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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, 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.

Document Type

Article

Publication Date

1-6-2015

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.

Digital Object Identifier (DOI)

http://dx.doi.org/10.1371/journal.pbio.1002033

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.

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