Enhancing antimicrobial resistance detection with MetaGeneMiner: Targeted gene extraction from metagenomes
10.1097/CM9.0000000000003182
- VernacularTitle:Enhancing antimicrobial resistance detection with MetaGeneMiner: Targeted gene extraction from metagenomes
- Author:
Chang LIU
1
;
Zizhen TANG
;
Linzhu LI
;
Yan KANG
;
Yue TENG
;
Yan YU
Author Information
1. Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- Keywords:
Metagenomics;
Genomic sequencing;
Clinical diagnostics;
Reference-based assembly;
Intensive care unit infections;
Antibiotic resistance
- From:
Chinese Medical Journal
2024;137(17):2092-2098
- CountryChina
- Language:Chinese
-
Abstract:
Background::Accurately and efficiently extracting microbial genomic sequences from complex metagenomic data is crucial for advancing our understanding in fields such as clinical diagnostics, environmental microbiology, and biodiversity. As sequencing technologies evolve, this task becomes increasingly challenging due to the intricate nature of microbial communities and the vast amount of data generated. Especially in intensive care units (ICUs), infections caused by antibiotic-resistant bacteria are increasingly prevalent among critically ill patients, significantly impacting the effectiveness of treatments and patient prognoses. Therefore, obtaining timely and accurate information about infectious pathogens is of paramount importance for the treatment of patients with severe infections, which enables precisely targeted anti-infection therapies, and a tool that can extract microbial genomic sequences from metagenomic dataset would be of help.Methods::We developed MetaGeneMiner to help with retrieving specific microbial genomic sequences from metagenomes using a k-mer-based approach. It facilitates the rapid and accurate identification and analysis of pathogens. The tool is designed to be user-friendly and efficient on standard personal computers, allowing its use across a wide variety of settings. We validated MetaGeneMiner using eight metagenomic samples from ICU patients, which demonstrated its efficiency and accuracy.Results::The software extensively retrieved coding sequences of pathogens Acinetobacter baumannii and herpes simplex virus type 1 and detected a variety of resistance genes. All documentation and source codes for MetaGeneMiner are freely available at https://gitee.com/sculab/MetaGeneMiner. Conclusions::It is foreseeable that MetaGeneMiner possesses the potential for applications across multiple domains, including clinical diagnostics, environmental microbiology, gut microbiome research, as well as biodiversity and conservation biology. Particularly in ICU settings, MetaGeneMiner introduces a novel, rapid, and precise method for diagnosing and treating infections in critically ill patients. This tool is capable of efficiently identifying infectious pathogens, guiding personalized and precise treatment strategies, and monitoring the development of antibiotic resistance, significantly impacting the diagnosis and treatment of severe infections.
