Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Whole genome sequencing of Mycobacterium tuberculosis: current standards and open issues

Nature Reviews Microbiology volume 17, pages 533–545 (2019)Cite this article

Subjects

Abstract

Whole genome sequencing (WGS) of Mycobacterium tuberculosis has rapidly progressed from a research tool to a clinical application for the diagnosis and management of tuberculosis and in public health surveillance. This development has been facilitated by drastic drops in cost, advances in technology and concerted efforts to translate sequencing data into actionable information. There is, however, a risk that, in the absence of a consensus and international standards, the widespread use of WGS technology may result in data and processes that lack harmonization, comparability and validation. In this Review, we outline the current landscape of WGS pipelines and applications, and set out best practices for M. tuberculosis WGS, including standards for bioinformatics pipelines, curated repositories of resistance-causing variants, phylogenetic analyses, quality control and standardized reporting.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Whole genome sequencing of Mycobacterium tuberculosis.
Fig. 2: Standard workflow for whole genome sequencing of Mycobacterium tuberculosis complex isolates.
Fig. 3: Current and potential approaches for determining resistance-related polymorphisms.
Fig. 4: Epidemiological and within-host applications of SNP-based comparisons between Mycobacterium tuberculosis complex isolates.

Similar content being viewed by others

References

  1. World Health Organization. Global tuberculosis report 2018. WHO https://www.who.int/tb/publications/global_report/archive/ (2018).

  2. The CRyPTIC Consortium and the 100,000 Genomes Project. Prediction of susceptibility to first-line tuberculosis drugs by DNA sequencing. N. Engl. J. Med. 379, 1403–1415 (2018).The first large-scale study demonstrating how phenotypic testing can be replaced by WGS for first-line drug testing.

    Google Scholar 

  3. Gardy, J. L. et al. Whole-genome sequencing and social-network analysis of a tuberculosis outbreak. N. Engl. J. Med. 364, 730–739 (2011).

    CAS  PubMed  Google Scholar 

  4. Cole, S. T. et al. Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence. Nature 393, 537–544 (1998).

    CAS  PubMed  Google Scholar 

  5. Cabibbe, A. M., Walker, T. M., Niemann, S. & Cirillo, D. M. Whole genome sequencing of Mycobacterium tuberculosis. Eur. Respir. J. 52, 1801163 (2018).

    PubMed  Google Scholar 

  6. Satta, G. et al. Mycobacterium tuberculosis and whole-genome sequencing: how close are we to unleashing its full potential? Clin. Microbiol. Infect. 24, 604–609 (2018).An extensive review of the literature outlining the potential of WGS for TB research and clinical use.

    CAS  PubMed  Google Scholar 

  7. Lipworth, S. et al. SNP-IT tool for identifying subspecies and associated lineages of Mycobacterium tuberculosis complex. Emerg. Infect. Dis. 25, 482–488 (2019).

    PubMed  PubMed Central  Google Scholar 

  8. Coll, F. et al. A robust SNP barcode for typing Mycobacterium tuberculosis complex strains. Nat. Commun. 5, 4812 (2014).This study reports the now standard sublineage typing scheme using SNP-based information for MTBC.

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Homolka, S. et al. High resolution discrimination of clinical Mycobacterium tuberculosis complex strains based on single nucleotide polymorphisms. PLOS ONE 7, e39855 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Trauner, A. et al. The within-host population dynamics of Mycobacterium tuberculosis vary with treatment efficacy. Genome Biol. 18, 71 (2017).

    PubMed  PubMed Central  Google Scholar 

  11. Merker, M., Kohl, T. A., Niemann, S. & Supply, P. The evolution of strain typing in the Mycobacterium tuberculosis complex. Adv. Exp. Med. Biol. 1019, 43–78 (2017).

    CAS  PubMed  Google Scholar 

  12. Jajou, R. et al. Epidemiological links between tuberculosis cases identified twice as efficiently by whole genome sequencing than conventional molecular typing: A population-based study. PLOS ONE 13, e0195413 (2018). This study shows the advantage of WGS approaches over mycobacterial interspersed repetitive unit variable-number tandem repeat genotyping for detection of transmission clusters.

    PubMed  PubMed Central  Google Scholar 

  13. Wyllie, D. H. et al. A quantitative evaluation of MIRU-VNTR typing against whole-genome sequencing for identifying Mycobacterium tuberculosis transmission: a prospective observational cohort study. EBioMedicine 34, 122–130 (2018).

    PubMed  PubMed Central  Google Scholar 

  14. Walker, T. M. et al. A cluster of multidrug-resistant Mycobacterium tuberculosis among patients arriving in Europe from the Horn of Africa: a molecular epidemiological study. Lancet Infect. Dis. 18, 431–440 (2018).

    PubMed  PubMed Central  Google Scholar 

  15. Tagliani, E. et al. EUSeqMyTB to set standards and build capacity for whole genome sequencing for tuberculosis in the EU. Lancet Infect. Dis. 18, 377 (2018). Announcement of the European Centre for Disease Prevention and Control efforts to establish and validate the use of WGS for all TB public health initiatives.

    PubMed  Google Scholar 

  16. Cohen, K. A. et al. Evolution of extensively drug-resistant tuberculosis over four decades: whole genome sequencing and dating analysis of Mycobacterium tuberculosis isolates from KwaZulu-Natal. PLOS Med. 12, e1001880 (2015).

    PubMed  PubMed Central  Google Scholar 

  17. Eldholm, V. et al. Four decades of transmission of a multidrug-resistant Mycobacterium tuberculosis outbreak strain. Nat. Commun. 6, 7119 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Merker, M. et al. Evolutionary history and global spread of the Mycobacterium tuberculosis Beijing lineage. Nat. Genet. 47, 242–249 (2015).

