Browsing by Author "Liu, Kevin"

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    ARGem: a new metagenomics pipeline for antibiotic resistance genes: metadata, analysis, and visualization
    Liang, Xiao; Zhang, Jingyi; Kim, Yoonjin; Ho, Josh; Liu, Kevin; Keenum, Ishi M.; Gupta, Suraj; Davis, Benjamin; Hepp, Shannon L.; Zhang, Liqing; Xia, Kang; Knowlton, Katharine F.; Liao, Jingqiu; Vikesland, Peter J.; Pruden, Amy; Heath, Lenwood S. (Frontiers, 2023-09-15)
    Antibiotic resistance is of crucial interest to both human and animal medicine. It has been recognized that increased environmental monitoring of antibiotic resistance is needed. Metagenomic DNA sequencing is becoming an attractive method to profile antibiotic resistance genes (ARGs), including a special focus on pathogens. A number of computational pipelines are available and under development to support environmental ARG monitoring; the pipeline we present here is promising for general adoption for the purpose of harmonized global monitoring. Specifically, ARGem is a user-friendly pipeline that provides full-service analysis, from the initial DNA short reads to the final visualization of results. The capture of extensive metadata is also facilitated to support comparability across projects and broader monitoring goals. The ARGem pipeline offers efficient analysis of a modest number of samples along with affordable computational components, though the throughput could be increased through cloud resources, based on the user’s configuration. The pipeline components were carefully assessed and selected to satisfy tradeoffs, balancing efficiency and flexibility. It was essential to provide a step to perform short read assembly in a reasonable time frame to ensure accurate annotation of identified ARGs. Comprehensive ARG and mobile genetic element databases are included in ARGem for annotation support. ARGem further includes an expandable set of analysis tools that include statistical and network analysis and supports various useful visualization techniques, including Cytoscape visualization of co-occurrence and correlation networks. The performance and flexibility of the ARGem pipeline is demonstrated with analysis of aquatic metagenomes. The pipeline is freely available at https://github.com/xlxlxlx/ARGem.