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Title: Emerging semantics to link phenotype and environment

Journal Article · · PeerJ
DOI:https://doi.org/10.7717/peerj.1470· OSTI ID:1257630
 [1];  [2];  [3];  [4];  [5];  [6];  [7];  [4];  [8];  [9];  [10];  [11];  [12];  [13];  [14];  [15];  [16];  [7];  [17];  [18] more »;  [10];  [5] « less
  1. Ronin Institute for Independent Scholarship, Monclair, NJ (United States); The Data Detektiv, Waltham, MA (United States)
  2. New Jersey Institute of Technology, Newark, NJ (United States)
  3. Alfred-Wegener-Institut, Helmholtz-Zentrum fur Polar-und Meeresforschung, Bremerhaven (Germany)
  4. Oregon State Univ., Corvallis, OR (United States)
  5. Univ. of South Dakota, Vermillion, SD (United States)
  6. Yale Univ., New Haven, CT (United States)
  7. Arizona State Univ., Tempe, AZ (United States)
  8. Iowa State Univ., Ames, IA (United States)
  9. Richmond, VA (United States)
  10. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  11. Museo Argentino de Ciencias Naturales-CONICET, Buenos Aires (Argentina)
  12. Univ. of California, Berkeley, CA (United States)
  13. Institut fur Evolutionsbiologie und Okologie, Univ. Bonn, Bonn (Germany)
  14. Naturalis Biodiversity Center, Leiden (The Netherlands)
  15. Univ. of Arizona, Tucson, AZ (United States)
  16. United States Department of Agriculture-ARS, Maricopa, AZ (United States)
  17. Pennsylvania State Univ., University Park, PA (United States)
  18. Phoenix Bioinformatics, Redwood City, CA (United States)

Understanding the interplay between environmental conditions and phenotypes is a fundamental goal of biology. Unfortunately, data that include observations on phenotype and environment are highly heterogeneous and thus difficult to find and integrate. One approach that is likely to improve the status quo involves the use of ontologies to standardize and link data about phenotypes and environments. Specifying and linking data through ontologies will allow researchers to increase the scope and flexibility of large-scale analyses aided by modern computing methods. Investments in this area would advance diverse fields such as ecology, phylogenetics, and conservation biology. While several biological ontologies are well-developed, using them to link phenotypes and environments is rare because of gaps in ontological coverage and limits to interoperability among ontologies and disciplines. In this manuscript, we present (1) use cases from diverse disciplines to illustrate questions that could be answered more efficiently using a robust linkage between phenotypes and environments, (2) two proof-of-concept analyses that show the value of linking phenotypes to environments in fishes and amphibians, and (3) two proposed example data models for linking phenotypes and environments using the extensible observation ontology (OBOE) and the Biological Collections Ontology (BCO); these provide a starting point for the development of a data model linking phenotypes and environments.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
Grant/Contract Number:
AC02-05CH11231; DEB-0956049; IOS:0822201; IOS:1127112; IOS:1340112; DEB 1208666; 287589; DO 1880/1-1; DBI-0735191; DBI-1265383; R24OD011883; DBI-1062404; DBI-1062542
OSTI ID:
1257630
Alternate ID(s):
OSTI ID: 1407295
Journal Information:
PeerJ, Vol. 3; ISSN 2167-8359
Publisher:
PeerJ Inc.Copyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 14 works
Citation information provided by
Web of Science

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Cited By (6)

A logic approach to modelling nomenclatural change journal May 2017
The environment ontology in 2016: bridging domains with increased scope, semantic density, and interoperation journal September 2016
Organizing phenotypic data—a semantic data model for anatomy journal June 2019
Organizing Phenotypic Data—A Semantic Data Model for Anatomy text January 2019
A Logic Approach to Modeling Nomenclatural Change journal June 2016
Methane Seep in Shallow-Water Permeable Sediment Harbors High Diversity of Anaerobic Methanotrophic Communities, Elba, Italy journal March 2016

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