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

Docker containers computational tools high-throughput analysis network analysis phenotype analysis systems biology

Journal

JAMIA open
ISSN: 2574-2531
Titre abrégé: JAMIA Open
Pays: United States
ID NLM: 101730643

Informations de publication

Date de publication:
Jul 2021
Historique:
received: 07 05 2021
revised: 12 08 2021
accepted: 02 09 2021
entrez: 20 9 2021
pubmed: 21 9 2021
medline: 21 9 2021
Statut: epublish

Résumé

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

Identifiants

pubmed: 34541463
doi: 10.1093/jamiaopen/ooab079
pii: ooab079
pmc: PMC8442701
doi:

Types de publication

Journal Article

Langues

eng

Pagination

ooab079

Informations de copyright

© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.

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Auteurs

Jennifer L Wilson (JL)

Department of Chemical and Systems Biology, Stanford University, Stanford, California, USA.

Mike Wong (M)

CoSE Computing for Life Science, San Francisco State University, San Francisco, California, USA.

Nicholas Stepanov (N)

Department of Computer Science, San Francisco State University, San Francisco, California, USA.

Dragutin Petkovic (D)

CoSE Computing for Life Science, San Francisco State University, San Francisco, California, USA.
Department of Computer Science, San Francisco State University, San Francisco, California, USA.

Russ Altman (R)

Department of Bioengineering, Stanford University, Stanford, California, USA.
Department of Genetics, Stanford University, Stanford, California, USA.

Classifications MeSH