Ontologies for data and knowledge sharing in biology: plant ROS signaling as a case study

Modern technologies have rapidly transformed biology into a data-intensive discipline. In addition to the enormous amounts of existing experimental data in the literature, every new study can produce a large amount of new data, resulting in novel ideas and more publications. In order to understand a biological process as completely as possible, scientists should be able to combine and analyze all such information. Not only molecular biology and bioinformatics, but all the other domains of biology including plant biology, require tools and technologies that enable experts to capture knowledge within distributed and heterogeneous sources of information. Ontologies have proven to be one of the most-useful means of constructing and formalizing expert knowledge. The key feature of an ontology is that it represents a computer-interpretable model of a particular subject area. This article outlines the importance of ontologies for systems biology, data integration and information analyses, as illustrated through the example of reactive oxygen species (ROS) signaling networks in plants.     

The semantic metadatabase (SEMEDA): ontology based integration of federated molecular biological data sources

A system for "intelligent" semantic integration and querying of federated databases is being implemented by using three main components: A component which enables SQL access to integrated databases by database federation (MARGBench), an ontology based semantic metadatabase (SEMEDA) and an ontology based query interface (SEMEDA-query). In this publication we explain and demonstrate the principles, architecture and the use of SEMEDA. Since SEMEDA is implemented as 3 tiered web application database providers can enter all relevant semantic and technical information about their databases by themselves via a web browser. SEMEDA' s collaborative ontology editing feature is not restricted to database integration, and might also be useful for ongoing ontology developments, such as the "Gene Ontology" [2]. SEMEDA can be found at http://www-bm.cs.uni-magdeburg.de/semeda/. We explain how this ontologically structured information can be used for semantic database integration. In addition, requirements to ontologies for molecular biological database integration are discussed and relevant existing ontologies are evaluated. We further discuss how ontologies and structured knowledge sources can be used in SEMEDA and whether they can be merged supplemented or updated to meet the requirements for semantic database integration.     

Bio-ontologies: current trends and future directions

In recent years, as a knowledge-based discipline, bioinformatics has been made more computationally amenable. After its beginnings as a technology advocated by computer scientists to overcome problems of heterogeneity, ontology has been taken up by biologists themselves as a means to consistently annotate features from genotype to phenotype. In medical informatics, artifacts called ontologies have been used for a longer period of time to produce controlled lexicons for coding schemes. In this article, we review the current position in ontologies and how they have become institutionalized within biomedicine. As the field has matured, the much older philosophical aspects of ontology have come into play. With this and the institutionalization of ontology has come greater formality. We review this trend and what benefits it might bring to ontologies and their use within biomedicine.     

Ontologies: Formalising biological knowledge for bioinformatics

An ontology is a domain of knowledge structured through formal rules so that it can be interpreted and used by computers. Ontologies are becoming increasingly important in bioinformatics because they can be linked to the information in databases and their knowledge then used to query the databases. Typical examples in current use are the Gene Ontology, which incorporates much of our knowledge about gene products, and ontologies of developmental anatomy, which, for example, facilitate tissue-based queries to gene expression databases both textually and spatially. This article considers the production, formulation and types of bio-ontologies together with the reasons why they are so useful.     

A top-level ontology of functions and its application in the Open Biomedical Ontologies

MOTIVATION: A clear understanding of functions in biology is a key component in accurate modelling of molecular, cellular and organismal biology. Using the existing biomedical ontologies it has been impossible to capture the complexity of the community's knowledge about biological functions. RESULTS: We present here a top-level ontological framework for representing knowledge about biological functions. This framework lends greater accuracy, power and expressiveness to biomedical ontologies by providing a means to capture existing functional knowledge in a more formal manner. An initial major application of the ontology of functions is the provision of a principled way in which to curate functional knowledge and annotations in biomedical ontologies. Further potential applications include the facilitation of ontology interoperability and automated reasoning. A major advantage of the proposed implementation is that it is an extension to existing biomedical ontologies, and can be applied without substantial changes to these domain ontologies. AVAILABILITY: The Ontology of Functions (OF) can be downloaded in OWL format from http://onto.eva.mpg.de/. Additionally, a UML profile and supplementary information and guides for using the OF can be accessed from the same website.     

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.     

