Related Experiment Video
Updated: Jan 14, 2026
Large-Scale Screens of Metagenomic Libraries
Published on: May 28, 2007
LorBin: efficient binning of long-read metagenomes by multiscale adaptive clustering and evaluation
Wei Xue1, Zuo Liu1, Yaozhong Zhang1
1College of Artificial Intelligence, Jiangsu Provincial Key Lab for Solid Organic Waste Utilization, Key Lab of Organic-based Fertilizers of China, Jiangsu Collaborative Innovation Center of Solid Organic Wastes, Educational Ministry Engineering Center of Resource-Saving Fertilizers, Nanjing Agricultural University, Nanjing, China.
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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.
