数字农科院2.0

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

文献类型： 外文期刊

作者： 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;Ke Yu

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期刊名称： Nature Communications

ISSN： 2041-1723

年卷期： 2024 年 15 卷 1 期

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收录情况： SCIE（2024版）

摘要： 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.

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