1. Finding our way through phenotypes
- Author
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Christian S. Wirkner, Monte Westerfeld, Bruno Chanet, Michael J. Sharkey, Rui Diogo, Erik Segerdell, John G. Lundberg, Suzanna E. Lewis, Christopher J. Mungall, Carolyn J. Lawrence, James Macklin, Anne E. Thessen, Katja C. Seltmann, Matthew J. Yoder, Andrew R. Deans, Ramona Walls, Peter E. Midford, Christina James-Zorn, Salvatore S. Anzaldo, Sandip Das, Sandor Csösz, Michael Ashburner, Peter D. Vize, J. Gordon Burleigh, Guillaume Lecointre, Melissa A. Haendel, T. Alexander Dececchi, Hong Cui, Mélanie Courtot, Laura M. Jackson, Hilmar Lapp, Paula M. Mabee, Robert W. Thacker, Pankaj Jaiswal, Jose Fernandez-Triana, Mauno Vihinen, Aaron D. Smith, Heather M. Hines, Alan Ruttenberg, Austin Mast, Wasila M. Dahdul, Agnès Dettai, Barry Smith, Aaron M. Zorn, Chelsea D. Specht, Nizar Ibrahim, Frank Friedrich, Michel Dumontier, Lars Vogt, István Mikó, Peter N. Robinson, Robert A. Wharton, Luke J. Harmon, James P. Balhoff, David Osumi-Sutherland, George Gkoutos, Christine E. Wall, Katja Schulz, David C. Blackburn, James B. Woolley, Stefan Richter, R. Burke Squires, Yongqun He, Helen Parkinson, Laurel Cooper, Nico M. Franz, Judith A. Blake, Eva Huala, Robert E. Druzinsky, Martín J. Ramírez, Anika Oellrich, Terry F. Hayamizu, Nicolas Le Novère, Sebastian Köhler, Department of Genetics University of Cambridge, University of Cambridge [UK] (CAM), University of Florida [Gainesville] (UF), Institut de Systématique, Evolution, Biodiversité (ISYEB ), Muséum national d'Histoire naturelle (MNHN)-Université Pierre et Marie Curie - Paris 6 (UPMC)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Department of Botany and Plant Pathology, Oregon State University (OSU), Terry Fox Laboratory, BC Cancer Agency (BCCRC)-British Columbia Cancer Agency Research Centre, Eötvös Loránd University (ELTE), GeneDx [Gaithersburg, MD, USA], Département Systématique et Évolution, and Muséum national d'Histoire naturelle (MNHN)
- Subjects
Computer and Information Sciences ,Databases, Factual ,QH301-705.5 ,Ecology (disciplines) ,Systems biology ,Genomics ,Computational biology ,Biology ,[SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy ,General Biochemistry, Genetics and Molecular Biology ,Bottleneck ,Computer Applications ,Phenomics ,Terminology as Topic ,Controlled vocabulary ,Animals ,Humans ,Biology (General) ,Data Curation ,Genetic Association Studies ,Data Management ,Evolutionary Biology ,Computing Systems ,General Immunology and Microbiology ,Data curation ,Library Science ,General Neuroscience ,Computational Biology ,Reproducibility of Results ,Biology and Life Sciences ,Biological Sciences ,Reference Standards ,Data science ,Data resources ,ComputingMethodologies_PATTERNRECOGNITION ,Phenotype ,Perspective ,Gene-Environment Interaction ,General Agricultural and Biological Sciences ,Information Technology ,Developmental Biology ,Computer Modeling - Abstract
Imagine if we could compute across phenotype data as easily as genomic data; this article calls for efforts to realize this vision and discusses the potential benefits., 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.
- Published
- 2015
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