Computable visually observed phenotype ontological framework for plants.

BACKGROUND: The ability to search for and precisely compare similar phenotypic appearances within and across species has vast potential in plant science and genetic research. The difficulty in doing so lies in the fact that many visual phenotypic data, especially visually observed phenotypes that often times cannot be directly measured quantitatively, are in the form of text annotations, and these descriptions are plagued by semantic ambiguity, heterogeneity, and low granularity. Though several bio-ontologies have been developed to standardize phenotypic (and genotypic) information and permit comparisons across species, these semantic issues persist and prevent precise analysis and retrieval of information. A framework suitable for the modeling and analysis of precise computable representations of such phenotypic appearances is needed. RESULTS: We have developed a new framework called the Computable Visually Observed Phenotype Ontological Framework for plants. This work provides a novel quantitative view of descriptions of plant phenotypes that leverages existing bio-ontologies and utilizes a computational approach to capture and represent domain knowledge in a machine-interpretable form. This is accomplished by means of a robust and accurate semantic mapping module that automatically maps high-level semantics to low-level measurements computed from phenotype imagery. The framework was applied to two different plant species with semantic rules mined and an ontology constructed. Rule quality was evaluated and showed high quality rules for most semantics. This framework also facilitates automatic annotation of phenotype images and can be adopted by different plant communities to aid in their research. CONCLUSIONS: The Computable Visually Observed Phenotype Ontological Framework for plants has been developed for more efficient and accurate management of visually observed phenotypes, which play a significant role in plant genomics research. The uniqueness of this framework is its ability to bridge the knowledge of informaticians and plant science researchers by translating descriptions of visually observed phenotypes into standardized, machine-understandable representations, thus enabling the development of advanced information retrieval and phenotype annotation analysis tools for the plant science community.

Quantitative determination of flavonoids by column high-performance liquid chromatography with mass spectrometry and ultraviolet absorption detection in Artemisia afra and comparative studies with various species of Artemisia plants.

A simple and specific analytical method for the quantitative determination of flavonoids from the aerial parts of the Artemisia afra plant samples was developed. By column high-performance liquid chromatography (HPLC) with UV absorption and mass spectrometry (MS) detection, separation was achieved on a reversed-phase octadecylsilyl (C18) column with water, methanol, and acetonitrile, all containing 0.1% acetic acid, as the mobile phase. These methods were used to analyze various species of Artemisia plant samples. The wavelength used for quantification of flavonoids with the diode array detector was 335 nm. The limits of detection (LOD) by HPLC/MS were found to be 7.5, 7.5, 10, 2.0, and 2.0 ng/mL; and by LC-UV the LODs were 500, 500, 500, 300, and 300 ng/mL for apigenin, chrysoeriol, tamarixetin, acacetin, and genkwanin, respectively. The HPLC/MS method was found to be 50-150 times more sensitive than the HPLC-UV method. HPLC/MS coupled with an electrospray ionization interface is described for the identification and quantification of flavonoids in various plant samples. This method involved the use of the [M+H]+ ions of the compounds at mass-to-charge ratio of 1.0606, 301.0712, 317.0661, 285.0763, and 285.0763 (calculated mass), respectively, in the positive ion mode with extractive ion monitoring.

The Plant Ontology Database: a community resource for plant structure and developmental stages controlled vocabulary and annotations.

The Plant Ontology Consortium (POC, http://www.plantontology.org) is a collaborative effort among model plant genome databases and plant researchers that aims to create, maintain and facilitate the use of a controlled vocabulary (ontology) for plants. The ontology allows users to ascribe attributes of plant structure (anatomy and morphology) and developmental stages to data types, such as genes and phenotypes, to provide a semantic framework to make meaningful cross-species and database comparisons. The POC builds upon groundbreaking work by the Gene Ontology Consortium (GOC) by adopting and extending the GOC's principles, existing software and database structure. Over the past year, POC has added hundreds of ontology terms to associate with thousands of genes and gene products from Arabidopsis, rice and maize, which are available through a newly updated web-based browser (http://www.plantontology.org/amigo/go.cgi) for viewing, searching and querying. The Consortium has also implemented new functionalities to facilitate the application of PO in genomic research and updated the website to keep the contents current. In this report, we present a brief description of resources available from the website, changes to the interfaces, data updates, community activities and future enhancement.

The plant structure ontology, a unified vocabulary of anatomy and morphology of a flowering plant.

Formal description of plant phenotypes and standardized annotation of gene expression and protein localization data require uniform terminology that accurately describes plant anatomy and morphology. This facilitates cross species comparative studies and quantitative comparison of phenotypes and expression patterns. A major drawback is variable terminology that is used to describe plant anatomy and morphology in publications and genomic databases for different species. The same terms are sometimes applied to different plant structures in different taxonomic groups. Conversely, similar structures are named by their species-specific terms. To address this problem, we created the Plant Structure Ontology (PSO), the first generic ontological representation of anatomy and morphology of a flowering plant. The PSO is intended for a broad plant research community, including bench scientists, curators in genomic databases, and bioinformaticians. The initial releases of the PSO integrated existing ontologies for Arabidopsis (Arabidopsis thaliana), maize (Zea mays), and rice (Oryza sativa); more recent versions of the ontology encompass terms relevant to Fabaceae, Solanaceae, additional cereal crops, and poplar (Populus spp.). Databases such as The Arabidopsis Information Resource, Nottingham Arabidopsis Stock Centre, Gramene, MaizeGDB, and SOL Genomics Network are using the PSO to describe expression patterns of genes and phenotypes of mutants and natural variants and are regularly contributing new annotations to the Plant Ontology database. The PSO is also used in specialized public databases, such as BRENDA, GENEVESTIGATOR, NASCArrays, and others. Over 10,000 gene annotations and phenotype descriptions from participating databases can be queried and retrieved using the Plant Ontology browser. The PSO, as well as contributed gene associations, can be obtained at www.plantontology.org.

Whole-plant growth stage ontology for angiosperms and its application in plant biology.

Plant growth stages are identified as distinct morphological landmarks in a continuous developmental process. The terms describing these developmental stages record the morphological appearance of the plant at a specific point in its life cycle. The widely differing morphology of plant species consequently gave rise to heterogeneous vocabularies describing growth and development. Each species or family specific community developed distinct terminologies for describing whole-plant growth stages. This semantic heterogeneity made it impossible to use growth stage description contained within plant biology databases to make meaningful computational comparisons. The Plant Ontology Consortium (http://www.plantontology.org) was founded to develop standard ontologies describing plant anatomical as well as growth and developmental stages that can be used for annotation of gene expression patterns and phenotypes of all flowering plants. In this article, we describe the development of a generic whole-plant growth stage ontology that describes the spatiotemporal stages of plant growth as a set of landmark events that progress from germination to senescence. This ontology represents a synthesis and integration of terms and concepts from a variety of species-specific vocabularies previously used for describing phenotypes and genomic information. It provides a common platform for annotating gene function and gene expression in relation to the developmental trajectory of a plant described at the organismal level. As proof of concept the Plant Ontology Consortium used the plant ontology growth stage ontology to annotate genes and phenotypes in plants with initial emphasis on those represented in The Arabidopsis Information Resource, Gramene database, and MaizeGDB.

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.