Moumi Nazifa Ahmed, Ahmed Shafayat, Brown Connor, Pruden Amy, Zhang Liqing
Department of Computer Science, Virginia Tech, Blacksburg, VA, United States.
Genetics, Bioinformatics, and Computational Biology, Virginia Tech, Blacksburg, VA, United States.
Front Microbiol. 2025 May 21;16:1604461. doi: 10.3389/fmicb.2025.1604461. eCollection 2025.
Antibiotic resistance (AR) presents a global health challenge, necessitating an improved understanding of the ecology, evolution, and dissemination of antibiotic resistance genes (ARGs). Several tools, databases, and algorithms are now available to facilitate the identification of ARGs in metagenomic sequencing data; however, direct annotation of short-read data provides limited contextual information. Knowledge of whether an ARG is carried in the chromosome or on a specific mobile genetic element (MGE) is critical to understanding mobility, persistence, and potential for co-selection. Here we developed ARGContextProfiler, a pipeline designed to extract and visualize ARG genomic contexts. By leveraging the assembly graph for genomic neighborhood extraction and validating contexts through read mapping, ARGContextProfiler minimizes chimeric errors that are a common artifact of assembly outputs. Testing on real, synthetic, and semi-synthetic data, including long-read sequencing data from environmental samples, demonstrated that ARGContextProfiler offers superior accuracy, precision, and sensitivity compared to conventional assembly-based methods. ARGContextProfiler thus provides a powerful tool for uncovering the genomic context of ARGs in metagenomic sequencing data, which can be of value to both fundamental and applied research aimed at understanding and stemming the spread of AR. The source code of ARGContextProfiler is publicly available at GitHub.
抗生素耐药性（AR）是一项全球性的健康挑战，因此有必要更好地理解抗生素耐药基因（ARG）的生态、进化和传播情况。目前已有多种工具、数据库和算法可用于在宏基因组测序数据中识别ARG；然而，对短读长数据的直接注释提供的背景信息有限。了解一个ARG是存在于染色体上还是特定的可移动遗传元件（MGE）上，对于理解其移动性、持久性和共选择潜力至关重要。在此，我们开发了ARGContextProfiler，这是一个旨在提取和可视化ARG基因组背景的流程。通过利用组装图进行基因组邻域提取，并通过读段映射验证背景信息，ARGContextProfiler将作为组装输出常见伪影的嵌合错误降至最低。对真实、合成和半合成数据（包括来自环境样本的长读长测序数据）进行测试表明，与传统的基于组装的方法相比，ARGContextProfiler具有更高的准确性、精确性和灵敏度。因此，ARGContextProfiler为揭示宏基因组测序数据中ARG的基因组背景提供了一个强大的工具，这对于旨在理解和遏制AR传播的基础研究和应用研究都具有价值。ARGContextProfiler的源代码可在GitHub上公开获取。