Evolving strategies for the incorporation of bioinformatics within the undergraduate cell biology curriculum

Recent advances in genomics and structural biology have resulted in an unprecedented increase in biological data available from Internet-accessible databases. In order to help students effectively use this vast repository of information, undergraduate biology students at Drake University were introduced to bioinformatics software and databases in three courses, beginning with an introductory course in cell biology. The exercises and projects that were used to help students develop literacy in bioinformatics are described. In a recently offered course in bioinformatics, students developed their own simple sequence analysis tool using the Perl programming language. These experiences are described from the point of view of the instructor as well as the students. A preliminary assessment has been made of the degree to which students had developed a working knowledge of bioinformatics concepts and methods. Finally, some conclusions have been drawn from these courses that may be helpful to instructors wishing to introduce bioinformatics within the undergraduate biology curriculum.     

Ontologies of developmental anatomy: their current and future roles

A central problem in current biology is elucidating the molecular networks that drive developmental change and physiological function. Such knowledge is needed partly to understand these networks, partly to be able to manipulate them, and partly to understand and help treat those human congenital abnormalities that arise as a result of mutation. Thus far, bioinformatics technology has been of limited use in this enterprise, mainly because its core focus has been on sequence technology and data archiving. For bioinformatics to be of use in this next tier of investigations, genetic and protein data need to be both archived and searchable by tissue since this is the level at which these networks operate. The resulting databases in turn require ontologies of developmental anatomy that can provide the formal infrastructure for handling gene expression, microarray and other tissue-based data. Here, the progress in making such ontologies, particularly for the developing mouse, is reported and the uses to which they are and will be put, together with the resources and tools currently available for investigating molecular networks and the genetic basis of congenital abnormalities, are considered.     

Biological ontologies in rice databases. An introduction to the activities in Gramene and Oryzabase

An enormous amount of information and materials in the field of biology has been accumulating, such as nucleotide and amino acid sequences, gene and protein functions, mutants and their phenotypes, and literature references, produced by the rapid development in this field. Effective use of the information may strongly promote biological studies, and may lead to many important findings. It is, however, time-consuming and laborious for individual researchers to collect information from individual original sites and to rearrange it for their own purpose. A concept, ontology, has been introduced in biology to support and encourage researchers to share and reuse information among biological databases. Ontology has a glossary, named dynamic controlled vocabulary, in which relationships between terms are defined. Since each term is strictly defined and identified with an ID number, a set of data represented in biological ontology is easily accessible to automated information processing, even if the data sets are across several databases and/or different organisms. In this mini-review, we introduce activities in Gramene and Oryzabase, which provide biological ontologies for Oryza sativa (rice).     

The outcomes of pathway database computations depend on pathway ontology

Different biological notions of pathways are used in different pathway databases. Those pathway ontologies significantly impact pathway computations. Computational users of pathway databases will obtain different results depending on the pathway ontology used by the databases they employ, and different pathway ontologies are preferable for different end uses. We explore differences in pathway ontologies by comparing the BioCyc and KEGG ontologies. The BioCyc ontology defines a pathway as a conserved, atomic module of the metabolic network of a single organism, i.e. often regulated as a unit, whose boundaries are defined at high-connectivity stable metabolites. KEGG pathways are on average 4.2 times larger than BioCyc pathways, and combine multiple biological processes from different organisms to produce a substrate-centered reaction mosaic. We compared KEGG and BioCyc pathways using genome context methods, which determine the functional relatedness of pairs of genes. For each method we employed, a pair of genes randomly selected from a BioCyc pathway is more likely to be related by that method than is a pair of genes randomly selected from a KEGG pathway, supporting the conclusion that the BioCyc pathway conceptualization is closer to a single conserved biological process than is that of KEGG.     

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.     

Ontologies for molecular biology and bioinformatics

About five years ago, ontology was almost unknown in bioinformatics, even more so in molecular biology. Nowadays, many bioinformatics articles mention it in connection with text mining, data integration or as a metaphysical cure for problems in standardisation of nomenclature and other applications. This article attempts to give an account of what concept ontologies in the domain of biology and bioinformatics are; what they are not; how they can be constructed; how they can be used; and some fallacies and pitfalls creators and users should be aware of.     

Ontology annotation: mapping genomic regions to biological function

With numerous whole genomes now in hand, and experimental data about genes and biological pathways on the increase, a systems approach to biological research is becoming essential. Ontologies provide a formal representation of knowledge that is amenable to computational as well as human analysis, an obvious underpinning of systems biology. Mapping function to gene products in the genome consists of two, somewhat intertwined enterprises: ontology building and ontology annotation. Ontology building is the formal representation of a domain of knowledge; ontology annotation is association of specific genomic regions (which we refer to simply as 'genes', including genes and their regulatory elements and products such as proteins and functional RNAs) to parts of the ontology. We consider two complementary representations of gene function: the Gene Ontology (GO) and pathway ontologies. GO represents function from the gene's eye view, in relation to a large and growing context of biological knowledge at all levels. Pathway ontologies represent function from the point of view of biochemical reactions and interactions, which are ordered into networks and causal cascades. The more mature GO provides an example of ontology annotation: how conclusions from the scientific literature and from evolutionary relationships are converted into formal statements about gene function. Annotations are made using a variety of different types of evidence, which can be used to estimate the relative reliability of different annotations.     

