Transforming the study of organisms: Phenomic data models and knowledge bases

Area of Science:

• Genomics
• Phenomics
• Bioinformatics

Background:

• Decreasing gene sequencing costs yield vast genomic data, but its scientific impact is limited by absent computable phenomic data.
• Phenotypic data is fragmented across numerous heterogeneous datasets, hindering large-scale integration due to format variability, digitization issues, and linguistic challenges.

Purpose of the Study:

• To compare and contrast existing data models and ontologies for nonhuman phenotypes and traits.
• To identify barriers to phenotypic data integration.
• To propose recommendations for a semantically interoperable phenotypic data ecosystem.

Main Methods:

• Literature review and comparative analysis of existing phenotypic data models and ontologies.
• Focus on nonhuman phenotypes and traits relevant to biodiversity and ecology.

Main Results:

• Existing semantic standards for phenotypic data representation have seen slow adoption, particularly in biodiversity and ecology.
• Phenotypic and trait data in knowledge bases often lack interoperability.
• Significant challenges exist in integrating heterogeneous phenotypic datasets.

Conclusions:

• Adoption of precise, computable semantic data models is crucial for maximizing the impact of genomic data.
• Developing an interoperable phenotypic data ecosystem requires addressing current integration barriers.
• Recommendations are provided for advancing semantic standards and data integration in phenomics.