Finding our way through phenotypes.

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Deans, AR; Lewis, SE; Huala, E; Anzaldo, SS; Ashburner, M; Balhoff, JP; Blackburn, DC; Blake, JA; Burleigh, JG; Chanet, B; Cooper, LD; Courtot, M; Csösz, S; Cui, H; Dahdul, W; Das, S; Dececchi, TA; Dettai, A; Diogo, R; Druzinsky, RE; Dumontier, M; Franz, NM; Friedrich, F; Gkoutos, GV; Haendel, M; Harmon, LJ; Hayamizu, TF; He, Y; Hines, HM; Ibrahim, N; Jackson, LM; Jaiswal, P; James-Zorn, C; Köhler, S; Lecointre, G; Lapp, H; Lawrence, CJ; Le Novère, N; Lundberg, JG; Macklin, J; Mast, AR et al.

Published Date

  • January 6, 2015

Published In

Volume / Issue

  • 13 / 1

Start / End Page

  • e1002033 -

PubMed ID

  • 25562316

Electronic International Standard Serial Number (EISSN)

  • 1545-7885

International Standard Serial Number (ISSN)

  • 1544-9173

Digital Object Identifier (DOI)

  • 10.1371/journal.pbio.1002033

Language

  • eng