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Title: Predicting metabolic modules in incomplete bacterial genomes with MetaPathPredict
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.  more » « less
Award ID(s):
1924492
PAR ID:
10548768
Author(s) / Creator(s):
; ; ; ; ; ;
Publisher / Repository:
eLife
Date Published:
Journal Name:
eLife
Volume:
13
ISSN:
2050-084X
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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