Automatically transforming pre- to post-composed phenotypes: EQ-lising HPO and MP

Abstract

Background: Large-scale mutagenesis projects are ongoing to improve our understanding about the pathology and subsequently the treatment of diseases. Such projects do not only record the genotype but also report phenotype descriptions of the genetically modified organisms under investigation. Thus far, phenotype data is stored in species-specific databases that lack coherence and interoperability in their phenotype representations. One suggestion to overcome the lack of integration are Entity-Quality (EQ) statements. However, a reliable automated transformation of the phenotype annotations from the databases into EQ statements is still missing.

Results: Here, we report on our ongoing efforts to develop a method (called EQ-liser) for the automated generation of EQ representations from phenotype ontology concept labels. We implemented the suggested method in a prototype and applied it to a subset of Mammalian and Human Phenotype Ontology concepts. In the case of MP, we were able to identify the correct EQ representation in over 52% of structure and process phenotypes. However, applying the EQ-liser prototype to the Human Phenotype Ontology yields a correct EQ representation in only 13.3% of the investigated cases.

Conclusions: With the application of the prototype to two phenotype ontologies, we were able to identify common patterns of mistakes when generating the EQ representation. Correcting these mistakes will pave the way to a species-independent solution to automatically derive EQ representations from phenotype ontology concept labels. Furthermore, we were able to identify inconsistencies in the existing manually defined EQ representations of current phenotype ontologies. Correcting these inconsistencies will improve the quality of the manually defined EQ statements.

Figure 1

EQ-liser’s workflow. Shows the individual steps executed with EQ-liser to decompose a phenotype ontology based on concept names.