    CAS  PubMed  Google Scholar 

  19. Zignol, M. et al. Genetic sequencing for surveillance of drug resistance in tuberculosis in highly endemic countries: a multi-country population-based surveillance study. Lancet Infect. Dis. 18, 675–683 (2018).

    PubMed  PubMed Central  Google Scholar 

  20. Gröschel, M. I. et al. Pathogen-based precision medicine for drug-resistant tuberculosis. PLOS Pathog. 14, e1007297 (2018).

    PubMed  PubMed Central  Google Scholar 

  21. World Health Organization. The use of next-generation sequencing technologies for the detection of mutations associated with drug resistance in Mycobacterium tuberculosis complex: technical guide. WHO https://apps.who.int/iris/handle/10665/274443 (2018). This guide is the first step towards validation of WGS as a tool for MTBC clinical and public health work.

  22. Nebenzahl-Guimaraes, H., Jacobson, K. R., Farhat, M. R. & Murray, M. B. Systematic review of allelic exchange experiments aimed at identifying mutations that confer drug resistance in Mycobacterium tuberculosis. J. Antimicrob. Chemother. 69, 331–342 (2014).

    CAS  PubMed  Google Scholar 

  23. Sandgren, A. et al. Tuberculosis drug resistance mutation database. PLOS Med. 6, e1000002 (2009).

    PubMed Central  Google Scholar 

  24. Coll, F. et al. Rapid determination of anti-tuberculosis drug resistance from whole-genome sequences. Genome Med. 7, 51 (2015).

    PubMed  PubMed Central  Google Scholar 

  25. Miotto, P. et al. A standardised method for interpreting the association between mutations and phenotypic drug resistance in Mycobacterium tuberculosis. Eur. Respir. J. 50, 1701354 (2017). This was the first study to use a score system to classify mutations for clinical interpretation.

    PubMed  PubMed Central  Google Scholar 

  26. Starks, A. M. et al. Collaborative effort for a centralized worldwide tuberculosis relational sequencing data platform. Clin. Infect. Dis. 61, S141–S146 (2015). This publication outlines the design and use of the ReSeqTB platform.

    PubMed Central  Google Scholar 

  27. Brown, T., Nikolayevskyy, V., Velji, P. & Drobniewski, F. Associations between Mycobacterium tuberculosis strains and phenotypes. Emerg. Infect. Dis. 16, 272–280 (2010).

    PubMed  PubMed Central  Google Scholar 

  28. Comas, I. et al. Out-of-Africa migration and Neolithic coexpansion of Mycobacterium tuberculosis with modern humans. Nat. Genet. 45, 1176–1182 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Meehan, C. J. et al. The relationship between transmission time and clustering methods in Mycobacterium tuberculosis epidemiology. EBioMedicine 37, 410–416 (2018).

    PubMed  PubMed Central  Google Scholar 

  30. Kohl, T. A. et al. Harmonized genome wide typing of tubercle bacilli using a web-based gene-by-gene nomenclature system. EBioMedicine 34, 131–138 (2018).

    PubMed  PubMed Central  Google Scholar 

  31. Walker, T. M. et al. Whole-genome sequencing to delineate Mycobacterium tuberculosis outbreaks: a retrospective observational study. Lancet Infect. Dis. 13, 137–146 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Koster, K. J. et al. Genomic sequencing is required for identification of tuberculosis transmission in Hawaii. BMC Infect. Dis. 18, 608 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Kohl, T. A. et al. MTBseq: a comprehensive pipeline for whole genome sequence analysis of Mycobacterium tuberculosis complex isolates. PeerJ 6, e5895 (2018).

    PubMed  PubMed Central  Google Scholar 

  34. Ezewudo, M. et al. Integrating standardized whole genome sequence analysis with a global Mycobacterium tuberculosis antibiotic resistance knowledgebase. Sci. Rep. 8, 15382 (2018).

    PubMed  PubMed Central  Google Scholar 

  35. Brynildsrud, O. B. et al. Global expansion of Mycobacterium tuberculosis lineage 4 shaped by colonial migration and local adaptation. Sci. Adv. 4, eaat5869 (2018).

    PubMed  PubMed Central  Google Scholar 

  36. Brown, A. C. et al. Rapid whole-genome sequencing of Mycobacterium tuberculosis isolates directly from clinical samples. J. Clin. Microbiol. 53, 2230–2237 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Conceição, E. C. et al. Analysis of potential household transmission events of tuberculosis in the city of Belem, Brazil. Tuberculosis 113, 125–129 (2018).

    PubMed  Google Scholar 

  38. Walker, T. M. et al. Whole-genome sequencing for prediction of Mycobacterium tuberculosis drug susceptibility and resistance: a retrospective cohort study. Lancet Infect. Dis. 15, 1193–1202 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Goig, G. A., Blanco, S., Garcia-Basteiro, A. & Comas, I. Pervasive contaminations in sequencing experiments are a major source of false genetic variability: a Mycobacterium tuberculosis meta-analysis. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/403824v1 (2018).

  40. Menardo, F. et al. Treemmer: a tool to reduce large phylogenetic datasets with minimal loss of diversity. BMC Bioinformatics 19, 164 (2018).

    PubMed  PubMed Central  Google Scholar 

  41. Bryant, J. M. et al. Inferring patient to patient transmission of Mycobacterium tuberculosis from whole genome sequencing data. BMC Infect. Dis. 13, 110 (2013).

    PubMed  PubMed Central  Google Scholar 

  42. Shea, J. et al. Comprehensive whole-genome sequencing and reporting of drug resistance profiles on clinical cases of Mycobacterium tuberculosis in New York state. J. Clin. Microbiol. 55, 1871–1882 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Phelan, J. et al. Mycobacterium tuberculosis whole genome sequencing and protein structure modelling provides insights into anti-tuberculosis drug resistance. BMC Med. 14, 31 (2016).

