Evidence map›Paper›PMID 40128535›Full record

ArticleNature communications2025

Benchmarking metagenomic binning tools on real datasets across sequencing platforms and binning modes.

Haitao Han, Ziye Wang, Shanfeng Zhu

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. Review
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

3 authors.

Haitao Han *Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.ORCID http://orcid.org/0000-0002-4372-0986
Ziye Wang *School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China.ORCID http://orcid.org/0000-0001-5850-0049
Shanfeng ZhuInstitute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China. zhusf@fudan.edu.cn.ORCID http://orcid.org/0000-0002-6067-5312

Funding

National Natural Science Foundation of China (National Science Foundation of China) 62272105
6 · The paper itself

Abstract

Metagenomic binning is a culture-free approach that facilitates the recovery of metagenome-assembled genomes by grouping genomic fragments. However, there remains a lack of a comprehensive benchmark to evaluate the performance of metagenomic binning tools across various combinations of data types and binning modes. In this study, we benchmark 13 metagenomic binning tools using short-read, long-read, and hybrid data under co-assembly, single-sample, and multi-sample binning, respectively. The benchmark results demonstrate that multi-sample binning exhibits optimal performance across short-read, long-read, and hybrid data. Moreover, multi-sample binning outperforms other binning modes in identifying potential antibiotic resistance gene hosts and near-complete strains containing potential biosynthetic gene clusters across diverse data types. This study also recommends three efficient binners across all data-binning combinations, as well as high-performance binners for each combination.

Indexed as

MetagenomeMetagenomicsBenchmarkingHigh-Throughput Nucleotide SequencingSequence Analysis, DNA

Identifiers

PMID40128535
PMCPMC11933696

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.