The Plant Ontology Consortium and plant ontologies

The goal of the Plant Ontology Consortium is to produce structured controlled vocabularies, arranged in ontologies, that can be applied to plant-based database information even as knowledge of the biology of the relevant plant taxa (e.g. development, anatomy, morphology, genomics, proteomics) is accumulating and changing. The collaborators of the Plant Ontology Consortium (POC) represent a number of core participant database groups. The Plant Ontology Consortium is expanding the paradigm of the Gene Ontology Consortium (http://www.geneontology.org). Various trait ontologies (agronomic traits, mutant phenotypes, phenotypes, traits, and QTL) and plant ontologies (plant development, anatomy [incl. morphology]) for several taxa (Arabidopsis, maize/corn/Zea mays and rice/Oryza) are under development. The products of the Plant Ontology Consortium will be open-source.     

OLS Client and OLS Dialog: Open Source Tools to Annotate Public Omics Datasets

The availability of user‐friendly software to annotate biological datasets and experimental details is becoming essential in data management practices, both in local storage systems and in public databases. The Ontology Lookup Service (OLS, http://www.ebi.ac.uk/ols ) is a popular centralized service to query, browse and navigate biomedical ontologies and controlled vocabularies. Recently, the OLS framework has been completely redeveloped (version 3.0), including enhancements in the data model, like the added support for Web Ontology Language based ontologies, among many other improvements. However, the new OLS is not backwards compatible and new software tools are needed to enable access to this widely used framework now that the previous version is no longer available. We here present the OLS Client as a free, open‐source Java library to retrieve information from the new version of the OLS. It enables rapid tool creation by providing a robust, pluggable programming interface and common data model to programmatically access the OLS. The library has already been integrated and is routinely used by several bioinformatics resources and related data annotation tools. Secondly, we also introduce an updated version of the OLS Dialog (version 2.0), a Java graphical user interface that can be easily plugged into Java desktop applications to access the OLS. The software and related documentation are freely available at https://github.com/PRIDE-Utilities/ols-client and https://github.com/PRIDE-Toolsuite/ols-dialog .     

The PSI semantic validator: A framework to check MIAPE compliance of proteomics data

The Human Proteome Organization's Proteomics Standards Initiative (PSI) promotes the development of exchange standards to improve data integration and interoperability. PSI specifies the suitable level of detail required when reporting a proteomics experiment (via the Minimum Information About a Proteomics Experiment), and provides extensible markup language (XML) exchange formats and dedicated controlled vocabularies (CVs) that must be combined to generate a standard compliant document. The framework presented here tackles the issue of checking that experimental data reported using a specific format, CVs and public bio‐ontologies (e.g. Gene Ontology, NCBI taxonomy) are compliant with the Minimum Information About a Proteomics Experiment recommendations. The semantic validator not only checks the XML syntax but it also enforces rules regarding the use of an ontology class or CV terms by checking that the terms exist in the resource and that they are used in the correct location of a document. Moreover, this framework is extremely fast, even on sizable data files, and flexible, as it can be adapted to any standard by customizing the parameters it requires: an XML Schema Definition, one or more CVs or ontologies, and a mapping file describing in a formal way how the semantic resources and the format are interrelated. As such, the validator provides a general solution to the common problem in data exchange: how to validate the correct usage of a data standard beyond simple XML Schema Definition validation. The framework source code and its various applications can be found at http://psidev.info/validator .     

FuGEFlow: data model and markup language for flow cytometry

Background  

Flow cytometry technology is widely used in both health care and research. The rapid expansion of flow cytometry applications has outpaced the development of data storage and analysis tools. Collaborative efforts being taken to eliminate this gap include building common vocabularies and ontologies, designing generic data models, and defining data exchange formats. The Minimum Information about a Flow Cytometry Experiment (MIFlowCyt) standard was recently adopted by the International Society for Advancement of Cytometry. This standard guides researchers on the information that should be included in peer reviewed publications, but it is insufficient for data exchange and integration between computational systems. The Functional Genomics Experiment (FuGE) formalizes common aspects of comprehensive and high throughput experiments across different biological technologies. We have extended FuGE object model to accommodate flow cytometry data and metadata.     

