An ontology approach to comparative phenomics in plants

BACKGROUND: Plant phenotype datasets include many different types of data, formats, and terms from specialized vocabularies. Because these datasets were designed for different audiences, they frequently contain language and details tailored to investigators with different research objectives and backgrounds. Although phenotype comparisons across datasets have long been possible on a small scale, comprehensive queries and analyses that span a broad set of reference species, research disciplines, and knowledge domains continue to be severely limited by the absence of a common semantic framework. RESULTS: We developed a workflow to curate and standardize existing phenotype datasets for six plant species, encompassing both model species and crop plants with established genetic resources. Our effort focused on mutant phenotypes associated with genes of known sequence in Arabidopsis thaliana (L.) Heynh. (Arabidopsis), Zea mays L. subsp. mays (maize), Medicago truncatula Gaertn. (barrel medic or Medicago), Oryza sativa L. (rice), Glycine max (L.) Merr. (soybean), and Solanum lycopersicum L. (tomato). We applied the same ontologies, annotation standards, formats, and best practices across all six species, thereby ensuring that the shared dataset could be used for cross-species querying and semantic similarity analyses. Curated phenotypes were first converted into a common format using taxonomically broad ontologies such as the Plant Ontology, Gene Ontology, and Phenotype and Trait Ontology. We then compared ontology-based phenotypic descriptions with an existing classification system for plant phenotypes and evaluated our semantic similarity dataset for its ability to enhance predictions of gene families, protein functions, and shared metabolic pathways that underlie informative plant phenotypes. CONCLUSIONS: The use of ontologies, annotation standards, shared formats, and best practices for cross-taxon phenotype data analyses represents a novel approach to plant phenomics that enhances the utility of model genetic organisms and can be readily applied to species with fewer genetic resources and less well-characterized genomes. In addition, these tools should enhance future efforts to explore the relationships among phenotypic similarity, gene function, and sequence similarity in plants, and to make genotype-to-phenotype predictions relevant to plant biology, crop improvement, and potentially even human health.This item is part of the UA Faculty Publications collection. For more information this item or other items in the UA Campus Repository, contact the University of Arizona Libraries at [email protected]

Interactive Actor Analysis for Rural Water Management in The Netherlands

Recent developments in the policy sciences emphasize the social environment in which decisions are made. The ‘network metaphor’ is often used to describe the key role of interactions between interdependent actors involved in decision making. These interactions take place in a policy arena drawn up by actors with an interest in and control over decisions on the issues addressed. Interdependencies, caused by the need for actors to increase their means of realizing objectives, are the driving force behind these interactions. Dependency relations are of special interest to water management and river basin management because of the fundamental asymmetrical interdependencies that exist in river basins between upstream and downstream stakeholders. Coleman’s linear system of action models decision making process involving dependencies between multiple stakeholders as exchange of control over issues, while interactions are required to negotiate exchanges of control. We developed an interactive method for actor analysis based on Coleman’s linear system of action and applied it to the national rural water management policy domain in The Netherlands. The method is firmly rooted in mathematical sociology and defies the criticism that methods for actor and stakeholder analysis do not specify a theoretical basis explaining the causal relations between the variables analyzed and policy change. With the application to the rural water management policy arena we intended to increase our insight into the practical applicability of this analyticmethod in an interactive workshop, the acceptability of the approach for the participating actors, its contribution to the process of decision making and our understanding of the rural water management policy arena in The Netherlands. We found that the Association of Water Authorities, the Ministry of Public Works and the Ministry of Agriculture are the most powerful actor in the policy domain, while governance and cost and benefits of rural water management are the most salient issues. Progress in policy development for rural water management is probably most promising for the issues governance, costs and benefits, safety and rural living conditions through improved interaction between the Association of Water Authorities, the Ministry of Agriculture and the Rural Credit Bank. Besides these analytic results the interactive approach implemented increased the participants understanding of their dependency on other actors in the rural water management policy domain and supported them in developing a sound perspective on their dependency position. We concluded that the method developed is acceptable to real-world policy decision makers, can successfully be applied in an interactive setting, potentially contributes to the process of decision making by increasing the participants understanding of their dependency position, has the potential to delivers valuable advice for future decision-making and increases our understanding of policy development for rural water management in general

An ontology approach to comparative phenomics in plants

BACKGROUND: Plant phenotype datasets include many different types of data, formats, and terms from specialized vocabularies. Because these datasets were designed for different audiences, they frequently contain language and details tailored to investigators with different research objectives and backgrounds. Although phenotype comparisons across datasets have long been possible on a small scale, comprehensive queries and analyses that span a broad set of reference species, research disciplines, and knowledge domains continue to be severely limited by the absence of a common semantic framework. RESULTS: We developed a workflow to curate and standardize existing phenotype datasets for six plant species, encompassing both model species and crop plants with established genetic resources. Our effort focused on mutant phenotypes associated with genes of known sequence in Arabidopsis thaliana (L.) Heynh. (Arabidopsis), Zea mays L. subsp. mays (maize), Medicago truncatula Gaertn. (barrel medic or Medicago), Oryza sativa L. (rice), Glycine max (L.) Merr. (soybean), and Solanum lycopersicum L. (tomato). We applied the same ontologies, annotation standards, formats, and best practices across all six species, thereby ensuring that the shared dataset could be used for cross-species querying and semantic similarity analyses. Curated phenotypes were first converted into a common format using taxonomically broad ontologies such as the Plant Ontology, Gene Ontology, and Phenotype and Trait Ontology. We then compared ontology-based phenotypic descriptions with an existing classification system for plant phenotypes and evaluated our semantic similarity dataset for its ability to enhance predictions of gene families, protein functions, and shared metabolic pathways that underlie informative plant phenotypes. CONCLUSIONS: The use of ontologies, annotation standards, shared formats, and best practices for cross-taxon phenotype data analyses represents a novel approach to plant phenomics that enhances the utility of model genetic organisms and can be readily applied to species with fewer genetic resources and less well-characterized genomes. In addition, these tools should enhance future efforts to explore the relationships among phenotypic similarity, gene function, and sequence similarity in plants, and to make genotype-to-phenotype predictions relevant to plant biology, crop improvement, and potentially even human health

Party Size and Constituency Representation: Evidence from the 19th-Century U.S. House of Representatives

Research on congressional parties assumes, but has not directly shown, that party size affects individual members\u27 calculations. Drawing on a key case from the nineteenth-century House the secession-driven Republican hegemony of 1861 this article explores the hypothesis that party voting not only declines but also becomes more strongly linked to constituency factors as relative party size increases. The analysis reveals that the jump in party size coincides with (1) a decrease in party voting among individual continuing members, (2) a strengthening association between some constituency factors and party voting, and (3) patterns of decline in individual party voting that are explained in part by constituency measures

Presentation of Partisanship: Constituency Connections and Partisan Congressional Activity

This article explores how House members relate their involvement in partisan Washington activity to constituency representation. Copyright (c) 2009 by the Southwestern Social Science Association.