CCO) is proposed as an extension to existing GO. Besides the classical add-ons given by an ontology (data repository, knowledge sharing, validation, annotation, and so on), CCO is intended to further evolve into a knowledge-based system that provides reasoning services oriented to hypotheses evaluation in the context of cell-cycle studies. A data integration pipeline prototype, covering the entire life cycle of the knowledge base, is presented. Concrete problems and initial results related to the implementation of automatic format mappings between ontologies and inconsistency checking issues are discussed in detail.

ONTO-ToolKit: enabling bio-ontology engineering via Galaxy

BackgroundThe biosciences increasingly face the challenge of integrating a wide variety of available data, information and knowledge in order to gain an understanding of biological systems. Data integration is supported by a diverse series of tools, but the lack of a consistent terminology to label these data still presents significant hurdles. As a consequence, much of the available biological data remains disconnected or worse: becomes misconnected. The need to address this terminology problem has spawned the building of a large number of bio-ontologies. OBOF, RDF and OWL are among the most used ontology formats to capture terms and relationships in the Life Sciences, opening the potential to use the Semantic Web to support data integration and further exploitation of integrated resources via automated retrieval and reasoning procedures.MethodsWe extended the Perl suite ONTO-PERL and functionally integrated it into the Galaxy platform. The resulting ONTO-ToolKit supports the analysis and handling of OBO-formatted ontologies via the Galaxy interface, and we demonstrated its functionality in different use cases that illustrate the flexibility to obtain sets of ontology terms that match specific search criteria.ResultsONTO-ToolKit is available as a tool suite for Galaxy. Galaxy not only provides a user friendly interface allowing the interested biologist to manipulate OBO ontologies, it also opens up the possibility to perform further biological (and ontological) analyses by using other tools available within the Galaxy environment. Moreover, it provides tools to translate OBO-formatted ontologies into Semantic Web formats such as RDF and OWL.ConclusionsONTO-ToolKit reaches out to researchers in the biosciences, by providing a user-friendly way to analyse and manipulate ontologies. This type of functionality will become increasingly important given the wealth of information that is becoming available based on ontologies.

OPPL-Galaxy, a Galaxy tool for enhancing ontology exploitation as part of bioinformatics workflows

BackgroundBiomedical ontologies are key elements for building up the Life Sciences Semantic Web. Reusing and building biomedical ontologies requires flexible and versatile tools to manipulate them efficiently, in particular for enriching their axiomatic content. The Ontology Pre Processor Language (OPPL) is an OWL-based language for automating the changes to be performed in an ontology. OPPL augments the ontologists’ toolbox by providing a more efficient, and less error-prone, mechanism for enriching a biomedical ontology than that obtained by a manual treatment.ResultsWe present OPPL-Galaxy, a wrapper for using OPPL within Galaxy. The functionality delivered by OPPL (i.e. automated ontology manipulation) can be combined with the tools and workflows devised within the Galaxy framework, resulting in an enhancement of OPPL. Use cases are provided in order to demonstrate OPPL-Galaxy’s capability for enriching, modifying and querying biomedical ontologies.ConclusionsCoupling OPPL-Galaxy with other bioinformatics tools of the Galaxy framework results in a system that is more than the sum of its parts. OPPL-Galaxy opens a new dimension of analyses and exploitation of biomedical ontologies, including automated reasoning, paving the way towards advanced biological data analyses.