Worm Phenotype Ontology: Integrating phenotype data within and beyond the C. elegans community

Abstract

BackgroundCaenorhabditis elegans gene-based phenotype information dates back to the 1970's, beginning with Sydney Brenner and the characterization of behavioral and morphological mutant alleles via classical genetics in order to understand nervous system function. Since then C. elegans has become an important genetic model system for the study of basic biological and biomedical principles, largely through the use of phenotype analysis. Because of the growth of C. elegans as a genetically tractable model organism and the development of large-scale analyses, there has been a significant increase of phenotype data that needs to be managed and made accessible to the research community. To do so, a standardized vocabulary is necessary to integrate phenotype data from diverse sources, permit integration with other data types and render the data in a computable form.ResultsWe describe a hierarchically structured, controlled vocabulary of terms that can be used to standardize phenotype descriptions in C. elegans, namely the Worm Phenotype Ontology (WPO). The WPO is currently comprised of 1,880 phenotype terms, 74% of which have been used in the annotation of phenotypes associated with greater than 18,000 C. elegans genes. The scope of the WPO is not exclusively limited to C. elegans biology, rather it is devised to also incorporate phenotypes observed in related nematode species. We have enriched the value of the WPO by integrating it with other ontologies, thereby increasing the accessibility of worm phenotypes to non-nematode biologists. We are actively developing the WPO to continue to fulfill the evolving needs of the scientific community and hope to engage researchers in this crucial endeavor.ConclusionsWe provide a phenotype ontology (WPO) that will help to facilitate data retrieval, and cross-species comparisons within the nematode community. In the larger scientific community, the WPO will permit data integration, and interoperability across the different Model Organism Databases (MODs) and other biological databases. This standardized phenotype ontology will therefore allow for more complex data queries and enhance bioinformatic analyses.

Applications of comparative evolution to human disease genetics

“…Notably, major organismal databases now use ontologies to describe phenotypes, e.g. as for the Worm [34], Human [35], and Mammalian [36] Phenotype Ontologies.…”

dcGO: database of domain-centric ontologies on functions, phenotypes, diseases and more

“…As listed in Table 1, currently dcGO has eight phenotype and/or anatomy ontologies covering seven major model organisms. They include Mouse/Mammalian Phenotypes (MP) from Mouse Genome Informatics (MGI) (11), Worm Phenotypes (WP) from WormBase (12), Yeast/Ascomycete Phenotype (YP) from Saccharomyces Genome Database (SGD) (13), Fly Phenotype (FP) and Fly Anatomy (FA) from FlyBase (14), Zebrafish Anatomy (ZA) from ZFIN (15), Xenopus Anatomy (XA) from Xenbase (16) and Arabidopsis Plant (AP) ontology from TAIR (17). In addition to model organisms, dcGO also contains three ontologies with specific relevance to humans, including Human Phenotype (HP) (18), Disease Ontology (DO) (19) and DrugBank ATC codes (DB).…”

“…The method has been formulated in a general way, enabling it to be applied to numerous ontologies. The dcGO database now contains a panel of ontologies from a variety of contexts: functions such as GO (6,7), enzymes (8), pathways (9) and keywords used by UniProt (10); phenotype and anatomy ontologies across major model organisms, including mouse (11), worm (12), yeast (13), fly (14), zebrafish (15), Xenopus (16) and Arabidopsis (17); human phenotypes (18), diseases (19) and drugs (20). In addition to complete sets of ontological terms, a collapsed subset (slim version) is also provided for each ontology.…”

Computational tools for comparative phenomics: the role and promise of ontologies

“…It is now increasingly common for the various databases to either directly annotate their phenotype data based on this method or employ it in order to define the classes of the species and domain specific phenotype ontologies they use for annotation in order to accurately express the meaning of their phenotype term, and to perform inferences over them [84, 22, 73, 86, 97, 76, 62]. …”