文献类型： 外文期刊

作者： Qiu, Zhiguang ¹ ; Yuan, Li ² ; Lian, Chun-Ang ¹ ; Lin, Bin ³ ; Chen, Jie ² ; Mu, Rong ¹ ; Qiao, Xuejiao ¹ ; Zhang, Liyu ¹ ; Xu, Zheng ⁵ ; Fan, Lu ⁷ ; Zhang, Yunzeng ⁸ ; Wang, Shanquan ⁹ ; Li, Junyi ¹⁰ ; Cao, Huiluo ¹¹ ; Li, Bing ¹² ; Chen, Baowei ¹³ ; Song, Chi ¹⁴ ; Liu, Yongxin ¹⁶ ; Shi, Lili ² ; Tian, Yonghong ² ; Ni, Jinren ¹ ; Zhang, Tong ¹⁹ ; Zhou, Jizhong ²⁰ ; Zhuang, Wei-Qin ²¹ ; Yu, Ke ¹ ;

作者机构： 1.Peking Univ, Sch Environm & Energy, Shenzhen Grad Sch, Ecoenvironm & Resource Efficiency Res Lab, Shenzhen, Peoples R China

2.Peking Univ, AI Sci Preferred Program AI4S, Shenzhen, Peoples R China

3.Peking Univ, Sch Elect & Comp Engn, Shenzhen, Peoples R China

4.Peng Cheng Lab, Shenzhen, Peoples R China

5.Southern Univ Sci & Technol, Yantian Hosp, Shenzhen, Peoples R China

6.Chinese Acad Sci, Inst Biomed & Biotechnol, Shenzhen Inst Adv Technol, Shenzhen, Guangdong, Peoples R China

7.Southern Univ Sci & Technol SUSTech, Dept Ocean Sci & Engn, Shenzhen, Peoples R China

8.Yangzhou Univ, Joint Int Res Lab Agr & Agriprod Safety, Minist Educ China, Yangzhou, Peoples R China

9.Sun Yat Sen Univ, Environm Microbi Res Ctr, Sch Environm Sci & Engn, Guangzhou, Peoples R China

10.Harbin Inst Technol Shenzhen, Sch Comp Sci & Technol, Shenzhen, Guangdong, Peoples R China

11.Univ Hong Kong, Dept Microbiol, Hong Kong, Peoples R China

12.Tsinghua Univ, Shenzhen Int Grad Sch, Shenzhen, Peoples R China

13.Sun Yat sen Univ, Sch Marine Sci, Guangdong Prov Key Lab Marine Resources & Coastal, Zhuhai, Peoples R China

14.Chengdu Univ Tradit Chinese Med, Inst Herbgen, Chengdu, Peoples R China

15.Wuhan Benagen Technol Co Ltd, Wuhan, Peoples R China

16.Chinese Acad Agr Sci, Agr Genom Inst Shenzhen, Shenzhen Branch, Guangdong Lab Lingnan Modern Agr,Genome Anal Lab,M, Shenzhen, Peoples R China

17.Peking Univ, Sch Chem Biol & Biotechnol, State Key Lab Chem Oncogen, Shenzhen Grad Sch, Shenzhen, Peoples R China

18.Peking Univ, Coll Environm Sci & Engn, Key Lab Water & Sediment Sci, Minist Educ, Beijing, Peoples R China

19.Univ Hong Kong, Dept Civil Engn, Hong Kong, Peoples R China

20.Univ Oklahoma, Inst Environm Genom, Norman, OK USA

21.Univ Auckland, Fac Engn, Dept Civil & Environm Engn, Auckland, New Zealand

期刊名称：NATURE COMMUNICATIONS （ 影响因子：16.6； 五年影响因子：17.0 ）

ISSN：

年卷期： 2024 年 15 卷 1 期

页码：

收录情况： SCI

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 similar to 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.