    PubMed  PubMed Central  Google Scholar 

  44. Witney, A. A. et al. Use of whole-genome sequencing to distinguish relapse from reinfection in a completed tuberculosis clinical trial. BMC Med. 15, 71 (2017).

    PubMed  PubMed Central  Google Scholar 

  45. Casali, N. et al. Whole genome sequence analysis of a large isoniazid-resistant tuberculosis outbreak in London: a retrospective observational study. PLOS Med. 13, e1002137 (2016).

    PubMed  PubMed Central  Google Scholar 

  46. Feuerriegel, S. et al. PhyResSE: a web tool delineating Mycobacterium tuberculosis antibiotic resistance and lineage from whole-genome sequencing data. J. Clin. Microbiol. 53, 1908–1914 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Bradley, P. et al. Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis. Nat. Commun. 6, 10063 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Iwai, H., Kato-Miyazawa, M., Kirikae, T. & Miyoshi-Akiyama, T. CASTB (the comprehensive analysis server for the Mycobacterium tuberculosis complex): a publicly accessible web server for epidemiological analyses, drug-resistance prediction and phylogenetic comparison of clinical isolates. Tuberculosis 95, 843–844 (2015).

    PubMed  Google Scholar 

  49. Steiner, A., Stucki, D., Coscolla, M., Borrell, S. & Gagneux, S. KvarQ: targeted and direct variant calling from fastq reads of bacterial genomes. BMC Genomics 15, 881 (2014).

    PubMed  PubMed Central  Google Scholar 

  50. Farhat, M. et al. genTB: translational genomics of tuberculosis. genTB https://gentb.hms.harvard.edu (2015).

  51. Schleusener, V., Köser, C. U., Beckert, P., Niemann, S. & Feuerriegel, S. Mycobacterium tuberculosis resistance prediction and lineage classification from genome sequencing: comparison of automated analysis tools. Sci. Rep. 7, 46327 (2017).

    CAS  PubMed  Google Scholar 

  52. Ngo, T.-M. & Teo, Y.-Y. Genomic prediction of tuberculosis drug-resistance: benchmarking existing databases and prediction algorithms. BMC Bioinformatics 20, 68 (2019).

    PubMed  PubMed Central  Google Scholar 

  53. Phelan, J. et al. The variability and reproducibility of whole genome sequencing technology for detecting resistance to anti-tuberculous drugs. Genome Med. 8, 132 (2016).

    PubMed  PubMed Central  Google Scholar 

  54. Macedo, R. et al. Dissecting whole-genome sequencing-based online tools for predicting resistance in Mycobacterium tuberculosis: can we use them for clinical decision guidance? Tuberculosis 110, 44–51 (2018).

    CAS  PubMed  Google Scholar 

  55. Angers-Loustau, A. et al. The challenges of designing a benchmark strategy for bioinformatics pipelines in the identification of antimicrobial resistance determinants using next generation sequencing technologies. F1000Res. 7, 459 (2018).

    Google Scholar 

  56. US Food and Drug Administration. Infectious disease next generation sequencing based diagnostic devices: microbial identification and detection of antimicrobial resistance and virulence markers. FederalRegister.gov https://www.federalregister.gov/documents/2016/08/11/2016-19109/infectious-disease-next-generation-sequencing-based-diagnostic-devices-microbial-identification-and (2016).

  57. Pouseele, H. & Supply, P. Accurate whole-genome sequencing-based epidemiological surveillance of Mycobacterium tuberculosis. Methods Microbiol. 42, 359–394 (2015).

    CAS  Google Scholar 

  58. Simonyan, V., Goecks, J. & Mazumder, R. Biocompute objects — a step towards evaluation and validation of biomedical scientific computations. PDA J. Pharm. Sci. Technol. 71, 136–146 (2017).

    PubMed  Google Scholar 

  59. Alterovitz, G. et al. Enabling precision medicine via standard communication of HTS provenance, analysis, and results. PLOS Biol. 16, e3000099 (2018).

    PubMed  PubMed Central  Google Scholar 

  60. Stucki, D. et al. Standard genotyping overestimates transmission of Mycobacterium tuberculosis among immigrants in a low-incidence country. J. Clin. Microbiol. 54, 1862–1870 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Liu, Q. et al. China’s tuberculosis epidemic stems from historical expansion of four strains of Mycobacterium tuberculosis. Nat. Ecol. Evol. 2, 1982–1992 (2018).

    PubMed  PubMed Central  Google Scholar 

  62. Holt, K. E. et al. Frequent transmission of the Mycobacterium tuberculosis Beijing lineage and positive selection for the EsxW Beijing variant in Vietnam. Nat. Genet. 50, 849–856 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Coll, F. et al. Genome-wide analysis of multi- and extensively drug-resistant Mycobacterium tuberculosis. Nat. Genet. 50, 307–316 (2018).

    PubMed  Google Scholar 

  64. Farhat, M. R. et al. GWAS for quantitative resistance phenotypes in Mycobacterium tuberculosis reveals resistance genes and regulatory regions. Nat. Commun. https://doi.org/10.1038/s41467-019-10110-6 (2019).

  65. Kwong, J. C., Mccallum, N., Sintchenko, V. & Howden, B. P. Whole genome sequencing in clinical and public health microbiology. Pathology 47, 199–210 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Crisan, A., McKee, G., Munzner, T. & Gardy, J. L. Evidence-based design and evaluation of a whole genome sequencing clinical report for the reference microbiology laboratory. PeerJ 6, e4218 (2017). This article reports new standards for reporting of WGS-based TB clinical information.

