[1] Guinn KM, Rubin EJ. Tuberculosis: just the FAQs. mBio, 2017; 8, e01910−17.
[2] World Health Organization. Global tuberculosis report 2024. Geneva: World Health Organization. 2024.
[3] Farhat M, Cox H, Ghanem M, et al. Drug-resistant tuberculosis: a persistent global health concern. Nat Rev Microbiol, 2024; 22, 617−35. doi:  10.1038/s41579-024-01025-1
[4] Kontsevaya I, Cabibbe AM, Cirillo DM, et al. Update on the diagnosis of tuberculosis. Clin Microbiol Infect, 2024; 30, 1115−22. doi:  10.1016/j.cmi.2023.07.014
[5] Lin ZX, Sun LQ, Wang C, et al. Bottlenecks and recent advancements in detecting Mycobacterium tuberculosis in patients with HIV. iLABMED, 2023; 1, 44−57. doi:  10.1002/ila2.11
[6] Demers AM, Venter A, Friedrich SO, et al. Direct susceptibility testing of Mycobacterium tuberculosis for pyrazinamide by use of the Bactec MGIT 960 system. J Clin Microbiol, 2016; 54, 1276−81. doi:  10.1128/JCM.03162-15
[7] Bonnet M, Gabillard D, Domoua S, et al. High performance of systematic combined urine liboarabinomannan test and sputum xpert MTB/RIF for tuberculosis screening in severely immunosuppressed ambulatory adults with human immunodeficiency virus. Clin Infect Dis, 2023; 77, 112−9. doi:  10.1093/cid/ciad125
[8] Zhang C, Sun LY, Wang D, et al. Advances in antimicrobial resistance testing. Adv Clin Chem, 2022; 111, 1−68.
[9] Meehan CJ, Goig GA, Kohl TA, et al. Whole genome sequencing of Mycobacterium tuberculosis: current standards and open issues. Nat Rev Microbiol, 2019; 17, 533−45. doi:  10.1038/s41579-019-0214-5
[10] Wang LL, Liu DK, Yung L, et al. Co-Infection with 4 species of mycobacteria identified by using next-generation sequencing. Emerg Infect Dis, 2021; 27, 2948−50. doi:  10.3201/eid2711.203458
[11] Goossens SN, Heupink TH, De Vos E, et al. Detection of minor variants in Mycobacterium tuberculosis whole genome sequencing data. Brief Bioinform, 2022; 23, bbab541. doi:  10.1093/bib/bbab541
[12] World Health Organization. WHO consolidated guidelines on tuberculosis: module 3: diagnosis: rapid diagnostics for tuberculosis detection. 3rd ed. World Health Organization. 2023.
[13] Lee JY. The principles and applications of high-throughput sequencing technologies. Dev Reprod, 2023; 27, 9−24. doi:  10.12717/DR.2023.27.1.9
[14] Reuter JA, Spacek DV, Snyder MP. High-throughput sequencing technologies. Mol Cell, 2015; 58, 586−97. doi:  10.1016/j.molcel.2015.05.004
[15] Cabibbe AM, Spitaleri A, Battaglia S, et al. Application of targeted next-generation sequencing assay on a portable sequencing platform for culture-free detection of drug-resistant tuberculosis from clinical samples. J Clin Microbiol, 2020; 58, e00632−20.
[16] Leung KSS, Tam KKG, Ng TTL, et al. Clinical utility of target amplicon sequencing test for rapid diagnosis of drug-resistant Mycobacterium tuberculosis from respiratory specimens. Front Microbiol, 2022; 13, 974428. doi:  10.3389/fmicb.2022.974428
[17] Mansoor H, Hirani N, Chavan V, et al. Clinical utility of target-based next-generation sequencing for drug-resistant TB. Int J Tuberc Lung Dis, 2023; 27, 41−48. doi:  10.5588/ijtld.22.0138
[18] Iyer A, Ndlovu Z, Sharma J, et al. Operationalising targeted next-generation sequencing for routine diagnosis of drug-resistant TB. Public Health Action, 2023; 13, 43−49. doi:  10.5588/pha.22.0041
[19] Jouet A, Gaudin C, Badalato N, et al. Deep amplicon sequencing for culture-free prediction of susceptibility or resistance to 13 anti-tuberculous drugs. Eur Respir J, 2021; 57, 2002338. doi:  10.1183/13993003.02338-2020
[20] Gómez-González PJ, Campino S, Phelan JE, et al. Portable sequencing of Mycobacterium tuberculosis for clinical and epidemiological applications. Brief Bioinform, 2022; 23, bbac256. doi:  10.1093/bib/bbac256
[21] Wang YH, Zhao Y, Bollas A, et al. Nanopore sequencing technology, bioinformatics and applications. Nat Biotechnol, 2021; 39, 1348−65. doi:  10.1038/s41587-021-01108-x
[22] Zhang C, Zhang LL, Wang F, et al. Development and performance evaluation of a culture-independent nanopore amplicon-based sequencing method for accurate typing and antimicrobial resistance profiling in Neisseria gonorrhoeae. Sci China Life Sci, 2024; 67, 421−23. doi:  10.1007/s11427-022-2382-0
[23] Zhang LL, Zhang C, Peng JP. Application of nanopore sequencing technology in the clinical diagnosis of infectious diseases. Biomed Environ Sci, 2022; 35, 381−92.
