Evidence map›Paper›PMID 38467684›Full record

ArticleNature communications2024

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 and 15 more

Abstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

30 citing papers in PubMed.

  1. Article
  2. Microorganisms · 2026
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  8. Spatially resolved denitrification coupled with methane and arsenite oxidation at the millimeter-scale straw-soil interface.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  9. Article
  10. Review
  11. An integrated culturomic and genomic database and analysis platform for methanogenic archaea.Database : the journal of biological databases and curation · 2026
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Microbial necromass carbon enhances arsenic methylation in paddy soils.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

25 authors.

Zhiguang Qiu *Eco-environment and Resource Efficiency Research Laboratory, School of Environment and Energy, Shenzhen Graduate School, Peking University, Shenzhen, China.ORCID 0000-0002-4988-6135
Li YuanAI for Science (AI4S)-Preferred Program, Peking University, Shenzhen, China.
Chun-Ang LianEco-environment and Resource Efficiency Research Laboratory, School of Environment and Energy, Shenzhen Graduate School, Peking University, Shenzhen, China.ORCID 0000-0002-2120-5588
Bin LinSchool of Electronic and Computer Engineering, Peking University, Shenzhen, China.
Jie ChenAI for Science (AI4S)-Preferred Program, Peking University, Shenzhen, China.
Rong MuEco-environment and Resource Efficiency Research Laboratory, School of Environment and Energy, Shenzhen Graduate School, Peking University, Shenzhen, China.
Xuejiao QiaoEco-environment and Resource Efficiency Research Laboratory, School of Environment and Energy, Shenzhen Graduate School, Peking University, Shenzhen, China.
Liyu ZhangEco-environment and Resource Efficiency Research Laboratory, School of Environment and Energy, Shenzhen Graduate School, Peking University, Shenzhen, China.
Zheng XuSouthern University of Sciences and Technology Yantian Hospital, Shenzhen, China.
Lu FanDepartment of Ocean Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, China.ORCID 0000-0002-4184-7211
Yunzeng ZhangJoint International Research Laboratory of Agriculture and Agri-Product Safety, the Ministry of Education of China, Yangzhou University, Yangzhou, China.
Shanquan WangEnvironmental Microbiomics Research Center, School of Environmental Science and Engineering, Sun Yat-Sen University, Guangzhou, China.ORCID 0000-0002-5880-5894
Junyi LiSchool of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, China.ORCID 0000-0001-8045-5264
Huiluo CaoDepartment of Microbiology, University of Hong Kong, Hong Kong, China.
Bing LiShenzhen International Graduate School, Tsinghua University, Shenzhen, China.ORCID 0000-0002-7161-0477
Baowei ChenGuangdong Provincial Key Laboratory of Marine Resources and Coastal Engineering, School of Marine Sciences, Sun Yat-sen University, Zhuhai, China.
Chi SongInstitute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu, China.ORCID 0000-0003-3904-963X
Yongxin LiuShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.ORCID 0000-0003-1832-9835
Lili ShiAI for Science (AI4S)-Preferred Program, Peking University, Shenzhen, China.
Yonghong TianAI for Science (AI4S)-Preferred Program, Peking University, Shenzhen, China.ORCID 0000-0002-2978-5935
Jinren NiEco-environment and Resource Efficiency Research Laboratory, School of Environment and Energy, Shenzhen Graduate School, Peking University, Shenzhen, China.ORCID 0000-0002-9114-8347
Tong ZhangDepartment of Civil Engineering, University of Hong Kong, Hong Kong, China.ORCID 0000-0003-1148-4322
Jizhong ZhouInstitute for Environmental Genomics, University of Oklahoma, Norman, OK, USA.ORCID 0000-0003-2014-0564
Wei-Qin ZhuangDepartment of Civil and Environmental Engineering, Faculty of Engineering, University of Auckland, Auckland, New Zealand.
Ke Yu *Eco-environment and Resource Efficiency Research Laboratory, School of Environment and Energy, Shenzhen Graduate School, Peking University, Shenzhen, China. yuke.sz@pku.edu.cn.ORCID 0000-0001-5039-6056

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

EcosystemMetagenomeSilicatesMetagenomicsbasaltSilicates

Identifiers

PMID38467684
PMCPMC10928208

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.