    Google Scholar 

  67. Tornheim, J. A. et al. Building the framework for standardized clinical laboratory reporting of next generation sequencing data for resistance-associated mutations in Mycobacterium tuberculosis complex. Clin. Infect. Dis. https://doi.org/10.1093/cid/ciz219 (2019).

  68. Tan, T. W. et al. Advancing standards for bioinformatics activities: persistence, reproducibility, disambiguation and Minimum Information About a Bioinformatics investigation (MIABi). BMC Genomics 11 (Suppl. 4), 27 (2010).

    Google Scholar 

  69. Field, N. et al. Strengthening the Reporting of Molecular Epidemiology for Infectious Diseases (STROME-ID): an extension of the STROBE statement. Lancet Infect. Dis. 14, 341–352 (2014).

    PubMed  Google Scholar 

  70. World Health Organization. WHO’s code of conduct for open and timely sharing of pathogen genetic sequence data during outbreaks of infectious disease. WHO https://www.who.int/blueprint/what/norms-standards/gsdsharing/en/ (2019).

  71. Allard, M. W. et al. Practical value of food pathogen traceability through building a whole-genome sequencing network and database. J. Clin. Microbiol. 54, 1975–1983 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Karikari, T. K. Bioinformatics in Africa: the rise of Ghana? PLOS Comput. Biol. 11, e1004308 (2015).

    PubMed  PubMed Central  Google Scholar 

  73. Tekola-Ayele, F. & Rotimi, C. N. Translational genomics in low- and middle-income countries: opportunities and challenges. Public Health Genomics 18, 242–247 (2015).

    PubMed  PubMed Central  Google Scholar 

  74. Helmy, M., Awad, M. & Mosa, K. A. Limited resources of genome sequencing in developing countries: challenges and solutions. Appl. Transl Genom. 9, 15–19 (2016).

    PubMed  PubMed Central  Google Scholar 

  75. Satta, G., Atzeni, A. & McHugh, T. D. Mycobacterium tuberculosis and whole genome sequencing: a practical guide and online tools available for the clinical microbiologist. Clin. Microbiol. Infect. 23, 69–72 (2017).

    CAS  PubMed  Google Scholar 

  76. Kurtzer, G. M., Sochat, V. & Bauer, M. W. Singularity: scientific containers for mobility of compute. PLOS ONE 12, e0177459 (2017).

    PubMed  PubMed Central  Google Scholar 

  77. Merkel, D. Docker: lightweight Linux containers for consistent development and deployment. Linux J. 2014, 2 (2014).

    Google Scholar 

  78. Grüning, B. et al. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nat. Methods 15, 475–476 (2018).

    PubMed  Google Scholar 

  79. Jackman, S., Birol, I., Jackman, S. & Birol, I. Linuxbrew and Homebrew for cross-platform package management. F1000Res. 5, 1795 (2016).

    Google Scholar 

  80. Langille, M. G. I. & Eisen, J. A. BioTorrents: a file sharing service for scientific data. PLOS ONE 5, e10071 (2010).

    PubMed  PubMed Central  Google Scholar 

  81. Karikari, T. K., Quansah, E. & Mohamed, W. M. Y. Widening participation would be key in enhancing bioinformatics and genomics research in Africa. Appl. Transl Genom. 6, 35–41 (2015).

    PubMed  PubMed Central  Google Scholar 

  82. Bah, S. Y., Morang’a, C. M., Kengne-Ouafo, J. A., Amenga–Etego, L. & Awandare, G. A. Highlights on the application of genomics and bioinformatics in the fight against infectious diseases: challenges and opportunities in Africa. Front. Genet. 9, 575 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Zignol, M. et al. Population-based resistance of Mycobacterium tuberculosis isolates to pyrazinamide and fluoroquinolones: results from a multicountry surveillance project. Lancet Infect. Dis. 16, 1185–1192 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. Kumwenda, S. et al. Challenges facing young African scientists in their research careers: a qualitative exploratory study. Malawi Med. J. 29, 1–4 (2017).

    PubMed  PubMed Central  Google Scholar 

  85. Rabbani, F. et al. Schools of public health in low and middle-income countries: an imperative investment for improving the health of populations? BMC Public Health 16, 941 (2016).

    PubMed  PubMed Central  Google Scholar 

  86. Helb, D. et al. Rapid detection of Mycobacterium tuberculosis and rifampin resistance by use of on-demand, near-patient technology. J. Clin. Microbiol. 48, 229–237 (2010).

    CAS  PubMed  Google Scholar 

  87. Wyllie, D. H. et al. Control of artifactual variation in reported intersample relatedness during clinical use of a Mycobacterium tuberculosis sequencing pipeline. J. Clin. Microbiol. 56, e00104–18 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Wood, D. E. & Salzberg, S. L. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 15, R46 (2014).

    PubMed  PubMed Central  Google Scholar 

  89. Kim, D., Song, L., Breitwieser, F. P. & Salzberg, S. L. Centrifuge: rapid and sensitive classification of metagenomic sequences. Genome Res. 26, 1721–1729 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Médigue, C., Cole, S. T., Camus, J.-C. & Pryor, M. J. Re-annotation of the genome sequence of Mycobacterium tuberculosis H37Rv. Microbiology 148, 2967–2973 (2002).

    PubMed  Google Scholar 

  91. Periwal, V. et al. Comparative whole-genome analysis of clinical isolates reveals characteristic architecture of Mycobacterium tuberculosis pangenome. PLOS ONE 10, e0122979 (2015).

    PubMed  PubMed Central  Google Scholar 

  92. Gao, Q. et al. Gene expression diversity among Mycobacterium tuberculosis clinical isolates. Microbiology 151, 5–14 (2005).

