Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China.
Fudan University, Shanghai, China.
Brief Bioinform. 2022 Mar 10;23(2). doi: 10.1093/bib/bbac030.
Whole genome sequencing (WGS) can provide insight into drug-resistance, transmission chains and the identification of outbreaks, but data analysis remains an obstacle to its routine clinical use. Although several drug-resistance prediction tools have appeared, until now no website integrates drug-resistance prediction with strain genetic relationships and species identification of nontuberculous mycobacteria (NTM). We have established a free, function-rich, user-friendly online platform for MTB WGS data analysis (SAM-TB, http://samtb.szmbzx.com) that integrates drug-resistance prediction for 17 antituberculosis drugs, detection of variants, analysis of genetic relationships and NTM species identification. The accuracy of SAM-TB in predicting drug-resistance was assessed using 3177 sequenced clinical isolates with results of phenotypic drug-susceptibility tests (pDST). Compared to pDST, the sensitivity of SAM-TB for detecting multidrug-resistant tuberculosis was 93.9% [95% confidence interval (CI) 92.6-95.1%] with specificity of 96.2% (95% CI 95.2-97.1%). SAM-TB also analyzes the genetic relationships between multiple strains by reconstructing phylogenetic trees and calculating pairwise single nucleotide polymorphism (SNP) distances to identify genomic clusters. The incorporated mlstverse software identifies NTM species with an accuracy of 98.2% and Kraken2 software can detect mixed MTB and NTM samples. SAM-TB also has the capacity to share both sequence data and analysis between users. SAM-TB is a multifunctional integrated website that uses WGS raw data to accurately predict antituberculosis drug-resistance profiles, analyze genetic relationships between multiple strains and identify NTM species and mixed samples containing both NTM and MTB. SAM-TB is a useful tool for guiding both treatment and epidemiological investigation.
全基因组测序（WGS）可以深入了解耐药性、传播链和暴发的识别，但数据分析仍然是其常规临床应用的障碍。虽然已经出现了几种耐药性预测工具，但到目前为止，还没有一个网站将耐药性预测与非结核分枝杆菌（NTM）的菌株遗传关系和物种鉴定相结合。我们建立了一个免费、功能丰富、用户友好的 MTB WGS 数据分析在线平台（SAM-TB，http://samtb.szmbzx.com），该平台集成了 17 种抗结核药物的耐药性预测、变异检测、遗传关系分析和 NTM 物种鉴定。使用 3177 例经测序的临床分离株的表型药敏试验（pDST）结果评估了 SAM-TB 预测耐药性的准确性。与 pDST 相比，SAM-TB 检测耐多药结核病的敏感性为 93.9%（95%CI 92.6-95.1%），特异性为 96.2%（95%CI 95.2-97.1%）。SAM-TB 还通过构建系统发育树和计算成对单核苷酸多态性（SNP）距离来分析多个菌株之间的遗传关系，以识别基因组簇。内置的 mlstverse 软件可以准确识别 NTM 物种，准确率为 98.2%，Kraken2 软件可以检测含有 MTB 和 NTM 的混合样本。SAM-TB 还具有在用户之间共享序列数据和分析结果的能力。SAM-TB 是一个功能齐全的综合网站，它使用 WGS 原始数据准确预测抗结核药物耐药谱，分析多个菌株之间的遗传关系，鉴定 NTM 物种和含有 NTM 和 MTB 的混合样本。SAM-TB 是指导治疗和流行病学调查的有用工具。
