ESPERANTO: a GLP-field sEmi-SuPERvised toxicogenomics metadAta curatioN TOol.
Bioinformatics
; 39(6)2023 06 01.
Article
em En
| MEDLINE
| ID: mdl-37354497
ABSTRACT
SUMMARY:
Biological data repositories are an invaluable source of publicly available research evidence. Unfortunately, the lack of convergence of the scientific community on a common metadata annotation strategy has resulted in large amounts of data with low FAIRness (Findable, Accessible, Interoperable and Reusable). The possibility of generating high-quality insights from their integration relies on data curation, which is typically an error-prone process while also being expensive in terms of time and human labour. Here, we present ESPERANTO, an innovative framework that enables a standardized semi-supervised harmonization and integration of toxicogenomics metadata and increases their FAIRness in a Good Laboratory Practice-compliant fashion. The harmonization across metadata is guaranteed with the definition of an ad hoc vocabulary. The tool interface is designed to support the user in metadata harmonization in a user-friendly manner, regardless of the background and the type of expertise. AVAILABILITY AND IMPLEMENTATION ESPERANTO and its user manual are freely available for academic purposes at https//github.com/fhaive/esperanto. The input and the results showcased in Supplementary File S1 are available at the same link.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Software
/
Metadados
Tipo de estudo:
Guideline
Limite:
Humans
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article