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Title: Emerging semantics to link phenotype and environment

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. Lastly, 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 formore » the development of a data model linking phenotypes and environments.« less
 [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)
Publication Date:
OSTI Identifier:
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
Accepted Manuscript
Journal Name:
Additional Journal Information:
Journal Volume: 3; Journal ID: ISSN 2167-8359
PeerJ Inc.
Research Org:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Org:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
Country of Publication:
United States
59 BASIC BIOLOGICAL SCIENCES phenotype; environment; ontology; semantic web; biodiversity; data integration