限られたゲノムデータからバクテリアの代謝可能性を予測する(Predicting Metabolic Potential in Bacteria From Limited Genome Data)

2024-07-24 パシフィック・ノースウェスト国立研究所(PNNL)

<関連情報>

• https://www.pnnl.gov/publications/predicting-metabolic-potential-bacteria-limited-genome-data
• https://elifesciences.org/articles/85749

MetaPathPredictで不完全な細菌ゲノムの代謝モジュールを予測する Predicting metabolic modules in incomplete bacterial genomes with MetaPathPredict

David Geller-McGrath,Kishori M Konwar,Virginia P Edgcomb,Maria Pachiadaki,Jack W Roddy,Travis J Wheeler,Jason E McDermott
eLife  Published:May 2, 2024
DOI:https://doi.org/10.7554/eLife.85749

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.