ArticleeLife2024
Predicting metabolic modules in incomplete bacterial genomes with MetaPathPredict.
Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Probiogenomic analysis of functional potential and safety ofMicrobiology spectrum · 2026Article
- Genome-based source attribution using a One HealthMicrobial genomics · 2026Article
- Genomic and phenotypic characterization of a human gutFuture microbiology · 2026Article
- Ecological constraints and evolutionary trade-offs shape nitrogen fixation across habitats.ISME communications · 2026Article
- Microbial Interactions in Food Fermentation: Interactions, Analysis Strategies, and Quality Enhancement.Foods (Basel, Switzerland) · 2025Review
- Microbes with higher metabolic independence are enriched in human gut microbiomes under stress.eLife · 2025Article
- A practical introduction to holo-omics.Cell reports methods · 2024Review
- Predicting variable gene content inmSystems · 2023Article
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Authors and funding
7 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.