MetaBinner: a high-performance and stand-alone ensemble binning method to recover individual genomes from complex microbial communities

Genome Biology

Method

Fengzhu Sun¹  Pingqin Huang²  Ronghui You³  Ziye Wang⁴  Shanfeng Zhu⁵ 

[1] Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, USA;School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China;The Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China;The Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China;School of Mathematical Science, Fudan University, Shanghai, China;The Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China;Shanghai Qi Zhi Institute, Shanghai, China;Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China;MOE Frontiers Center for Brain Science and Shanghai Institute of Artificial Intelligence Algorithms, Fudan University, Shanghai, China;Zhangjiang Fudan International Innovation Center, Shanghai, China;

关键词: Binning;    Metagenomics;    Metagenome;   

DOI  :  10.1186/s13059-022-02832-6

 received in 2021-08-04, accepted in 2022-12-05,  发布年份 2022

来源: Springer

【 摘 要 】

Binning aims to recover microbial genomes from metagenomic data. For complex metagenomic communities, the available binning methods are far from satisfactory, which usually do not fully use different types of features and important biological knowledge. We developed a novel ensemble binner, MetaBinner, which generates component results with multiple types of features by k-means and uses single-copy gene information for initialization. It then employs a two-stage ensemble strategy based on single-copy genes to integrate the component results efficiently and effectively. Extensive experimental results on three large-scale simulated datasets and one real-world dataset demonstrate that MetaBinner outperforms the state-of-the-art binners significantly.

【 授权许可】

CC BY   
© The Author(s) 2023

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