SAM-TB: a whole genome sequencing data analysis website for detection of Mycobacterium tuberculosis drug resistance and transmission.
SAM-TB: a whole genome sequencing data analysis website for detection of Mycobacterium tuberculosis drug resistance and transmission.
复制标题
SAM-TB：结核分枝杆菌耐药及传播检测的全基因组测序数据分析网站
DOI：
10.1093/bib/bbac030
复制
发表时间：
2022-03-10
影响因子：
9.5
通讯作者：
Gao Q
中科院分区：
文献类型：
--
作者：
Yang T;Gan M;Liu Q;Liang W;Tang Q;Luo G;Zuo T;Guo Y;Hong C;Li Q;Tan W;Gao Q
全基因组测序（WGS）可以提供对耐药性、传播链和疫情识别的深入了解，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预测工具，但到目前为止，还没有一个网站将耐药预测与菌株遗传关系和非结核分枝杆菌（NTM）的种属鉴定相结合。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株测序的临床分离株和表型药物敏感试验（pDST）结果评估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还通过重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。整合的mlstverse软件识别NTM物种的准确率为98.2%，Kraken 2软件可以检测混合的MTB和NTM样品。SAM-TB还具有在用户之间共享序列数据和分析的能力。SAM-TB是一个多功能的综合网站，使用WGS原始数据准确预测抗结核药物耐药性，分析多个菌株之间的遗传关系，并确定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。
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.
登录
查看更多内容
影响因子：
6.7
作者：
Gröschel MI;Walker TM;van der Werf TS;Lange C;Niemann S;Merker M
通讯作者：
Merker M
影响因子：
16.6
作者：
Bradley P;Gordon NC;Walker TM;Dunn L;Heys S;Huang B;Earle S;Pankhurst LJ;Anson L;de Cesare M;Piazza P;Votintseva AA;Golubchik T;Wilson DJ;Wyllie DH;Diel R;Niemann S;Feuerriegel S;Kohl TA;Ismail N;Omar SV;Smith EG;Buck D;McVean G;Walker AS;Peto TE;Crook DW;Iqbal Z
通讯作者：
Iqbal Z
DOI：
10.1056/nejmoa1800474
发表时间：
2018-10-11
期刊：
The New England journal of medicine
影响因子：
--
作者：
CRyPTIC Consortium and the 100,000 Genomes Project;Allix-Béguec C;Arandjelovic I;Bi L;Beckert P;Bonnet M;Bradley P;Cabibbe AM;Cancino-Muñoz I;Caulfield MJ;Chaiprasert A;Cirillo DM;Clifton DA;Comas I;Crook DW;De Filippo MR;de Neeling H;Diel R;Drobniewski FA;Faksri K;Farhat MR;Fleming J;Fowler P;Fowler TA;Gao Q;Gardy J;Gascoyne-Binzi D;Gibertoni-Cruz AL;Gil-Brusola A;Golubchik T;Gonzalo X;Grandjean L;He G;Guthrie JL;Hoosdally S;Hunt M;Iqbal Z;Ismail N;Johnston J;Khanzada FM;Khor CC;Kohl TA;Kong C;Lipworth S;Liu Q;Maphalala G;Martinez E;Mathys V;Merker M;Miotto P;Mistry N;Moore DAJ;Murray M;Niemann S;Omar SV;Ong RT;Peto TEA;Posey JE;Prammananan T;Pym A;Rodrigues C;Rodrigues M;Rodwell T;Rossolini GM;Sánchez Padilla E;Schito M;Shen X;Shendure J;Sintchenko V;Sloutsky A;Smith EG;Snyder M;Soetaert K;Starks AM;Supply P;Suriyapol P;Tahseen S;Tang P;Teo YY;Thuong TNT;Thwaites G;Tortoli E;van Soolingen D;Walker AS;Walker TM;Wilcox M;Wilson DJ;Wyllie D;Yang Y;Zhang H;Zhao Y;Zhu B
通讯作者：
Zhu B
影响因子：
2.8
作者：
Stirling C;Andrews S;Croft T;Vickers J;Turner P;Robinson A
通讯作者：
Robinson A
DOI：
10.1093/bioinformatics/bts378
发表时间：
2012-09-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Rausch T;Zichner T;Schlattl A;Stütz AM;Benes V;Korbel JO
通讯作者：
Korbel JO