[24] Guo YF, Li ZZ, Li LJ, et al. A dual-process of targeted and unbiased Nanopore sequencing enables accurate and rapid diagnosis of lower respiratory infections. eBioMedicine, 2023; 98, 104858. doi:  10.1016/j.ebiom.2023.104858
[25] World Health Organization. Use of targeted next-generation sequencing to detect drug-resistant tuberculosis: Rapid communication, July 2023. Geneva: World Health Organization, 2023.
[26] Guo YF, Li HN, Chen HB, et al. Metagenomic next-generation sequencing to identify pathogens and cancer in lung biopsy tissue. eBioMedicine, 2021; 73, 103639. doi:  10.1016/j.ebiom.2021.103639
[27] Guo XJ, Takiff HE, Wang J, et al. An office building outbreak: the changing epidemiology of tuberculosis in Shenzhen, China. Epidemiol Infect, 2020; 148, e59. doi:  10.1017/S0950268820000552
[28] Yu HY, Zhang Y, Chen XC, et al. Whole-genome sequencing and epidemiological analysis of a tuberculosis outbreak in a high school of southern China. Infect Genet Evol, 2020; 83, 104343. doi:  10.1016/j.meegid.2020.104343
[29] Ransom EM, Potter RF, Dantas G, et al. Genomic prediction of antimicrobial resistance: ready or not, here it comes!. Clin Chem, 2020; 66, 1278−89. doi:  10.1093/clinchem/hvaa172
[30] Shi CL, Han P, Tang PJ, et al. Clinical metagenomic sequencing for diagnosis of pulmonary tuberculosis. J Infect, 2020; 81, 567−74. doi:  10.1016/j.jinf.2020.08.004
[31] Chen PX, Sun WW, He YY. Comparison of metagenomic next-generation sequencing technology, culture and GeneXpert MTB/RIF assay in the diagnosis of tuberculosis. J Thorac Dis, 2020; 12, 4014−24. doi:  10.21037/jtd-20-1232
[32] Yu GC, Shen YQ, Zhong FM, et al. Diagnostic accuracy of nanopore sequencing using respiratory specimens in the diagnosis of pulmonary tuberculosis. Int J Infect Dis, 2022; 122, 237−43. doi:  10.1016/j.ijid.2022.06.001
[33] Zhou X, Wu HL, Ruan QL, et al. Clinical evaluation of diagnosis efficacy of active Mycobacterium tuberculosis complex infection via metagenomic next-generation sequencing of direct clinical samples. Front Cell Infect Microbiol, 2019; 9, 351. doi:  10.3389/fcimb.2019.00351
[34] Xu P, Yang K, Yang L, et al. Next-generation metagenome sequencing shows superior diagnostic performance in acid-fast staining sputum smear-negative pulmonary tuberculosis and non-tuberculous mycobacterial pulmonary disease. Front Microbiol, 2022; 13, 898195. doi:  10.3389/fmicb.2022.898195
[35] Xiang ZB, Leng EL, Cao WF, et al. A systematic review and meta-analysis of the diagnostic accuracy of metagenomic next-generation sequencing for diagnosing tuberculous meningitis. Front Immunol, 2023; 14, 1223675. doi:  10.3389/fimmu.2023.1223675
[36] Chen ST, Wang CL, Zou YJ, et al. Tuberculosis-targeted next-generation sequencing and machine learning: an ultrasensitive diagnostic strategy for paucibacillary pulmonary tuberculosis and tuberculous meningitis. Clin Chim Acta, 2024; 553, 117697. doi:  10.1016/j.cca.2023.117697
[37] World Health Organization. The end TB strategy. Geneva: World Health Organization, 2015.
[38] Wu SH, Xiao YX, Hsiao HC, et al. Development and assessment of a novel whole-gene-based targeted next-generation sequencing assay for detecting the susceptibility of Mycobacterium tuberculosis to 14 drugs. Microbiol Spectr, 2022; 10, e02605−22.
