AMRomics: a scalable workflow to analyze large microbial genome collections

Whole genome analysis for microbial genomics is critical to studying and monitoring antimicrobial resistance strains. The exponential growth of microbial sequencing data necessitates a fast and scalable computational pipeline to generate the desired outputs in a timely and cost-effective manner. Recent methods have been implemented to integrate individual genomes into large collections of specific bacterial populations and are widely employed for systematic genomic surveillance. However, they do not scale well when the population expands and turnaround time remains the main issue for this type of analysis. Here, we introduce AMRomics, an optimized microbial genomics pipeline that can work efficiently with big datasets. We use different bacterial data collections to compare AMRomics against competitive tools and show that our pipeline can generate similar results of interest but with better performance. The software is open source and is publicly available at https://github.com/amromics/amromics under an MIT license

微生物基因组学领域的全基因组分析，对于研究与监测抗菌药物耐药菌株至关重要。当前微生物测序数据呈指数级增长，亟需一套快速且可扩展的计算分析流程，以实现目标输出的及时生成与成本效益优化。现有方法已实现将单个基因组整合至特定细菌种群的大型集合中，并被广泛应用于系统性基因组监测工作，但当种群规模扩大时，此类方法的扩展性欠佳，处理周转时长仍是这类分析的核心瓶颈。为此，我们推出AMRomics——一款针对大数据集可高效运行的优化型微生物基因组学分析流程。我们通过多组细菌数据集，将AMRomics与同类竞争工具进行对比，结果显示本流程可生成一致的目标分析结果，且性能更优。该软件为开源软件，以MIT许可证授权，可在https://github.com/amromics/amromics公开获取。