Show simple item record

dc.contributor.authorThessen, Anne
dc.contributor.authorWalls, Ramona
dc.contributor.authorVogt, Lars
dc.contributor.authorSinger, Jessica
dc.contributor.authorWarren, Robert
dc.contributor.authorButtigieg, Pier Luigi
dc.contributor.authorBalhof, James
dc.contributor.authorMungall, Christopher
dc.contributor.authorMcGuinness, Deborah L.
dc.contributor.authorStucky, Brian
dc.contributor.authorYoder, Matthew
dc.contributor.authorHaendel, Melissa
dc.description.abstractThe 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.
dc.publisherPLOS Computational Biology
dc.titleTransforming the study of organisms: Phenomic data models and knowledge bases

Files in this item


There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record