Finding our way through phenotypes.

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 co...

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Main Authors: Andrew R Deans, Suzanna E Lewis, Eva Huala, Salvatore S Anzaldo, Michael Ashburner, James P Balhoff, David C Blackburn, Judith A Blake, J Gordon Burleigh, Bruno Chanet, Laurel D Cooper, Mélanie Courtot, Sándor Csösz, Hong Cui, Wasila Dahdul, Sandip Das, T Alexander Dececchi, Agnes Dettai, Rui Diogo, Robert E Druzinsky, Michel Dumontier, Nico M Franz, Frank Friedrich, George V Gkoutos, Melissa Haendel, Luke J Harmon, Terry F Hayamizu, Yongqun He, Heather M Hines, Nizar Ibrahim, Laura M Jackson, Pankaj Jaiswal, Christina James-Zorn, Sebastian Köhler, Guillaume Lecointre, Hilmar Lapp, Carolyn J Lawrence, Nicolas Le Novère, John G Lundberg, James Macklin, Austin R Mast, Peter E Midford, István Mikó, Christopher J Mungall, Anika Oellrich, David Osumi-Sutherland, Helen Parkinson, Martín J Ramírez, Stefan Richter, Peter N Robinson, Alan Ruttenberg, Katja S Schulz, Erik Segerdell, Katja C Seltmann, Michael J Sharkey, Aaron D Smith, Barry Smith, Chelsea D Specht, R Burke Squires, Robert W Thacker, Anne Thessen, Jose Fernandez-Triana, Mauno Vihinen, Peter D Vize, Lars Vogt, Christine E Wall, Ramona L Walls, Monte Westerfeld, Robert A Wharton, Christian S Wirkner, James B Woolley, Matthew J Yoder, Aaron M Zorn, Paula Mabee
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2015-01-01
Series:PLoS Biology
Online Access:https://journals.plos.org/plosbiology/article/file?id=10.1371/journal.pbio.1002033&type=printable
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Summary: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.
ISSN:1544-9173
1545-7885

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