TY - JOUR
T1 - Transforming the study of organisms
T2 - Phenomic data models and knowledge bases
AU - Thessen, Anne E.
AU - Walls, Ramona L.
AU - Vogt, Lars
AU - Singer, Jessica
AU - Warren, Robert
AU - Buttigieg, Pier Luigi
AU - Balhoff, James P.
AU - Mungall, Christopher J.
AU - McGuinness, Deborah L.
AU - Stucky, Brian J.
AU - Yoder, Matthew J.
AU - Haendel, Melissa A.
N1 - Publisher Copyright:
© 2020 Thessen 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.
PY - 2020/11/24
Y1 - 2020/11/24
N2 - The rapidly decreasing cost of gene sequencing has resulted in a deluge of genomic data from across the tree of life; however, outside a few model organism databases, genomic data are limited in their scientific impact because they are not accompanied by computable phenomic data. The majority of phenomic data are contained in countless small, heterogeneous phenotypic data sets that are very difficult or impossible to integrate at scale because of variable formats, lack of digitization, and linguistic problems. One powerful solution is to represent phenotypic data using data models with precise, computable semantics, but adoption of semantic standards for representing phenotypic data has been slow, especially in biodiversity and ecology. Some phenotypic and trait data are available in a semantic language from knowledge bases, but these are often not interoperable. In this review, we will compare and contrast existing ontology and data models, focusing on nonhuman phenotypes and traits. We discuss barriers to integration of phenotypic data and make recommendations for developing an operationally useful, semantically interoperable phenotypic data ecosystem.
AB - The rapidly decreasing cost of gene sequencing has resulted in a deluge of genomic data from across the tree of life; however, outside a few model organism databases, genomic data are limited in their scientific impact because they are not accompanied by computable phenomic data. The majority of phenomic data are contained in countless small, heterogeneous phenotypic data sets that are very difficult or impossible to integrate at scale because of variable formats, lack of digitization, and linguistic problems. One powerful solution is to represent phenotypic data using data models with precise, computable semantics, but adoption of semantic standards for representing phenotypic data has been slow, especially in biodiversity and ecology. Some phenotypic and trait data are available in a semantic language from knowledge bases, but these are often not interoperable. In this review, we will compare and contrast existing ontology and data models, focusing on nonhuman phenotypes and traits. We discuss barriers to integration of phenotypic data and make recommendations for developing an operationally useful, semantically interoperable phenotypic data ecosystem.
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U2 - 10.1371/journal.pcbi.1008376
DO - 10.1371/journal.pcbi.1008376
M3 - Review article
C2 - 33232313
AN - SCOPUS:85096818055
SN - 1553-734X
VL - 16
JO - PLoS computational biology
JF - PLoS computational biology
IS - 11
M1 - e1008376
ER -