The Ontologies Community of Practice: A CGIAR Initiative for Big Data in Agrifood Systems.

Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labeling or annotation in order to be interoperable. As recommended by the FAIR principles, data, labels, and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no comprehensive full set of ontologies recommended for data annotation across agricultural research disciplines. In this paper, we discuss the added value of the Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture for harnessing relevant expertise in ontology development and identifying innovative solutions that support quality data annotation. The Ontologies CoP stimulates knowledge sharing among stakeholders, such as researchers, data managers, domain experts, experts in ontology design, and platform development teams.

BioHackathon 2015: Semantics of data for life sciences and reproducible research.

We report on the activities of the 2015 edition of the BioHackathon, an annual event that brings together researchers and developers from around the world to develop tools and technologies that promote the reusability of biological data. We discuss issues surrounding the representation, publication, integration, mining and reuse of biological data and metadata across a wide range of biomedical data types of relevance for the life sciences, including chemistry, genotypes and phenotypes, orthology and phylogeny, proteomics, genomics, glycomics, and metabolomics. We describe our progress to address ongoing challenges to the reusability and reproducibility of research results, and identify outstanding issues that continue to impede the progress of bioinformatics research. We share our perspective on the state of the art, continued challenges, and goals for future research and development for the life sciences Semantic Web.

Cancer Care Treatment Outcome Ontology: A Novel Computable Ontology for Profiling Treatment Outcomes in Patients With Solid Tumors.

PURPOSE: There is as yet no computer-processable resource to describe treatment end points in cancer, hindering our ability to systematically capture and share outcomes data to inform better patient care. To address these unmet needs, we have built an ontology, the Cancer Care Treatment Outcome Ontology (CCTOO), to organize high-level concepts of treatment end points with structured knowledge representation to facilitate standardized sharing of real-world data. METHODS: End points from oncology trials in ClinicalTrials.gov were extracted, queried using the keyword cancer, and followed by an expert appraisal. Synonyms and relevant terms were imported from the National Cancer Institute Thesaurus and Common Terminology Criteria for Adverse Events. Logical relationships among concepts were manually represented by production rules. The applicability of 1,847 rules was tested in an index case. RESULTS: After removing duplicated terms from 54,705 trial entries, an ontology holding 1,133 terms was built. CCTOO organized concepts into four domains (cancer treatment, health services, physical, and psychosocial health-related concepts), 13 subgroups (including efficacy, safety, and quality of life), and two (taxonomic and evaluative) concept hierarchies. This ontology has a comprehensive term coverage in the cancer trial literature: at least one term was mentioned in 98% of MEDLINE abstracts of phase I to III trials, whereas concepts about efficacy were mentioned in 7,208 (79%) phase I, 15,051 (92%) phase II, and 3,884 (86%) phase III trials. The event sequence of the index case was readily convertible to a comprehensive profile incorporating response, treatment toxicity, and survival by applying the set of production rules curated in the CCTOO. CONCLUSION: CCTOO categorizes high-level treatment end points used in oncology and provides a mechanism for profiling individual patient data by outcomes to facilitate translational analysis.

GFVO: the Genomic Feature and Variation Ontology.

Falling costs in genomic laboratory experiments have led to a steady increase of genomic feature and variation data. Multiple genomic data formats exist for sharing these data, and whilst they are similar, they are addressing slightly different data viewpoints and are consequently not fully compatible with each other. The fragmentation of data format specifications makes it hard to integrate and interpret data for further analysis with information from multiple data providers. As a solution, a new ontology is presented here for annotating and representing genomic feature and variation dataset contents. The Genomic Feature and Variation Ontology (GFVO) specifically addresses genomic data as it is regularly shared using the GFF3 (incl. FASTA), GTF, GVF and VCF file formats. GFVO simplifies data integration and enables linking of genomic annotations across datasets through common semantics of genomic types and relations. Availability and implementation. The latest stable release of the ontology is available via its base URI; previous and development versions are available at the ontology's GitHub repository: https://github.com/BioInterchange/Ontologies; versions of the ontology are indexed through BioPortal (without external class-/property-equivalences due to BioPortal release 4.10 limitations); examples and reference documentation is provided on a separate web-page: http://www.biointerchange.org/ontologies.html. GFVO version 1.0.2 is licensed under the CC0 1.0 Universal license (https://creativecommons.org/publicdomain/zero/1.0) and therefore de facto within the public domain; the ontology can be appropriated without attribution for commercial and non-commercial use.

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.

The 3rd DBCLS BioHackathon: improving life science data integration with Semantic Web technologies.

BACKGROUND: BioHackathon 2010 was the third in a series of meetings hosted by the Database Center for Life Sciences (DBCLS) in Tokyo, Japan. The overall goal of the BioHackathon series is to improve the quality and accessibility of life science research data on the Web by bringing together representatives from public databases, analytical tool providers, and cyber-infrastructure researchers to jointly tackle important challenges in the area of in silico biological research. RESULTS: The theme of BioHackathon 2010 was the 'Semantic Web', and all attendees gathered with the shared goal of producing Semantic Web data from their respective resources, and/or consuming or interacting those data using their tools and interfaces. We discussed on topics including guidelines for designing semantic data and interoperability of resources. We consequently developed tools and clients for analysis and visualization. CONCLUSION: We provide a meeting report from BioHackathon 2010, in which we describe the discussions, decisions, and breakthroughs made as we moved towards compliance with Semantic Web technologies - from source provider, through middleware, to the end-consumer.

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

BACKGROUND: Biomedical 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. RESULTS: We 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. CONCLUSIONS: Coupling 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.

The emergence of Semantic Systems Biology.

Over the past decade the biological sciences have been widely embracing Systems Biology and its various data integration approaches to discover new knowledge. Molecular Systems Biology aims to develop hypotheses based on integrated, or modelled data. These hypotheses can be subsequently used to design new experiments for testing, leading to an improved understanding of the biology; a more accurate model of the biological system and therefore an improved ability to develop hypotheses. During the same period the biosciences have also eagerly taken up the emerging Semantic Web as evidenced by the dedicated exploitation of Semantic Web technologies for data integration and sharing in the Life Sciences. We describe how these two approaches merged in Semantic Systems Biology: a data integration and analysis approach complementary to model-based Systems Biology. Semantic Systems Biology augments the integration and sharing of knowledge, and opens new avenues for computational support in quality checking and automated reasoning, and to develop new, testable hypotheses.

Gauging triple stores with actual biological data.

BACKGROUND: Semantic 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. RESULTS: Previously 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. CONCLUSIONS: Our 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.

Reasoning with bio-ontologies: using relational closure rules to enable practical querying.

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.

ONTO-ToolKit: enabling bio-ontology engineering via Galaxy.

BACKGROUND: The 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. METHODS: We 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. RESULTS: ONTO-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. CONCLUSIONS: ONTO-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.

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

BACKGROUND: Life 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. RESULTS: We 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. CONCLUSION: The 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.

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.

BACKGROUND: Bio-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. RESULTS: Analogous 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/). CONCLUSIONS: Ontology 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.

The aMAZE LightBench: a web interface to a relational database of cellular processes.

The aMAZE LightBench (http://www.amaze.ulb. ac.be/) is a web interface to the aMAZE relational database, which contains information on gene expression, catalysed chemical reactions, regulatory interactions, protein assembly, as well as metabolic and signal transduction pathways. It allows the user to browse the information in an intuitive way, which also reflects the underlying data model. Moreover links are provided to literature references, and whenever appropriate, to external databases.