On gene ontology and function annotation

The effort of function annotation does not merely involve associating a gene with some structured vocabulary that describes action. Rather the details of the actions, the components of the actions, the larger context of the actions are important issues that are of direct relevance, because they help understand the biological system to which the gene/protein belongs. Currently Gene Ontology (GO) Consortium offers the most comprehensive sets of relationships to describe gene/protein activity. However, its choice to segregate gene ontology to subdomains of molecular function, biological process and cellular component is creating significant limitations in terms of future scope of use. If we are to understand biology in its total complexity, comprehensive ontologies in larger biological domains are essential. A vigorous discussion on this topic is necessary for the larger benefit of the biological community. I highlight this point because larger-bio-domain ontologies cannot be simply created by integrating subdomain ontologies. Relationships in larger bio-domain-ontologies are more complex due to larger size of the system and are therefore more labor intensive to create. The current limitations of GO will be a handicap in derivation of more complex relationships from the high throughput biology data.     

Swami一新一代生物学平台

生物信息学的发展产生了越来越多的数据库和生物学软件,研究人员在应用这些生物学工具处理实验数据时需要大量的时间解决数据格式转换和管理等问题。本文介绍了一种交互式的基于网络的新一代生物信息学分析平台一Swam,它综合了主要的生物信息学数据库和软件,可以加速数据处理并帮助用户管理数据。因此它将推动生物信息学向更深层次发展。     

Biomedical ontologies: a functional perspective

The information explosion in biology makes it difficult for researchers to stay abreast of current biomedical knowledge and to make sense of the massive amounts of online information. Ontologies--specifications of the entities, their attributes and relationships among the entities in a domain of discourse--are increasingly enabling biomedical researchers to accomplish these tasks. In fact, bio-ontologies are beginning to proliferate in step with accruing biological data. The myriad of ontologies being created enables researchers not only to solve some of the problems in handling the data explosion but also introduces new challenges. One of the key difficulties in realizing the full potential of ontologies in biomedical research is the isolation of various communities involved: some workers spend their career developing ontologies and ontology-related tools, while few researchers (biologists and physicians) know how ontologies can accelerate their research. The objective of this review is to give an overview of biomedical ontology in practical terms by providing a functional perspective--describing how bio-ontologies can and are being used. As biomedical scientists begin to recognize the many different ways ontologies enable biomedical research, they will drive the emergence of new computer applications that will help them exploit the wealth of research data now at their fingertips.     

Bio-Ontology and text: bridging the modeling gap

MOTIVATION: Natural language processing (NLP) techniques are increasingly being used in biology to automate the capture of new biological discoveries in text, which are being reported at a rapid rate. Yet, information represented in NLP data structures is classically very different from information organized with ontologies as found in model organisms or genetic databases. To facilitate the computational reuse and integration of information buried in unstructured text with that of genetic databases, we propose and evaluate a translational schema that represents a comprehensive set of phenotypic and genetic entities, as well as their closely related biomedical entities and relations as expressed in natural language. In addition, the schema connects different scales of biological information, and provides mappings from the textual information to existing ontologies, which are essential in biology for integration, organization, dissemination and knowledge management of heterogeneous phenotypic information. A common comprehensive representation for otherwise heterogeneous phenotypic and genetic datasets, such as the one proposed, is critical for advancing systems biology because it enables acquisition and reuse of unprecedented volumes of diverse types of knowledge and information from text. RESULTS: A novel representational schema, PGschema, was developed that enables translation of phenotypic, genetic and their closely related information found in textual narratives to a well-defined data structure comprising phenotypic and genetic concepts from established ontologies along with modifiers and relationships. Evaluation for coverage of a selected set of entities showed that 90% of the information could be represented (95% confidence interval: 86-93%; n = 268). Moreover, PGschema can be expressed automatically in an XML format using natural language techniques to process the text. To our knowledge, we are providing the first evaluation of a translational schema for NLP that contains declarative knowledge about genes and their associated biomedical data (e.g. phenotypes). AVAILABILITY: http://zellig.cpmc.columbia.edu/PGschema