    CAS  PubMed  Google Scholar 

  93. Kato-Maeda, M. et al. Comparing genomes within the species Mycobacterium tuberculosis. Genome Res. 11, 547–554 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Alland, D. et al. Role of large sequence polymorphisms (LSPs) in generating genomic diversity among clinical isolates of Mycobacterium tuberculosis and the utility of LSPs in phylogenetic analysis. J. Clin. Microbiol. 45, 39–46 (2007).

    CAS  PubMed  Google Scholar 

  95. Ioerger, T. R. et al. Variation among genome sequences of H37Rv strains of Mycobacterium tuberculosis from multiple laboratories. J. Bacteriol. 192, 3645–3653 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. Lee, R. S. & Behr, M. A. Does choice matter? Reference-based alignment for molecular epidemiology of tuberculosis. J. Clin. Microbiol. 54, 1891–1895 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. Norman, A., Folkvardsen, D. B., Overballe-Petersen, S. & Lillebaek, T. Complete genome sequence of Mycobacterium tuberculosis DKC2, the predominant Danish outbreak strain. Microbiol. Resour. Announc. 8, e01554–18 (2019).

    PubMed  PubMed Central  Google Scholar 

  98. Roetzer, A. et al. Whole genome sequencing versus traditional genotyping for investigation of a Mycobacterium tuberculosis outbreak: a longitudinal molecular epidemiological study. PLOS Med. 10, e1001387 (2013).

    PubMed  PubMed Central  Google Scholar 

  99. Bainomugisa, A. et al. A complete high-quality MinION nanopore assembly of an extensively drug-resistant Mycobacterium tuberculosis Beijing lineage strain identifies novel variation in repetitive PE/PPE gene regions. Microb. Genom. 4, 256719 (2018).

    Google Scholar 

  100. Iqbal, Z., Caccamo, M., Turner, I., Flicek, P. & McVean, G. De novo assembly and genotyping of variants using colored de Bruijn graphs. Nat. Genet. 44, 226–232 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  101. Yadon, A. N. et al. A comprehensive characterization of PncA polymorphisms that confer resistance to pyrazinamide. Nat. Commun. 8, 588 (2017).

    PubMed  PubMed Central  Google Scholar 

  102. Yang, Y. et al. Machine learning for classifying tuberculosis drug-resistance from DNA sequencing data. Bioinformatics 34, 1666–1671 (2018).

    CAS  PubMed  Google Scholar 

  103. Chen, M. L. et al. Deep learning predicts tuberculosis drug resistance status from genome sequencing data. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/275628v2 (2018).

  104. Rajendran, V. & Sethumadhavan, R. Drug resistance mechanism of PncA in Mycobacterium tuberculosis. J. Biomol. Struct. Dyn. 32, 209–221 (2013).

    PubMed  Google Scholar 

  105. Kavvas, E. S. et al. Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance. Nat. Commun. 9, 4306 (2018).

    PubMed  PubMed Central  Google Scholar 

  106. Duchêne, S. et al. Genome-scale rates of evolutionary change in bacteria. Microb. Genom. 2, e000094 (2016).

    PubMed  PubMed Central  Google Scholar 

  107. Lee, R. S. et al. Reemergence and amplification of tuberculosis in the Canadian arctic. J. Infect. Dis. 211, 1905–1914 (2015).

    PubMed  Google Scholar 

  108. Clark, T. G. et al. Elucidating emergence and transmission of multidrug-resistant tuberculosis in treatment experienced patients by whole genome sequencing. PLOS ONE 8, e83012 (2013).

    PubMed  PubMed Central  Google Scholar 

  109. Guthrie, J. L. et al. Genotyping and whole-genome sequencing to identify tuberculosis transmission to pediatric patients in British Columbia, Canada, 2005–2014. J. Infect. Dis. 218, 1155–1163 (2018).

    PubMed  PubMed Central  Google Scholar 

  110. Bryant, J. M. et al. Whole-genome sequencing to establish relapse or re-infection with Mycobacterium tuberculosis: a retrospective observational study. Lancet. Respir. Med. 1, 786–792 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. Guerra-Assunção, J. A. et al. Recurrence due to relapse or reinfection with Mycobacterium tuberculosis: a whole-genome sequencing approach in a large, population-based cohort with a high HIV infection prevalence and active follow-up. J. Infect. Dis. 211, 1154–1163 (2015).

    PubMed  Google Scholar 

  112. Schürch, A. C. et al. The tempo and mode of molecular evolution of Mycobacterium tuberculosis at patient-to-patient scale. Infect. Genet. Evol. 10, 108–114 (2010).

    PubMed  Google Scholar 

  113. Lieberman, T. D. et al. Genomic diversity in autopsy samples reveals within-host dissemination of HIV-associated Mycobacterium tuberculosis. Nat. Med. 22, 1470–1474 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  114. Ford, C. B. et al. Mycobacterium tuberculosis mutation rate estimates from different lineages predict substantial differences in the emergence of drug-resistant tuberculosis. Nat. Genet. 45, 784–790 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  115. Ford, C. B. et al. Use of whole genome sequencing to estimate the mutation rate of Mycobacterium tuberculosis during latent infection. Nat. Genet. 43, 482–486 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. Hatherell, H.-A. et al. Interpreting whole genome sequencing for investigating tuberculosis transmission: a systematic review. BMC Med. 14, 21 (2016). This is a systematic review of the potential for WGS in determining transmission of MTBC strains.

    PubMed  PubMed Central  Google Scholar 

  117. Verver, S. et al. Transmission of tuberculosis in a high incidence urban community in South Africa. Int. J. Epidemiol. 33, 351–357 (2004).

