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
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
ooab079Informations de copyright
© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.
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