BASALT refines binning from metagenomic data and increases resolution of genome-resolved metagenomic analysis

Zhiguang Qiu, Li Yuan, Chun-Ang Lian, Bin Lin, Jie Chen, Rong Mu, Xuejiao Qiao, Liyu Zhang, Zheng Xu, Lu Fan, Yunzeng Zhang, Shanquan Wang, Junyi Li, Huiluo Cao, Bing Li, Baowei Chen, Chi Song, Yongxin Liu, Lili Shi, Yonghong Tian, Jinren Ni, Tong Zhang, Jizhong Zhou, Wei-Qin Zhuang and Ke Yu ()
Additional contact information
Zhiguang Qiu: Peking University
Li Yuan: Peking University
Chun-Ang Lian: Peking University
Bin Lin: Peking University
Jie Chen: Peking University
Rong Mu: Peking University
Xuejiao Qiao: Peking University
Liyu Zhang: Peking University
Zheng Xu: Southern University of Sciences and Technology Yantian Hospital
Lu Fan: Southern University of Science and Technology (SUSTech)
Yunzeng Zhang: Yangzhou University
Shanquan Wang: Sun Yat-Sen University
Junyi Li: Harbin Institute of Technology (Shenzhen)
Huiluo Cao: University of Hong Kong
Bing Li: Tsinghua University
Baowei Chen: Sun Yat-sen University
Chi Song: Chengdu University of Traditional Chinese Medicine
Yongxin Liu: Chinese Academy of Agricultural Sciences
Lili Shi: Peking University
Yonghong Tian: Peking University
Jinren Ni: Peking University
Tong Zhang: University of Hong Kong
Jizhong Zhou: University of Oklahoma
Wei-Qin Zhuang: University of Auckland
Ke Yu: Peking University

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract Metagenomic binning is an essential technique for genome-resolved characterization of uncultured microorganisms in various ecosystems but hampered by the low efficiency of binning tools in adequately recovering metagenome-assembled genomes (MAGs). Here, we introduce BASALT (Binning Across a Series of Assemblies Toolkit) for binning and refinement of short- and long-read sequencing data. BASALT employs multiple binners with multiple thresholds to produce initial bins, then utilizes neural networks to identify core sequences to remove redundant bins and refine non-redundant bins. Using the same assemblies generated from Critical Assessment of Metagenome Interpretation (CAMI) datasets, BASALT produces up to twice as many MAGs as VAMB, DASTool, or metaWRAP. Processing assemblies from a lake sediment dataset, BASALT produces ~30% more MAGs than metaWRAP, including 21 unique class-level prokaryotic lineages. Functional annotations reveal that BASALT can retrieve 47.6% more non-redundant opening-reading frames than metaWRAP. These results highlight the robust handling of metagenomic sequencing data of BASALT.

Date: 2024
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DOI: 10.1038/s41467-024-46539-7

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