Language
English
Publication Date
10-24-2025
Journal
Nature Communications
DOI
10.1038/s41467-025-64916-8
PMID
41136439
PMCID
PMC12552423
PubMedCentral® Posted Date
10-24-2025
PubMedCentral® Full Text Version
Post-print
Abstract
Long-read sequencing has transformed metagenomics and improved the quality of metagenome-assembled genomes (MAGs). However, current binning methods struggle with identifying unknown species and managing imbalanced species distributions. Here, we present LorBin, an unsupervised binner specially designed to reconstruct MAGs in natural microbiomes. LorBin deploys a two-stage multiscale adaptive DBSCAN and BIRCH clustering with evaluation decision models using single-copy genes to maximize MAG recovery. LorBin outperforms six competing binners in both simulated and real microbiomes, including oral, gut, and marine samples. LorBin generated 15-189% more high-quality MAGs with high serendipity and identified 2.4-17 times more novel taxa than state-of-the-art binning methods. Together, LorBin is a promising long-read metagenomic binner for accessing species-rich samples containing unknown taxa and is efficient at retrieving more complete genomes from imbalanced natural microbiomes.
Keywords
Metagenome, Metagenomics, Microbiota, Humans, Cluster Analysis, Gastrointestinal Microbiome, Software, Algorithms, Metagenomics, Computational biology and bioinformatics
Published Open-Access
yes
Recommended Citation
Xue, Wei; Liu, Zuo; Zhang, Yaozhong; et al., "LorBin: Efficient Binning of Long-Read Metagenomes by Multiscale Adaptive Clustering and Evaluation" (2025). Faculty and Staff Publications. 5111.
https://digitalcommons.library.tmc.edu/baylor_docs/5111
Included in
Genetic Phenomena Commons, Genetic Processes Commons, Genetic Structures Commons, Medical Genetics Commons, Medical Molecular Biology Commons, Medical Specialties Commons