Plant Ontology (PO): a Controlled Vocabulary of Plant Structures and Growth Stages

The Plant Ontology Consortium (POC) (www.plantontology.org) is a collaborative effort among several plant databases and experts in plant systematics, botany and genomics. A primary goal of the POC is to develop simple yet robust and extensible controlled vocabularies that accurately reflect the biology of plant structures and developmental stages. These provide a network of vocabularies linked by relationships (ontology) to facilitate queries that cut across datasets within a database or between multiple databases. The current version of the ontology integrates diverse vocabularies used to describe Arabidopsis, maize and rice (Oryza sp.) anatomy, morphology and growth stages. Using the ontology browser, over 3500 gene annotations from three species-specific databases, The Arabidopsis Information Resource (TAIR) for Arabidopsis, Gramene for rice and MaizeGDB for maize, can now be queried and retrieved.     

The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration

The value of any kind of data is greatly enhanced when it exists in a form that allows it to be integrated with other data. One approach to integration is through the annotation of multiple bodies of data using common controlled vocabularies or 'ontologies'. Unfortunately, the very success of this approach has led to a proliferation of ontologies, which itself creates obstacles to integration. The Open Biomedical Ontologies (OBO) consortium is pursuing a strategy to overcome this problem. Existing OBO ontologies, including the Gene Ontology, are undergoing coordinated reform, and new ontologies are being created on the basis of an evolving set of shared principles governing ontology development. The result is an expanding family of ontologies designed to be interoperable and logically well formed and to incorporate accurate representations of biological reality. We describe this OBO Foundry initiative and provide guidelines for those who might wish to become involved.     

National Center for Biomedical Ontology: advancing biomedicine through structured organization of scientific knowledge

The National Center for Biomedical Ontology is a consortium that comprises leading informaticians, biologists, clinicians, and ontologists, funded by the National Institutes of Health (NIH) Roadmap, to develop innovative technology and methods that allow scientists to record, manage, and disseminate biomedical information and knowledge in machine-processable form. The goals of the Center are (1) to help unify the divergent and isolated efforts in ontology development by promoting high quality open-source, standards-based tools to create, manage, and use ontologies, (2) to create new software tools so that scientists can use ontologies to annotate and analyze biomedical data, (3) to provide a national resource for the ongoing evaluation, integration, and evolution of biomedical ontologies and associated tools and theories in the context of driving biomedical projects (DBPs), and (4) to disseminate the tools and resources of the Center and to identify, evaluate, and communicate best practices of ontology development to the biomedical community. Through the research activities within the Center, collaborations with the DBPs, and interactions with the biomedical community, our goal is to help scientists to work more effectively in the e-science paradigm, enhancing experiment design, experiment execution, data analysis, information synthesis, hypothesis generation and testing, and understand human disease.     

Physical properties of biological entities: an introduction to the ontology of physics for biology

As biomedical investigators strive to integrate data and analyses across spatiotemporal scales and biomedical domains, they have recognized the benefits of formalizing languages and terminologies via computational ontologies. Although ontologies for biological entities-molecules, cells, organs-are well-established, there are no principled ontologies of physical properties-energies, volumes, flow rates-of those entities. In this paper, we introduce the Ontology of Physics for Biology (OPB), a reference ontology of classical physics designed for annotating biophysical content of growing repositories of biomedical datasets and analytical models. The OPB's semantic framework, traceable to James Clerk Maxwell, encompasses modern theories of system dynamics and thermodynamics, and is implemented as a computational ontology that references available upper ontologies. In this paper we focus on the OPB classes that are designed for annotating physical properties encoded in biomedical datasets and computational models, and we discuss how the OPB framework will facilitate biomedical knowledge integration.     

Proteomics and Beyond: a report on the 3rd Annual Spring Workshop of the HUPO-PSI 21-23 April 2006, San Francisco, CA, USA

The theme of the third annual Spring workshop of the HUPO-PSI was "proteomics and beyond" and its underlying goal was to reach beyond the boundaries of the proteomics community to interact with groups working on the similar issues of developing interchange standards and minimal reporting requirements. Significant developments in many of the HUPO-PSI XML interchange formats, minimal reporting requirements and accompanying controlled vocabularies were reported, with many of these now feeding into the broader efforts of the Functional Genomics Experiment (FuGE) data model and Functional Genomics Ontology (FuGO) ontologies.     

Ontology based document enrichment in bioinformatics

Controlled vocabularies are common within bioinformatics resources. They can be used to give a summary of the knowledge held about a particular entity. They are also used to constrain values given for particular attributes of an entity. This helps create a shared understanding of a domain and aids increased precision and recall during querying of resources. Ontologies can also provide such facilities, but can also enhance their utility. Controlled vocabularies are often simply lists of words, but may be viewed as a kind of ontology. Ideally ontologies are structurally enriched with relationships between terms within the vocabulary. Use of such rich forms of vocabularies in database annotation could enhance those resources usability by both humans and computers. The representation of the knowledge content of biological resources in a computationally accessible form opens the prospect of greater support for a biologist investigating new data.     

