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]

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

Finding Our Way through Phenotypes

Despite a large and multifaceted effort to understand the vast landscape of phenotypic data, their current form inhibits productive data analysis. The lack of a community-wide, consensus-based, human- and machine-interpretable language for describing phenotypes and their genomic and environmental contexts is perhaps the most pressing scientific bottleneck to integration across many key fields in biology, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. Here we survey the current phenomics landscape, including data resources and handling, and the progress that has been made to accurately capture relevant data descriptions for phenotypes. We present an example of the kind of integration across domains that computable phenotypes would enable, and we call upon the broader biology community, publishers, and relevant funding agencies to support efforts to surmount today's data barriers and facilitate analytical reproducibility

Finding Our Way through Phenotypes

Despite a large and multifaceted effort to understand the vast landscape of phenotypic data, their current form inhibits productive data analysis. The lack of a community-wide, consensus-based, human- and machine-interpretable language for describing phenotypes and their genomic and environmental contexts is perhaps the most pressing scientific bottleneck to integration across many key fields in biology, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. Here we survey the current phenomics landscape, including data resources and handling, and the progress that has been made to accurately capture relevant data descriptions for phenotypes. We present an example of the kind of integration across domains that computable phenotypes would enable, and we call upon the broader biology community, publishers, and relevant funding agencies to support efforts to surmount today's data barriers and facilitate analytical reproducibility

Análisis de puntos de rocío y contenidos de humedad de mezclas gaseosas N2-H2O y CH4-H2O

EI conocimiento actual del contenido de humedad de mezclas gaseosas saturadas es aún incompleto, especialmente en la región de altas presiones y bajas temperaturas. Por tanto se realizaron medidas experimentales de punta de rocío con los sistemas nitrógeno-agua y metano-agua, a presiones de 3 y 6 MPa y a temperaturas de 258 K a 288 K. Los puntos de rocío se determinaron con el método del espejo de punta de rocío y los contenidos de humedad por medio de la titulación de Karl-Fischer. Los valores experimentales se compararon con correlaciones de la literatura. El método de Sharma-Campbell resultó ser la mejor descripción del sistema nitrógeno-agua. Para temperaturas inferiores a 273 K la suposición de comportamiento de gas ideal mostro ser suficiente para el sistema metano-agua, mientras que para temperaturas superiores a 273 K los cálculos mediante el principio de estados correspondientes con dos parámetros, en combinación con una corrección de fugacidad, manifestó ser la mejor.The actual knowledge of the exact water content in saturated gas mixtures still is incomplete, especially in the high pressure and low temperature region. Hence, dew point measurements with nitrogen water and methan-water mixtures were performed, at pressures of 3 and 6MPa and temperatures from 258 K to 288 K. The dew points were determined with the dew point mirror method and the water content by means of the Karl-Fischer-titration. The experimental values were compared to correlations from the literature. The approach by Sharma-Campbell resulted in the best description of the system nitrogen water. For temperatures below 273 K the assumption of ideal behavior proved to be sufficient for the system methan-water, whereas for temperatures above 273 K calculations with the two-parameter corresponding states principle in combination with a fugacity correction turned out to be the best

Análisis de puntos de rocío y contenidos de humedad de mezclas gaseosas n2-h2o y ch4-h2o

EI conocimiento actual del contenido de humedad de mezclas gaseosas saturadas es aún incompleto, especialmente en la región de altas presiones y bajas temperaturas. Por tanto se realizaron medidas experimentales de punta de rocío con los sistemas nitrógeno-agua y metano-agua, a presiones de 3 y 6 MPa y a temperaturas de 258 K a 288 K. Los puntos de rocío se determinaron con el método del espejo de punta de rocío y los contenidos de humedad por medio de la titulación de Karl-Fischer. Los valores experimentales se compararon con correlaciones de la literatura. El método de Sharma-Campbell resultó ser la mejor descripción del sistema nitrógeno-agua. Para temperaturas inferiores a 273 K la suposición de comportamiento de gas ideal mostro ser suficiente para el sistema metano-agua, mientras que para temperaturas superiores a 273 K los cálculos mediante el principio de estados correspondientes con dos parámetros, en combinación con una corrección de fugacidad, manifestó ser la mejor.The actual knowledge of the exact water content in saturated gas mixtures still is incomplete, especially in the high pressure and low temperature region. Hence, dew point measurements with nitrogen water and methan-water mixtures were performed, at pressures of 3 and 6MPa and temperatures from 258 K to 288 K. The dew points were determined with the dew point mirror method and the water content by means of the Karl-Fischer-titration. The experimental values were compared to correlations from the literature. The approach by Sharma-Campbell resulted in the best description of the system nitrogen water. For temperatures below 273 K the assumption of ideal behavior proved to be sufficient for the system methan-water, whereas for temperatures above 273 K calculations with the two-parameter corresponding states principle in combination with a fugacity correction turned out to be the best