Finding our way through phenotypes.

Deans, Andrew R and Lewis, Suzanna E and Huala, Eva and Anzaldo, Salvatore S and Ashburner, Michael and Balhoff, James P and Blackburn, David C and Blake, Judith A and Burleigh, J Gordon and Chanet, Bruno and Cooper, Laurel D and Courtot, Mélanie and Csösz, Sándor and Cui, Hong and Dahdul, Wasila and Das, Sandip and Dececchi, T Alexander and Dettai, Agnes and Diogo, Rui and Druzinsky, Robert E and Dumontier, Michel and Franz, Nico M and Friedrich, Frank and Gkoutos, George V and Haendel, Melissa and Harmon, Luke J and Hayamizu, Terry F and He, Yongqun and Hines, Heather M and Ibrahim, Nizar and Jackson, Laura M and Jaiswal, Pankaj and James-Zorn, Christina and Köhler, Sebastian and Lecointre, Guillaume and Lapp, Hilmar and Lawrence, Carolyn J and Le Novère, Nicolas and Lundberg, John G and Macklin, James and Mast, Austin R and Midford, Peter E and Mikó, István and Mungall, Christopher J and Oellrich, Anika and Osumi-Sutherland, David and Parkinson, Helen and Ramírez, Martín J and Richter, Stefan and Robinson, Peter N and Ruttenberg, Alan and Schulz, Katja S and Segerdell, Erik and Seltmann, Katja C and Sharkey, Michael J and Smith, Aaron D. and Smith, Barry and Specht, Chelsea D and Squires, R Burke and Thacker, Robert W and Thessen, Anne and Fernandez-Triana, Jose and Vihinen, Mauno and Vize, Peter D and Vogt, Lars and Wall, Christine E and Walls, Ramona L and Westerfeld, Monte and Wharton, Robert A and Wirkner, Christian S and Woolley, James B and Yoder, Matthew J and Zorn, Aaron M and Mabee, Paula (2015) Finding our way through phenotypes. PLoS biology, 13 (1). e1002033. ISSN 1544-9173

Preview

Text Deans_A_etal_2015_Finding_our_way_through_phenotypes(1).pdf Available under License Creative Commons Attribution. Download (3MB) | Preview

Abstract

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.

Item Type:	Article
ID number or DOI:	10.1371/journal.pbio.1002033
Keywords:	Phenotypes; ontologies; genomics; plant genomics; evolutionary biology; model organisms;
Subjects:	Q Science > QH Natural history > QH301 Biology
NAU Depositing Author Academic Status:	Faculty/Staff
Department/Unit:	College of Engineering, Forestry, and Natural Science > Biological Sciences
Date Deposited:	16 Oct 2015 20:08
URI:	http://openknowledge.nau.edu/id/eprint/1756

Actions (login required)

IR Staff Record View

Downloads