    PubMed  Google Scholar 

  118. Bjorn-Mortensen, K. et al. Tracing Mycobacterium tuberculosis transmission by whole genome sequencing in a high incidence setting: a retrospective population-based study in East Greenland. Sci. Rep. 6, 33180 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  119. Stimson, J. et al. Beyond the SNP threshold: identifying outbreak clusters using inferred transmissions. Mol. Biol. Evol. 36, 587–603 (2019).

    PubMed  Google Scholar 

  120. Biek, R., Pybus, O. G., Lloyd-Smith, J. O. & Didelot, X. Measurably evolving pathogens in the genomic era. Trends Ecol. Evol. 30, 306–313 (2015).

    PubMed  PubMed Central  Google Scholar 

  121. Campbell, F. et al. outbreaker2: a modular platform for outbreak reconstruction. BMC Bioinformatics 19, 363 (2018).

    PubMed  PubMed Central  Google Scholar 

  122. Didelot, X., Gardy, J. & Colijn, C. Bayesian inference of infectious disease transmission from whole-genome sequence data. Mol. Biol. Evol. 31, 1869–1879 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  123. Didelot, X., Fraser, C., Gardy, J. & Colijn, C. Genomic infectious disease epidemiology in partially sampled and ongoing outbreaks. Mol. Biol. Evol. 34, 997–1007 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  124. De Maio, N., Worby, C. J., Wilson, D. J. & Stoesser, N. Bayesian reconstruction of transmission within outbreaks using genomic variants. PLOS Comput. Biol. 14, e1006117 (2018).

    PubMed  PubMed Central  Google Scholar 

  125. Klinkenberg, D., Backer, J. A., Didelot, X., Colijn, C. & Wallinga, J. Simultaneous inference of phylogenetic and transmission trees in infectious disease outbreaks. PLOS Comput. Biol. 13, e1005495 (2017).

    PubMed  PubMed Central  Google Scholar 

  126. Kühnert, D. et al. Tuberculosis outbreak investigation using phylodynamic analysis. Epidemics 25, 47–53 (2018).

    PubMed  PubMed Central  Google Scholar 

  127. Eldholm, V. et al. Armed conflict and population displacement as drivers of the evolution and dispersal of Mycobacterium tuberculosis. Proc. Natl Acad. Sci. USA 113, 13881–13886 (2016).

    CAS  PubMed  Google Scholar 

  128. Streicher, E. M. et al. Mycobacterium tuberculosis population structure determines the outcome of genetics-based second-line drug resistance testing. Antimicrob. Agents Chemother. 56, 2420–2427 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  129. Folkvardsen, D. B. et al. Rifampin heteroresistance in Mycobacterium tuberculosis cultures as detected by phenotypic and genotypic drug susceptibility test methods. J. Clin. Microbiol. 51, 4220–4222 (2013).

    PubMed  PubMed Central  Google Scholar 

  130. Shamputa, I. C. et al. Mixed infection and clonal representativeness of a single sputum sample in tuberculosis patients from a penitentiary hospital in Georgia. Respir. Res. 7, 99 (2006).

    PubMed  PubMed Central  Google Scholar 

  131. Sobkowiak, B. et al. Identifying mixed Mycobacterium tuberculosis infections from whole genome sequence data. BMC Genomics 19, 613 (2018).

    PubMed  PubMed Central  Google Scholar 

  132. Gan, M., Liu, Q., Yang, C., Gao, Q. & Luo, T. Deep whole-genome sequencing to detect mixed infection of Mycobacterium tuberculosis. PLOS ONE 11, e0159029 (2016).

    PubMed  PubMed Central  Google Scholar 

  133. Votintseva, A. A. et al. Same-day diagnostic and surveillance data for tuberculosis via whole-genome sequencing of direct respiratory samples. J. Clin. Microbiol. 55, 1285–1298 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  134. Doyle, R. M. et al. Direct whole-genome sequencing of sputum accurately identifies drug-resistant Mycobacterium tuberculosis faster than MGIT culture sequencing. J. Clin. Microbiol. 56, e00666–18 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  135. Doughty, E. L., Sergeant, M. J., Adetifa, I., Antonio, M. & Pallen, M. J. Culture-independent detection and characterisation of Mycobacterium tuberculosis and M. africanum in sputum samples using shotgun metagenomics on a benchtop sequencer. PeerJ 2, e585 (2014).

    PubMed  PubMed Central  Google Scholar 

  136. Phelan, J. E. et al. Recombination in pe/ppe genes contributes to genetic variation in Mycobacterium tuberculosis lineages. BMC Genomics 17, 151 (2016).

    PubMed  PubMed Central  Google Scholar 

  137. Reisner, B. S., Gatson, A. M. & Woods, G. L. Evaluation of mycobacteria growth indicator tubes for susceptibility testing of Mycobacterium tuberculosis to isoniazid and rifampin. Diagn. Microbiol. Infect. Dis. 22, 325–329 (1995).

    CAS  PubMed  Google Scholar 

  138. Strydom, K. et al. Comparison of three commercial molecular assays for detection of rifampin and isoniazid resistance among Mycobacterium tuberculosis isolates in a high-HIV-prevalence setting. J. Clin. Microbiol. 53, 3032–3034 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  139. Nathavitharana, R. R. et al. Multicenter noninferiority evaluation of Hain GenoType MTBDRplus version 2 and Nipro NTM+MDRTB line probe assays for detection of rifampin and isoniazid resistance. J. Clin. Microbiol. 54, 1624–1630 (2016).

    PubMed  PubMed Central  Google Scholar 

  140. Mitarai, S. et al. Comprehensive multicenter evaluation of a new line probe assay kit for identification of Mycobacterium species and detection of drug-resistant Mycobacterium tuberculosis. J. Clin. Microbiol. 50, 884–890 (2012).

