Browsing by Author "Mabee, Paula"

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Open Access
Finding our way through phenotypes.
(PLoS Biol, 2015-01) Deans, Andrew R; Lewis, Suzanna E; Huala, Eva; Anzaldo, Salvatore S; Ashburner, Michael; Balhoff, James P; Blackburn, David C; Blake, Judith A; Burleigh, J Gordon; Chanet, Bruno; Cooper, Laurel D; Courtot, Mélanie; Csösz, Sándor; Cui, Hong; Dahdul, Wasila; Das, Sandip; Dececchi, T Alexander; Dettai, Agnes; Diogo, Rui; Druzinsky, Robert E; Dumontier, Michel; Franz, Nico M; Friedrich, Frank; Gkoutos, George V; Haendel, Melissa; Harmon, Luke J; Hayamizu, Terry F; He, Yongqun; Hines, Heather M; Ibrahim, Nizar; Jackson, Laura M; Jaiswal, Pankaj; James-Zorn, Christina; Köhler, Sebastian; Lecointre, Guillaume; Lapp, Hilmar; Lawrence, Carolyn J; Le Novère, Nicolas; Lundberg, John G; Macklin, James; Mast, Austin R; Midford, Peter E; Mikó, István; Mungall, Christopher J; Oellrich, Anika; Osumi-Sutherland, David; Parkinson, Helen; Ramírez, Martín J; Richter, Stefan; Robinson, Peter N; Ruttenberg, Alan; Schulz, Katja S; Segerdell, Erik; Seltmann, Katja C; Sharkey, Michael J; Smith, Aaron D; Smith, Barry; Specht, Chelsea D; Squires, R Burke; Thacker, Robert W; Thessen, Anne; Fernandez-Triana, Jose; Vihinen, Mauno; Vize, Peter D; Vogt, Lars; Wall, Christine E; Walls, Ramona L; Westerfeld, Monte; Wharton, Robert A; Wirkner, Christian S; Woolley, James B; Yoder, Matthew J; Zorn, Aaron M; Mabee, Paula
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.
Open Access
Moving the mountain: analysis of the effort required to transform comparative anatomy into computable anatomy.
(Database : the journal of biological databases and curation, 2015-01) Dahdul, Wasila; Dececchi, T Alexander; Ibrahim, Nizar; Lapp, Hilmar; Mabee, Paula
The diverse phenotypes of living organisms have been described for centuries, and though they may be digitized, they are not readily available in a computable form. Using over 100 morphological studies, the Phenoscape project has demonstrated that by annotating characters with community ontology terms, links between novel species anatomy and the genes that may underlie them can be made. But given the enormity of the legacy literature, how can this largely unexploited wealth of descriptive data be rendered amenable to large-scale computation? To identify the bottlenecks, we quantified the time involved in the major aspects of phenotype curation as we annotated characters from the vertebrate phylogenetic systematics literature. This involves attaching fully computable logical expressions consisting of ontology terms to the descriptions in character-by-taxon matrices. The workflow consists of: (i) data preparation, (ii) phenotype annotation, (iii) ontology development and (iv) curation team discussions and software development feedback. Our results showed that the completion of this work required two person-years by a team of two post-docs, a lead data curator, and students. Manual data preparation required close to 13% of the effort. This part in particular could be reduced substantially with better community data practices, such as depositing fully populated matrices in public repositories. Phenotype annotation required ∼40% of the effort. We are working to make this more efficient with Natural Language Processing tools. Ontology development (40%), however, remains a highly manual task requiring domain (anatomical) expertise and use of specialized software. The large overhead required for data preparation and ontology development contributed to a low annotation rate of approximately two characters per hour, compared with 14 characters per hour when activity was restricted to character annotation. Unlocking the potential of the vast stores of morphological descriptions requires better tools for efficiently processing natural language, and better community practices towards a born-digital morphology. Database URL: http://kb.phenoscape.org
Open Access
Phenex: ontological annotation of phenotypic diversity.
(PLoS One, 2010-05-05) Balhoff, James P; Dahdul, Wasila M; Kothari, Cartik R; Lapp, Hilmar; Lundberg, John G; Mabee, Paula; Midford, Peter E; Westerfield, Monte; Vision, Todd J
BACKGROUND: Phenotypic differences among species have long been systematically itemized and described by biologists in the process of investigating phylogenetic relationships and trait evolution. Traditionally, these descriptions have been expressed in natural language within the context of individual journal publications or monographs. As such, this rich store of phenotype data has been largely unavailable for statistical and computational comparisons across studies or integration with other biological knowledge. METHODOLOGY/PRINCIPAL FINDINGS: Here we describe Phenex, a platform-independent desktop application designed to facilitate efficient and consistent annotation of phenotypic similarities and differences using Entity-Quality syntax, drawing on terms from community ontologies for anatomical entities, phenotypic qualities, and taxonomic names. Phenex can be configured to load only those ontologies pertinent to a taxonomic group of interest. The graphical user interface was optimized for evolutionary biologists accustomed to working with lists of taxa, characters, character states, and character-by-taxon matrices. CONCLUSIONS/SIGNIFICANCE: Annotation of phenotypic data using ontologies and globally unique taxonomic identifiers will allow biologists to integrate phenotypic data from different organisms and studies, leveraging decades of work in systematics and comparative morphology.
Open Access
Presence-absence reasoning for evolutionary phenotypes
(Phenotype Day at International Conference on Intelligent Systems for Molecular Biology (ISBM 2014), 2014-07) Balhoff, James P; Dececchi, Thomas Alexander; Mabee, Paula; Lapp, Hilmar