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Journal Articles PLoS Biology Year : 2015

Finding Our Way through Phenotypes

Andrew R Deans
  • Function : Author
Suzanna E Lewis
  • Function : Author
Eva Huala
  • Function : Author
Salvatore S Anzaldo
  • Function : Author
James P Balhoff
  • Function : Author
Judith A Blake
  • Function : Author
J Gordon Burleigh
  • Function : Author
Wasila Dahdul
  • Function : Author
Sandip Das
  • Function : Author
T Alexander Dececchi
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Rui Diogo
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Robert E Druzinsky
  • Function : Author
Michel Dumontier
Nico M Franz
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Frank Friedrich
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George V Gkoutos
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Melissa Haendel
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Luke J Harmon
  • Function : Author
Terry F Hayamizu
  • Function : Author
Yongqun He
  • Function : Author
Heather M Hines
  • Function : Author
Nizar Ibrahim
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Laura M Jackson
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Pankaj Jaiswal
  • Function : Author
Christina James-Zorn
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Sebastian Köhler
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Hilmar Lapp
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Carolyn J Lawrence
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Nicolas Le Novère
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John G Lundberg
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James Macklin
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Austin R Mast
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Peter E Midford
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István Mikó
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Christopher J Mungall
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Anika Oellrich
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David Osumi-Sutherland
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Helen Parkinson
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Martín J Ramírez
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Stefan Richter
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Peter N Robinson
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Alan Ruttenberg
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Katja S Schulz
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Erik Segerdell
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Katja C Seltmann
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Michael J Sharkey
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Aaron D Smith
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Barry Smith
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Chelsea D Specht
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R Burke Squires
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Robert W Thacker
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Anne Thessen
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Jose Fernandez-Triana
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Mauno Vihinen
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Peter D Vize
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Lars Vogt
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Christine E Wall
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Ramona L Walls
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Monte Westerfeld
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Robert A Wharton
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Christian S Wirkner
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James B Woolley
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Matthew J Yoder
  • Function : Author
Aaron M Zorn
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Paula M Mabee
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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, consensusbased, 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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Dates and versions

hal-04042415 , version 1 (23-03-2023)

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Andrew R Deans, Suzanna E Lewis, Eva Huala, Salvatore S Anzaldo, Michael Ashburner, et al.. Finding Our Way through Phenotypes. PLoS Biology, 2015, 13 (1), pp.e1002033. ⟨10.1371/journal.pbio.1002033⟩. ⟨hal-04042415⟩
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