Predicting metabolic modules in incomplete bacterial genomes with MetaPathPredict.
Elife
; 132024 May 02.
Article
en En
| MEDLINE
| ID: mdl-38696239
ABSTRACT
The reconstruction of complete microbial metabolic pathways using 'omics data from environmental samples remains challenging. Computational pipelines for pathway reconstruction that utilize machine learning methods to predict the presence or absence of KEGG modules in incomplete genomes are lacking. Here, we present MetaPathPredict, a software tool that incorporates machine learning models to predict the presence of complete KEGG modules within bacterial genomic datasets. Using gene annotation data and information from the KEGG module database, MetaPathPredict employs deep learning models to predict the presence of KEGG modules in a genome. MetaPathPredict can be used as a command line tool or as a Python module, and both options are designed to be run locally or on a compute cluster. Benchmarks show that MetaPathPredict makes robust predictions of KEGG module presence within highly incomplete genomes.
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Banco de datos:
MEDLINE
Asunto principal:
Programas Informáticos
/
Genoma Bacteriano
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Redes y Vías Metabólicas
Idioma:
En
Año:
2024
Tipo del documento:
Article