放大器
计算生物学
结核分枝杆菌
多路复用
周转时间
扩增子测序
结核分枝杆菌复合物
DNA测序
肺结核
数据挖掘
计算机科学
生物
基因
遗传学
聚合酶链反应
医学
操作系统
病理
16S核糖体RNA
作者
Lulu Zhang,Xia Yu,Chi Zhang,Xin Zhang,Hairong Huang,Junping Peng
标识
DOI:10.1021/acs.analchem.4c04166
摘要
The great variety of antimicrobial resistance (AMR) profiles among tuberculosis (TB) patients necessitates a comprehensive detection method. This study developed culture-independent, long amplicon-based targeted next-generation sequencing (tNGS) methods for predicting AMR across 16 drugs within the Mycobacterium tuberculosis complex (MTBC). Multiplex PCR amplification was employed to enrich 20 gene regions, with sequencing performed on either the Oxford Nanopore Technologies (ONT) or Illumina platforms. Customized bioinformatics pipelines provide a streamlined process from raw data to clinician-friendly reports. The ONT tNGS method has been optimized, and its performance has been thoroughly evaluated, utilizing Q20+ chemistry in combination with the R10.4.1 flow cell. It requires only 15 high-quality reads per target gene to accurately identify all variants, with the turnaround time taking 4 h and 50 min. Studies confirmed that this method effectively identifies Mycobacterium species and was highly resistant to interference from other clinical pathogens. To ensure optimal coverage, it is recommended to input at least 500 copies of the genome and sequence 500MB of high-quality FASTQ data. Diagnostic performance evaluations demonstrate that this method achieves 98.35% concordance with phenotypic drug susceptibility testing (pDST) and is consistent with the results obtained from Xpert MTB/RIF assays. The design of long amplicons not only ensures comprehensive coverage of target regions but also simplifies primer design, facilitating compatibility with various sequencing platforms. Compared with previous studies, the optimized ONT tNGS method in this study significantly improves turnaround time, detection accuracy, and the comprehensive coverage of mutations associated with AMR.
科研通智能强力驱动
Strongly Powered by AbleSci AI