[39] Dippenaar A, Goossens SN, Grobbelaar M, et al. Nanopore sequencing for Mycobacterium tuberculosis: a critical review of the literature, new developments, and future opportunities. J Clin Microbiol, 2022; 60, e00646−21.
[40] Tafess K, Ng TTL, Lao HY, et al. Targeted-sequencing workflows for comprehensive drug resistance profiling of Mycobacterium tuberculosis cultures using two commercial sequencing platforms: comparison of analytical and diagnostic performance, turnaround time, and cost. Clin Chem, 2020; 66, 809−20. doi:  10.1093/clinchem/hvaa092
[41] Chan WS, Au CH, Chung Y, et al. Rapid and economical drug resistance profiling with Nanopore MinION for clinical specimens with low bacillary burden of Mycobacterium tuberculosis. BMC Res Notes, 2020; 13, 444. doi:  10.1186/s13104-020-05287-9
[42] Gliddon HD, Frampton D, Munsamy V, et al. A rapid drug resistance genotyping workflow for Mycobacterium tuberculosis, using targeted isothermal amplification and nanopore sequencing. Microbiol Spectr, 2021; 9, e00610−21.
[43] Zhao KS, Tu CL, Chen W, et al. Rapid identification of drug-resistant tuberculosis genes using direct PCR amplification and oxford nanopore technology sequencing. Can J Infect Dis Med Microbiol, 2022; 2022, 7588033.
[44] Votintseva AA, Bradley P, Pankhurst L, et al. Same-day diagnostic and surveillance data for tuberculosis via whole-genome sequencing of direct respiratory samples. J Clin Microbiol, 2017; 55, 1285−98. doi:  10.1128/JCM.02483-16
[45] Sun YK, Li XN, He JL, et al. Rapid diagnosis of Mycoplasma pneumoniae and prediction of antibiotic resistance by nanopore adaptive sampling. iLABMED, 2024; 2, 266−76. doi:  10.1002/ila2.64
[46] Zhang LL, Yu X, Zhang C, et al. Development and comprehensive evaluation of culture-independent, long amplicon-based targeted next-generation sequencing methods for predicting antimicrobial resistance in tuberculosis. Anal Chem, 2025; 97, 281−89. doi:  10.1021/acs.analchem.4c04166
[47] Gliddon HD, Frampton D, Munsamy V, et al. A rapid drug resistance genotyping workflow for Mycobacterium tuberculosis, using targeted isothermal amplification and nanopore sequencing. Microbiol Spectr, 2021; 9, e00610-21.
[48] Song J, Du WL, Liu ZC, et al. Application of amplicon-based targeted NGS technology for diagnosis of drug-resistant tuberculosis using FFPE specimens. Microbiol Spectr, 2022; 10, e01358−21.
[49] Zhang G, Zhang HQ, Zhang Y, et al. Targeted next-generation sequencing technology showed great potential in identifying spinal tuberculosis and predicting the drug resistance. J Infect, 2023; 87, e110−2. doi:  10.1016/j.jinf.2023.10.018
[50] Coll F, Mcnerney R, Guerra-Assunção JA, et al. A robust SNP barcode for typing Mycobacterium tuberculosis complex strains. Nat Commun, 2014; 5, 4812. doi:  10.1038/ncomms5812
[51] Sharma MK, Stobart M, Akochy PM, et al. Evaluation of whole genome sequencing-based predictions of antimicrobial resistance to TB first line agents: a lesson from 5 years of data. Int J Mol Sci, 2024; 25, 6245. doi:  10.3390/ijms25116245
[52] Verboven L, Phelan J, Heupink TH, et al. TBProfiler for automated calling of the association with drug resistance of variants in Mycobacterium tuberculosis. PLoS One, 2022; 17, e0279644. doi:  10.1371/journal.pone.0279644
[53] Bradley P, Gordon NC, Walker TM, et al. Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis. Nat Commun, 2015; 6, 10063. doi:  10.1038/ncomms10063
[54] Yang TT, Gan MY, Liu QY, et al. SAM-TB: a whole genome sequencing data analysis website for detection of Mycobacterium tuberculosis drug resistance and transmission. Brief Bioinform, 2022; 23, bbac030. doi:  10.1093/bib/bbac030
[55] Gröschel MI, Owens M, Freschi L, et al. GenTB: a user-friendly genome-based predictor for tuberculosis resistance powered by machine learning. Genome Med, 2021; 13, 138. doi:  10.1186/s13073-021-00953-4
[56] Jandrasits C, Kröger S, Haas W, et al. Computational pan-genome mapping and pairwise SNP-distance improve detection of Mycobacterium tuberculosis transmission clusters. PLoS Comput Biol, 2019; 15, e1007527. doi:  10.1371/journal.pcbi.1007527
[57] Song T, Dai HH, Wang S, et al. TransCluster: a cell-type identification method for single-cell RNA-Seq data using deep learning based on transformer. Front Genet, 2022; 13, 1038919. doi:  10.3389/fgene.2022.1038919
[58] Famulare M, Hu H. Extracting transmission networks from phylogeographic data for epidemic and endemic diseases: Ebola virus in Sierra Leone, 2009 H1N1 pandemic influenza and polio in Nigeria. Int Health, 2015; 7, 298. doi:  10.1093/inthealth/ihv031
[59] Didelot X, Fraser C, Gardy J, et al. Genomic infectious disease epidemiology in partially sampled and ongoing outbreaks. Mol Biol Evol, 2017; 34, 997−1007.