Folk Zoological Life-Forms: Their Universality and Growth

Folk zoological life-form terms, like folk botanical life-form terms (Brown 1977a), are added to languages in a highly regular manner. Life-forms of the triad FISH, BIRD, and SNAKE are lexically encoded first, although in no particular order, followed by WUG (e.g., American English hug) and then MAMMAL. Four general principles of naming-behavior underlie these regularities: (1) criteria clustering; (2) conjunctivity (including binary opposition); (3) dimension salience; and (4) marking. In addition, size of folk zoological life-form vocabularies is positively correlated with societal complexity. This is caused by the decay of folk biological taxonomies as societies become more complex. [cognitive anthropology, ethnobiology, folk classification, language universals, language change]     

The future role of bio-ontologies for developing a general data standard in biology: chance and challenge for zoo-morphology

Due to lack of common data standards, the communicability and comparability of biological data across various levels of organization and taxonomic groups is continuously decreasing. However, the interdependence between molecular and higher levels of organization is of growing interest and calls for co-operations between biologists from different methodological and theoretical backgrounds. A general data standard in biology would greatly facilitate such co-operations. This article examines the role that defined and formalized vocabularies (i.e., ontologies) could have in developing such a data standard. I suggest basic criteria for developing data standards on grounds of distinguishing content, concept, nomenclatural, and format standards and discuss the role of data bases and their use of bio-ontologies in current activities for data standardization in biology. General principles of ontology development are introduced, including foundational ontology properties (e.g. class–subclass, parthood), and how concepts are defined. After addressing problems that are specific to morphological data, the notion of a general structure concept for morphology is introduced and why it is required for developing a morphological ontology. The necessity for a general morphological ontology to be taxon-independent and free of homology assumptions is discussed and how it can solve the problems of morphology. The article concludes with an outlook on how the use of ontologies will likely establish some sort of general data standard in biology and why the development of a set of commonly used foundational ontology properties and the use of globally unique identifiers for all classes defined in ontologies is crucial for its success.     

Quality control for terms and definitions in ontologies and taxonomies

Background  

Ontologies and taxonomies are among the most important computational resources for molecular biology and bioinformatics. A series of recent papers has shown that the Gene Ontology (GO), the most prominent taxonomic resource in these fields, is marked by flaws of certain characteristic types, which flow from a failure to address basic ontological principles. As yet, no methods have been proposed which would allow ontology curators to pinpoint flawed terms or definitions in ontologies in a systematic way.     

The MGED Ontology: a resource for semantics-based description of microarray experiments

MOTIVATION: The generation of large amounts of microarray data and the need to share these data bring challenges for both data management and annotation and highlights the need for standards. MIAME specifies the minimum information needed to describe a microarray experiment and the Microarray Gene Expression Object Model (MAGE-OM) and resulting MAGE-ML provide a mechanism to standardize data representation for data exchange, however a common terminology for data annotation is needed to support these standards. RESULTS: Here we describe the MGED Ontology (MO) developed by the Ontology Working Group of the Microarray Gene Expression Data (MGED) Society. The MO provides terms for annotating all aspects of a microarray experiment from the design of the experiment and array layout, through to the preparation of the biological sample and the protocols used to hybridize the RNA and analyze the data. The MO was developed to provide terms for annotating experiments in line with the MIAME guidelines, i.e. to provide the semantics to describe a microarray experiment according to the concepts specified in MIAME. The MO does not attempt to incorporate terms from existing ontologies, e.g. those that deal with anatomical parts or developmental stages terms, but provides a framework to reference terms in other ontologies and therefore facilitates the use of ontologies in microarray data annotation. AVAILABILITY: The MGED Ontology version.1.2.0 is available as a file in both DAML and OWL formats at http://mged.sourceforge.net/ontologies/index.php. Release notes and annotation examples are provided. The MO is also provided via the NCICB's Enterprise Vocabulary System (http://nciterms.nci.nih.gov/NCIBrowser/Dictionary.do). CONTACT: Stoeckrt@pcbi.upenn.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

