PhenClust, a standalone tool for identifying trends within sets of biological phenotypes using semantic similarity and the Unified Medical Language System metathesaurus

Jennifer L Wilson; Mike Wong; Nicholas Stepanov; Dragutin Petkovic; Russ Altman

doi:10.1093/jamiaopen/ooab079

PhenClust, a standalone tool for identifying trends within sets of biological phenotypes using semantic similarity and the Unified Medical Language System metathesaurus

JAMIA Open. 2021 Sep 15;4(3):ooab079. doi: 10.1093/jamiaopen/ooab079. eCollection 2021 Jul.

Authors

Jennifer L Wilson¹, Mike Wong², Nicholas Stepanov³, Dragutin Petkovic^{2

3}, Russ Altman^{4

5}

Affiliations

¹ Department of Chemical and Systems Biology, Stanford University, Stanford, California, USA.
² CoSE Computing for Life Science, San Francisco State University, San Francisco, California, USA.
³ Department of Computer Science, San Francisco State University, San Francisco, California, USA.
⁴ Department of Bioengineering, Stanford University, Stanford, California, USA.
⁵ Department of Genetics, Stanford University, Stanford, California, USA.

Abstract

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.

Keywords: Docker containers; computational tools; high-throughput analysis; network analysis; phenotype analysis; systems biology.