UCLA

Objectives

We sought to cluster biological phenotypes using semantic similarity and create an easy-to-install, stable, and reproducible tool.

Materials and methods

We generated Phenotype Clustering (PhenClust)-a novel application of semantic similarity for interpreting biological phenotype associations-using the Unified Medical Language System (UMLS) metathesaurus, demonstrated the tool's application, and developed Docker containers with stable installations of two UMLS versions.

Results

PhenClust identified disease clusters for drug network-associated phenotypes and a meta-analysis of drug target candidates. The Dockerized containers eliminated the requirement that the user install the UMLS metathesaurus.

Discussion

Clustering phenotypes summarized all phenotypes associated with a drug network and two drug candidates. Docker containers can support dissemination and reproducibility of tools that are otherwise limited due to insufficient software support.

Conclusion

PhenClust can improve interpretation of high-throughput biological analyses where many phenotypes are associated with a query and the Dockerized PhenClust achieved our objective of decreasing installation complexity.