An upper-level ontology for the biomedical domain

At the US National Library of Medicine we have developed the Unified Medical Language System (UMLS), whose goal it is to provide integrated access to a large number of biomedical resources by unifying the vocabularies that are used to access those resources. The UMLS currently interrelates some 60 controlled vocabularies in the biomedical domain. The UMLS coverage is quite extensive, including not only many concepts in clinical medicine, but also a large number of concepts applicable to the broad domain of the life sciences. In order to provide an overarching conceptual framework for all UMLS concepts, we developed an upper-level ontology, called the UMLS semantic network. The semantic network, through its 134 semantic types, provides a consistent categorization of all concepts represented in the UMLS. The 54 links between the semantic types provide the structure for the network and represent important relationships in the biomedical domain. Because of the growing number of information resources that contain genetic information, the UMLS coverage in this area is being expanded. We recently integrated the taxonomy of organisms developed by the NLM's National Center for Biotechnology Information, and we are currently working together with the developers of the Gene Ontology to integrate this resource, as well. As additional, standard, ontologies become publicly available, we expect to integrate these into the UMLS construct.     

SPARQL Assist language-neutral query composer

Background

More than one million terms from biomedical ontologies and controlled vocabularies are available through the Ontology Lookup Service (OLS). Although OLS provides ample possibility for querying and browsing terms, the visualization of parts of the ontology graphs is rather limited and inflexible.

Results

We created the OLSVis web application, a visualiser for browsing all ontologies available in the OLS database. OLSVis shows customisable subgraphs of the OLS ontologies. Subgraphs are animated via a real-time force-based layout algorithm which is fully interactive: each time the user makes a change, e.g. browsing to a new term, hiding, adding, or dragging terms, the algorithm performs smooth and only essential reorganisations of the graph. This assures an optimal viewing experience, because subsequent screen layouts are not grossly altered, and users can easily navigate through the graph. URL: http://ols.wordvis.com

Conclusions

The OLSVis web application provides a user-friendly tool to visualise ontologies from the OLS repository. It broadens the possibilities to investigate and select ontology subgraphs through a smooth visualisation method.     

Owlifier: Creating OWL-DL ontologies from simple spreadsheet-based knowledge descriptions

Discovery and integration of data is important in many ecological studies, especially those that concern broad-scale ecological questions. Data discovery and integration are often difficult and time consuming tasks for researchers, which is due in part to the use of informal, ambiguous, and sometimes inconsistent terms for describing data content. Ontologies offer a solution to this problem by providing consistent definitions of ecological concepts that in turn can be used to annotate, relate, and search for data sets. However, unlike in molecular biology or biomedicine, few ontology development efforts exist within ecology. Ontology development often requires considerable expertise in ontology languages and development tools, which is often a barrier for ontology creation in ecology. In this paper we describe an approach for ontology creation that allows ecologists to use common spreadsheet tools to describe different aspects of an ontology. We present conventions for creating, relating, and constraining concepts through spreadsheets, and provide software tools for converting these ontologies into equivalent OWL-DL representations. We also consider inverse translations, i.e., to convert ontologies represented using OWL-DL into our spreadsheet format. Our approach allows large lists of terms to be easily related and organized into concept hierarchies, and generally provides a more intuitive and natural interface for ontology development by ecologists.     

The Arabidopsis Information Resource (TAIR): a model organism database providing a centralized,curated gateway to Arabidopsis biology,research materials and community

Arabidopsis thaliana is the most widely-studied plant today. The concerted efforts of over 11 000 researchers and 4000 organizations around the world are generating a rich diversity and quantity of information and materials. This information is made available through a comprehensive on-line resource called the Arabidopsis Information Resource (TAIR) (http://arabidopsis.org), which is accessible via commonly used web browsers and can be searched and downloaded in a number of ways. In the last two years, efforts have been focused on increasing data content and diversity, functionally annotating genes and gene products with controlled vocabularies, and improving data retrieval, analysis and visualization tools. New information include sequence polymorphisms including alleles, germplasms and phenotypes, Gene Ontology annotations, gene families, protein information, metabolic pathways, gene expression data from microarray experiments and seed and DNA stocks. New data visualization and analysis tools include SeqViewer, which interactively displays the genome from the whole chromosome down to 10 kb of nucleotide sequence and AraCyc, a metabolic pathway database and map tool that allows overlaying expression data onto the pathway diagrams. Finally, we have recently incorporated seed and DNA stock information from the Arabidopsis Biological Resource Center (ABRC) and implemented a shopping-cart style on-line ordering system.