    PubMed  PubMed Central  Google Scholar 

  141. Hillemann, D., Rüsch-Gerdes, S. & Richter, E. Feasibility of the GenoType MTBDRsl assay for fluoroquinolone, amikacin-capreomycin, and ethambutol resistance testing of Mycobacterium tuberculosis strains and clinical specimens. J. Clin. Microbiol. 47, 1767–1772 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  142. Tagliani, E. et al. Diagnostic performance of the new version (v2.0) of GenoType MTBDR sl assay for detection of resistance to fluoroquinolones and second-line injectable drugs: a multicenter study. J. Clin. Microbiol. 53, 2961–2969 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  143. Ng, K. C. et al. Potential application of digitally linked tuberculosis diagnostics for real-time surveillance of drug-resistant tuberculosis transmission: validation and analysis of test results. JMIR Med. Inform. 6, e12 (2018).

    PubMed  PubMed Central  Google Scholar 

  144. Chakravorty, S. et al. The new Xpert MTB/RIF Ultra: improving detection of Mycobacterium tuberculosis and resistance to rifampin in an assay suitable for point-of-care testing. mBio 8, e00812–17 (2017).

    PubMed  PubMed Central  Google Scholar 

  145. Ng, K. C. S. et al. Xpert Ultra can unambiguously identify specific rifampin resistance-conferring mutations. J. Clin. Microbiol. 56, e00686–18 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  146. Molina-Moya, B. et al. Diagnostic accuracy study of multiplex PCR for detecting tuberculosis drug resistance. J. Infect. 71, 220–230 (2015).

    CAS  PubMed  Google Scholar 

  147. Hillemann, D., Haasis, C., Andres, S., Behn, T. & Kranzer, K. Validation of the FluoroType MTBDR assay for detection of rifampin and isoniazid resistance in Mycobacterium tuberculosis complex isolates. J. Clin. Microbiol. 56, e00072–18 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  148. Pang, Y. et al. Rapid diagnosis of MDR and XDR tuberculosis with the MeltPro TB assay in China. Sci. Rep. 6, 25330 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  149. Kaswa, M. K. et al. Pseudo-outbreak of pre-extensively drug-resistant (Pre-XDR) tuberculosis in Kinshasa: collateral damage caused by false detection of fluoroquinolone resistance by GenoType MTBDRsl. J. Clin. Microbiol. 52, 2876–2880 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  150. Ajileye, A. et al. Some synonymous and nonsynonymous gyrA mutations in Mycobacterium tuberculosis lead to systematic false-positive fluoroquinolone resistance results with the Hain GenoType MTBDRsl assays. Antimicrob. Agents Chemother. 61, e02169–16 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  151. Colman, R. E. et al. Detection of low-level mixed-population drug resistance in Mycobacterium tuberculosis using high fidelity amplicon sequencing. PLOS ONE 10, e0126626 (2015).

    PubMed  PubMed Central  Google Scholar 

  152. Colman, R. E. et al. Rapid drug susceptibility testing of drug-resistant Mycobacterium tuberculosis isolates directly from clinical samples by use of amplicon sequencing: a proof-of-concept study. J. Clin. Microbiol. 54, 2058–2067 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  153. Makhado, N. A. et al. Outbreak of multidrug-resistant tuberculosis in South Africa undetected by WHO-endorsed commercial tests: an observational study. Lancet Infect. Dis. 18, 1350–1359 (2018).

    PubMed  Google Scholar 

  154. Tagliani, E. et al. Culture and next-generation sequencing-based drug susceptibility testing unveil high levels of drug-resistant-TB in Djibouti: results from the first national survey. Sci. Rep. 7, 17672 (2017).

    PubMed  PubMed Central  Google Scholar 

  155. Gagneux, S. Ecology and evolution of Mycobacterium tuberculosis. Nat. Rev. Microbiol. 16, 202–213 (2018).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

C.J.M. and L.R. are also affiliated with BCCM/ITM Mycobacterial Culture Collection, Institute of Tropical Medicine, Antwerp, Belgium. J.G. is also affiliated with the BC Centre for Disease Control, Vancouver, Canada. B.O.-A. is also affiliated with the Center for Global Health Security and Diplomacy, Ottawa, Canada. M.S. is also affiliated with the University of Arizona, Tucson, AZ, USA. I.C. is also affiliated with the CIBER in Epidemiology and Public Health, Spain. C.J.M., B.O.-A., L.R. and B.C.d.J. are supported by a European Research Council grant (INTERRUPTB; no. 311725). I.C. and G.A.G. are supported by a European Research Council grant (TB-ACCELERATE; no. 638553). T.C.R. receives salary support from the not-for-profit organization Foundation for Innovative New Diagnostics (the terms of this arrangement have been reviewed and approved by the University of California, San Diego). T.M.W. is an NIHR Academic Clinical Lecturer. J.L.G. and J.G. receive funding from the University of British Columbia, Vancouver, Canada. T.A.K., C.U., V.D. and S.N. receive funding from the German Center for Infection Research (DZIF) and are funded by the Deutsche Forschungsgemeinschaft (German Research Foundation) under Germany’s Excellence Strategy (EXC 22167–390884018). L.V., T.H.H. and A.V.R. are funded by FWO Odysseus G0F8316N. M.R.F. is supported by the US National Institutes of Health BD2K K01 (MRF ES026835). P.S. is supported by the Agence Nationale de la Recherche (ANR-16-CE35-0009).

Reviewer information

Nature Reviews Microbiology thanks T. McHugh, V. Sintchenko, and other anonymous reviewer(s), for their contribution to the peer review of this work.

