AIMC Topic: Metagenome
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Genome-resolved metagenomics from short-read sequencing data in the era of artificial intelligence.
Functional & integrative genomics
Jun 10, 2025
Genome-resolved metagenomics is a computational method that enables researchers to reconstruct microbial genomes from a given sample directly. This process involves three major steps, i.e. (i) preprocessing of the reads (ii) metagenome assembly, and ...
XenoBug: machine learning-based tool to predict pollutant-degrading enzymes from environmental metagenomes.
NAR genomics and bioinformatics
Jun 1, 2025
Application of machine learning-based methods to identify novel bacterial enzymes capable of degrading a wide range of xenobiotics offers enormous potential for bioremediation of toxic and carcinogenic recalcitrant xenobiotics such as pesticides, pla...
Deciphering the biosynthetic potential of microbial genomes using a BGC language processing neural network model.
Nucleic acids research
Apr 10, 2025
Biosynthetic gene clusters (BGCs), key in synthesizing microbial secondary metabolites, are mostly hidden in microbial genomes and metagenomes. To unearth this vast potential, we present BGC-Prophet, a transformer-based language model for BGC predict...
A review of neural networks for metagenomic binning.
Briefings in bioinformatics
Mar 4, 2025
One of the main goals of metagenomic studies is to describe the taxonomic diversity of microbial communities. A crucial step in metagenomic analysis is metagenomic binning, which involves the (supervised) classification or (unsupervised) clustering o...
DeepES: deep learning-based enzyme screening to identify orphan enzyme genes.
Bioinformatics (Oxford, England)
Mar 4, 2025
MOTIVATION: Progress in sequencing technology has led to determination of large numbers of protein sequences, and large enzyme databases are now available. Although many computational tools for enzyme annotation were developed, sequence information i...
Using gut microbiome metagenomic hypervariable features for diabetes screening and typing through supervised machine learning.
Microbial genomics
Mar 1, 2025
Diabetes mellitus is a complex metabolic disorder and one of the fastest-growing global public health concerns. The gut microbiota is implicated in the pathophysiology of various diseases, including diabetes. This study utilized 16S rRNA metagenomic ...
Deep learning revealed the distribution and evolution patterns for invertible promoters across bacterial lineages.
Nucleic acids research
Nov 27, 2024
Invertible promoters (invertons) are crucial regulatory elements in bacteria, facilitating gene expression changes under stress. Despite their importance, their prevalence and the range of regulated gene functions are largely unknown. We introduced D...
VirDetect-AI: a residual and convolutional neural network-based metagenomic tool for eukaryotic viral protein identification.
Briefings in bioinformatics
Nov 22, 2024
This study addresses the challenging task of identifying viruses within metagenomic data, which encompasses a broad array of biological samples, including animal reservoirs, environmental sources, and the human body. Traditional methods for virus ide...
MicroHDF: predicting host phenotypes with metagenomic data using a deep forest-based framework.
Briefings in bioinformatics
Sep 23, 2024
The gut microbiota plays a vital role in human health, and significant effort has been made to predict human phenotypes, especially diseases, with the microbiota as a promising indicator or predictor with machine learning (ML) methods. However, the a...
DeepCheck: multitask learning aids in assessing microbial genome quality.
Briefings in bioinformatics
Sep 23, 2024
Metagenomic analyses facilitate the exploration of the microbial world, advancing our understanding of microbial roles in ecological and biological processes. A pivotal aspect of metagenomic analysis involves assessing the quality of metagenome-assem...
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