Andrew R. Deans, Pennsylvania State University
Suzanna E. Lewis, Lawrence Berkeley National Lab
Eva Huala, Carnegie Institution for Science
Salvatore S. Anzaldo, Arizona State University
Michael Ashburner, University of Cambridge, UK
James P. Balhoff, National Evolutionary Synthesis Center
David C. Blackburn, California Academy of Sciences
Judith A. Blake, The Jackson Laboratory
J. Gordon Burleigh, University of Florida
Bruno Chanet, Muséum national d'Histoire naturelle, France
Laurel D. Cooper, Oregon State University
Mélanie Courtot, Simon Fraser University, Canada
Sándor Csösz, MTA-ELTE-MTM, Hungary
Hong Cui, University of Arizona
Wasila Dahdul, University of South Dakota
Sandip Das, University of Delhi, India
T. Alexander Dececchi, University of South Dakota
Agnes Dettai, Muséum national d'Histoire naturelle, France
Rui Diogo, Howard University
Robert E. Druzinsky, University of Illinois - Chicago
Michel Dumontier, Stanford Center for Biomedical Informatics Research
Nico M. Franz, Arizona State University
Frank Friedrich, Hamburg University, Germany
George V. Gkoutos, Aberystwyth University, UK
Melissa Haendel, Oregon Health & Science University
Luke J. Harmon, University of Idaho
Terry F Hayamizu, The Jackson Laboratory
Yongqun He, University of Michigan
Heather M. Hines, Pennsylvania State University
Nizar Ibrahim, University of Illinois - Chicago
Laura M. Jackson, University of South Dakota
Pankaj Jaiswal, Oregon State University
Christina James-Zorn, Cincinnati Children's Hospital
Sebastian Köhler, Charité-Universitätsmedizin Berlin, Germany
Guillaume Lecointre, Muséum national d'Histoire naturelle, France
Hilmar Lapp, National Evolutionary Synthesis Center
Carolyn J. Lawrence, Iowa State University
Nicolas Le Novère, Babraham Institute, UK
John G. Lundberg, The Academy of Natural Sciences
James Macklin, Eastern Cereal and Oilseed Research Centre, Canada
Austin R. Mast, Florida State University
Peter E. Midford
István Mikó, Pennsylvania State University
Christopher J. Mungall, Lawrence Berkeley National Lab
Anika Oellrich, European Molecular Biology Laboratory - European Bioinformatics Institute, UK
David Osumi-Sutherland, European Molecular Biology Laboratory - European Bioinformatics Institute, UK
Helen Parkinson, European Molecular Biology Laboratory - European Bioinformatics Institute, UK
Martín J. Ramírez, Museo Argentino de Ciencias Naturales - CONICET, Argentina
Stefan Richter, Universität Rostock, Germany
Peter N. Robinson, Charité – Universitätsmedizin Berlin, Germany
Alan Ruttenberg, University at Buffalo
Katja S. Schulz, Smithsonian Institution
Erik Segerdell, Oregon Health & Science University
Katja C. Seltmann, American Museum of Natural History
Michael Sharkey, University of KentuckyFollow
Aaron D. Smith, Northern Arizona University
Barry Smith, University at Buffalo
Chelsea D. Specht, University of California - Berkeley
R. Burke Squires, National Institutes of Health
Robert W. Thacker, University of Alabama - Birmingham
Anne Thessen, The Data Detektiv
Jose Fernandez-Triana, Canadian National Collection of Insects, Canada
Mauno Vihinen, Lund University, Sweden
Peter D. Vize, University of Calgary, Canada
Lars Vogt, Institut für Evolutionsbiologie und Ökologie, Germany
Christine E. Wall, Duke University
Ramona L. Walls, University of Arizona
Monte Westerfeld, University of Oregon
Robert A. Wharton, Texas A & M University
Christian S. Wirkner, Universität Rostock, Germany
James B. Woolley, Texas A & M University
Matthew J. Yoder, University of Illinois - Urbana-Champaign
Aaron M. Zorn, Cincinnati Children's Hospital
Paula Mabee, University of South Dakota


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.

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Notes/Citation Information

Published in PLOS Biology, v. 13, no. 1, article e1002033, p. 1-9.

© 2015 Deans et al.

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Funding Information

This effort was funded by the US National Science Foundation, grant number DEB-0956049. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.