SAM-TB: a whole genome sequencing data analysis website for detection of Mycobacterium tuberculosis drug resistance and transmission

Tingting Yang; Mingyu Gan; Qingyun Liu; Wenying Liang; Qiqin Tang; Geyang Luo; Tianyu Zuo; Yongchao Guo; Chuangyue Hong; Qibing Li; Weiguo Tan; Qian Gao

doi:10.1093/bib/bbac030

SAM-TB: a whole genome sequencing data analysis website for detection of Mycobacterium tuberculosis drug resistance and transmission

Brief Bioinform. 2022 Mar 10;23(2):bbac030. doi: 10.1093/bib/bbac030.

Authors

Tingting Yang¹, Mingyu Gan², Qingyun Liu², Wenying Liang³, Qiqin Tang³, Geyang Luo⁴, Tianyu Zuo⁵, Yongchao Guo⁶, Chuangyue Hong⁷, Qibing Li³, Weiguo Tan⁷, Qian Gao⁴

Affiliations

¹ Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China.
² Fudan University, Shanghai, China.
³ Clabee Genomics, Shenzhen, China.
⁴ Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), School of Basic Medical Sciences, Shanghai Medical College, Shanghai Institute of Infectious Disease and Biosecurity and Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.
⁵ Fudan University, Shanghai, China. Presently, he is a data analyst in patsnap, Shanghai, China.
⁶ Shenzhen Uni-medica Technology Co., Ltd, Shenzhen, China.
⁷ Shenzhen Center for Chronic Disease Control, Shenzhen, China.

Abstract

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.

Keywords: drug-resistant tuberculosis; drug-susceptibility testing; nontuberculous mycobacteria; transmission; whole genome sequencing.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Antitubercular Agents / pharmacology
Antitubercular Agents / therapeutic use
Data Analysis
Drug Resistance
Mycobacterium tuberculosis*
Phylogeny
Whole Genome Sequencing / methods

Substances

Antitubercular Agents