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Finding Our Way through Phenotypes.

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Author

  • 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
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Summary, in English

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.

Department/s

  • BioCARE: Biomarkers in Cancer Medicine improving Health Care, Education and Innovation
  • Protein Bioinformatics

Publishing year

2015

Language

English

Publication/Series

PLoS Biology

Volume

13

Issue

1

Document type

Journal article

Publisher

Public Library of Science (PLoS)

Topic

  • Biological Sciences

Status

Published

Research group

  • Protein Bioinformatics

ISBN/ISSN/Other

  • ISSN: 1545-7885