[60] Thorpe J, Sawaengdee W, Ward D, et al. Multi-platform whole genome sequencing for tuberculosis clinical and surveillance applications. Sci Rep, 2024; 14, 5201. doi:  10.1038/s41598-024-55865-1
[61] Walker TM, Ip CL, Harrell RH, et al. Whole-genome sequencing to delineate Mycobacterium tuberculosis outbreaks: a retrospective observational study. Lancet Infect Dis, 2013; 13, 137−46. doi:  10.1016/S1473-3099(12)70277-3
[62] Wilson MR, Sample HA, Zorn KC, et al. Clinical metagenomic sequencing for diagnosis of meningitis and encephalitis. N Engl J Med, 2019; 380, 2327−40. doi:  10.1056/NEJMoa1803396
[63] Liu SD, Ye XC, Cheng F, et al. Clinical efficacy and computed tomography diagnostic value of bedaquiline‐containing regimens in the treatment of drug‐resistant pulmonary tuberculosis. iLABMED, 2024; 2, 149−56. doi:  10.1002/ila2.57
[64] The CRyPTIC Consortium. A data compendium associating the genomes of 12, 289 Mycobacterium tuberculosis isolates with quantitative resistance phenotypes to 13 antibiotics. PLoS Biol, 2022; 20, e3001721. doi:  10.1371/journal.pbio.3001721
[65] The CRyPTIC Consortium and the 100, 000 Genomes Project. Prediction of susceptibility to first-line tuberculosis drugs by DNA sequencing. N Engl J Med, 2018; 379, 1403−15. doi:  10.1056/NEJMoa1800474
[66] Musvosvi M, Huang H, Wang CL, et al. T cell receptor repertoires associated with control and disease progression following Mycobacterium tuberculosis infection. Nat Med, 2023; 29, 258−69. doi:  10.1038/s41591-022-02110-9
[67] Assefa DG, Dememew ZG, Zeleke ED, et al. Financial burden of tuberculosis diagnosis and treatment for patients in Ethiopia: a systematic review and meta-analysis. BMC Public Health, 2024; 24, 260. doi:  10.1186/s12889-024-17713-9
[68] Shrestha S, Addae A, Miller C, et al. Cost-effectiveness of targeted next-generation sequencing (tNGS) for detection of tuberculosis drug resistance in India, South Africa and Georgia: a modeling analysis. EClinicalMedicine, 2025; 79, 103003. doi:  10.1016/j.eclinm.2024.103003
[69] World Health Organization. Catalogue of mutations in Mycobacterium tuberculosis complex and their association with drug resistance. 2nd ed. Geneva: World Health Organization, 2023.
[70] Pankhurst LJ, Del Ojo Elias C, Votintseva AA, et al. Rapid, comprehensive, and affordable mycobacterial diagnosis with whole-genome sequencing: a prospective study. Lancet Respir Med, 2016; 4, 49−58. doi:  10.1016/S2213-2600(15)00466-X
[71] Gómez-González PJ, Campino S, Phelan JE, et al. Portable sequencing of Mycobacterium tuberculosis for clinical and epidemiological applications. Brief Bioinform, 2022; 23, bbac256.
[72] Li LS, Zhuang L, Yang L, et al. Machine learning model based on SERPING1, C1QB, and C1QC: A novel diagnostic approach for latent tuberculosis infection. iLABMED, 2024; 2, 248−65. doi:  10.1002/ila2.65
[73] Tang YW, Yao JD. Bridging the divide: Harmonizing polarized clinical laboratory medicine practices. iLABMED, 2024; 2, 67−69. doi:  10.1002/ila2.46