Evidence map›Paper›PMID 39966439›Full record

ArticleNature communications2025

Species-resolved profiling of antibiotic resistance genes in complex metagenomes through long-read overlapping with Argo.

Xi Chen, Xiaole Yin, Xiaoqing Xu, Tong Zhang

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 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

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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

4 authors.

Xi ChenEnvironmental Microbiome Engineering and Biotechnology Lab, Department of Civil Engineering, The University of Hong Kong, Hong Kong SAR, China.ORCID http://orcid.org/0009-0008-5001-7157
Xiaole YinEnvironmental Microbiome Engineering and Biotechnology Lab, Department of Civil Engineering, The University of Hong Kong, Hong Kong SAR, China.
Xiaoqing XuEnvironmental Microbiome Engineering and Biotechnology Lab, Department of Civil Engineering, The University of Hong Kong, Hong Kong SAR, China.
Tong ZhangEnvironmental Microbiome Engineering and Biotechnology Lab, Department of Civil Engineering, The University of Hong Kong, Hong Kong SAR, China. zhangt@hku.hk.ORCID http://orcid.org/0000-0003-1148-4322

Funding

University Grants Committee (UGC) T21-705/20-N
6 · The paper itself

Abstract

Environmental surveillance of antibiotic resistance genes (ARGs) is critical for understanding and mitigating the spread of antimicrobial resistance. Current short-read-based ARG profiling methods are limited in their ability to provide detailed host information, which is indispensable for tracking the transmission and assessing the risk of ARGs. Here, we present Argo, a novel approach that leverages long-read overlapping to rapidly identify and quantify ARGs in complex environmental metagenomes at the species level. Argo significantly enhances the resolution of ARG detection by assigning taxonomic labels collectively to clusters of reads, rather than to individual reads. By benchmarking the performance in host identification using simulation, we confirm the advantage of long-read overlapping over existing metagenomic profiling strategies in terms of accuracy. Using sequenced mock communities with varying quality scores and read lengths, along with a global fecal dataset comprising 329 human and non-human primate samples, we demonstrate Argo's capability to deliver comprehensive and species-resolved ARG profiles in real settings.

Indexed as

BacteriaDrug Resistance, BacterialDrug Resistance, MicrobialGenes, BacterialMetagenomeMetagenomicsAnimalsAnti-Bacterial AgentsFecesHumansAnti-Bacterial Agents

Identifiers

PMID39966439
PMCPMC11836353

What OpenQuestion holds

Textmetadata
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.