Author information

Authors and Affiliations

  1. Unit of Mycobacteriology, Department of Biomedical Sciences, Institute of Tropical Medicine, Antwerp, Belgium

    Conor J. Meehan, Bouke C. de Jong & Leen Rigouts

  2. Institute of Biomedicine of Valencia, CSIC, Valencia, Spain

    Galo A. Goig & Inaki Comas

  3. Molecular and Experimental Mycobacteriology, Priority Area Infections, Research Center Borstel, Borstel, Germany

    Thomas A. Kohl, Viola Dreyer, Christian Utpatel & Stefan Niemann

  4. German Center for Infection Research, Partner Site Hamburg–Lübeck–Borstel–Riems, Borstel, Germany

    Thomas A. Kohl, Viola Dreyer, Christian Utpatel & Stefan Niemann

  5. Tuberculosis Omics Research Consortium, Department of Epidemiology and Social Medicine, Institute of Global Health, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium

    Lennert Verboven, Margaretha de Vos, Tim H. Heupink & Annelies Van Rie

  6. DST–NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa

    Anzaan Dippenaar & Robin M. Warren

  7. Critical Path Institute, Tucson, AZ, USA

    Matthew Ezewudo & Marco Schito

  8. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Maha R. Farhat

  9. Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA, USA

    Maha R. Farhat

  10. University of British Columbia, Vancouver, Canada

    Jennifer L. Guthrie & Jennifer Gardy

  11. Adrem Data Laboratory, Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium

    Kris Laukens

  12. Emerging Bacterial Pathogens Unit, Division of Immunology, Transplantation and Infectious Diseases, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Paolo Miotto, Andrea M. Cabibbe, Elisa Tagliani & Daniela M. Cirillo

  13. Center for Global Health Security and Diplomacy, Ottawa, Canada

    Boatema Ofori-Anyinam

  14. Food and Drugs Authority, Accra, Ghana

    Boatema Ofori-Anyinam

  15. University of Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, Lille, France

    Philip Supply

  16. U1019 — UMR 8204, Center for Infection and Immunity of Lille, Lille, France

    Philip Supply

  17. Foundation for Innovative New Diagnostics, Geneva, Switzerland

    Anita Suresh & Timothy C. Rodwell

  18. National Tuberculosis Reference Laboratory, Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, Netherlands

    Dick van Soolingen

  19. National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China

    Yang Zhou & Yanlin Zhao

  20. Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK

    Philip M. Ashton

  21. Swiss Tropical and Public Health Institute, Basel, Switzerland

    Daniela Brites, Fabrizio Menardo, Sebastien Gagneux & Chloé Loiseau

  22. University of Basel, Basel, Switzerland

    Daniela Brites, Fabrizio Menardo, Sebastien Gagneux & Chloé Loiseau

  23. Key Laboratory of Medical Molecular Virology, Ministry of Education and Health, School of Basic Medical Sciences, Fudan University, Shanghai, China

    Qian Gao & Qingyun Liu

  24. Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK

    Timothy M. Walker

  25. Global Tuberculosis Programme, World Health Organization, Geneva, Switzerland

    Matteo Zignol

Authors
  1. Conor J. Meehan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Galo A. Goig
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thomas A. Kohl
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lennert Verboven
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Anzaan Dippenaar
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Matthew Ezewudo
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Maha R. Farhat
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jennifer L. Guthrie
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Kris Laukens
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Paolo Miotto
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Boatema Ofori-Anyinam
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Viola Dreyer
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Philip Supply
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Anita Suresh
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Christian Utpatel
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Dick van Soolingen
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Yang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Philip M. Ashton
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Daniela Brites
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Andrea M. Cabibbe
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Bouke C. de Jong
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Margaretha de Vos
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Fabrizio Menardo
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Sebastien Gagneux
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Qian Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Tim H. Heupink
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Qingyun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Chloé Loiseau
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Leen Rigouts
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Timothy C. Rodwell
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. Elisa Tagliani
    View author publications

    You can also search for this author in PubMed Google Scholar

  32. Timothy M. Walker
    View author publications

    You can also search for this author in PubMed Google Scholar

  33. Robin M. Warren
    View author publications

    You can also search for this author in PubMed Google Scholar

  34. Yanlin Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  35. Matteo Zignol
    View author publications

    You can also search for this author in PubMed Google Scholar

  36. Marco Schito
    View author publications

    You can also search for this author in PubMed Google Scholar

  37. Jennifer Gardy
    View author publications

    You can also search for this author in PubMed Google Scholar

  38. Daniela M. Cirillo
    View author publications

    You can also search for this author in PubMed Google Scholar

  39. Stefan Niemann
    View author publications

    You can also search for this author in PubMed Google Scholar

  40. Inaki Comas
    View author publications

    You can also search for this author in PubMed Google Scholar

  41. Annelies Van Rie
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

C.J.M., G.A.G., T.A.K., L.V., A.D., M.E., M.R.F., J.L.G., K.L., P.M., B.O.-A., V.D., P.S., A.S., C.U., D.v.S., Y.Z., M.S., J.G., D.M.C., S.N., I.C. and A.V.R. researched the data for the article. C.J.M., G.A.G., T.A.K., L.V., A.D., M.E., M.R.F., J.L.G., K.L., P.M., B.O.-A., V.D., P.S., A.S., C.U., D.v.S. Y.Z., M.d.V., S.G., T.H.H., L.R., E.T., T.M.W., R.M.W., M.S., J.G., D.M.C., S.N., I.C. and A.V.R. substantially contributed to the discussion of the content. C.J.M., G.A.G., T.A.K., L.V., A.D., M.E., M.R.F., J.L.G., K.L., P.M., B.O.-A., V.D., P.S., A.S., C.U., D.v.S., Y.Z., M.S., J.G., D.M.C., S.N., I.C. and A.V.R. wrote the article. All authors reviewed and edited the manuscript before submission.

Corresponding authors

Correspondence to Inaki Comas or Annelies Van Rie.

Ethics declarations

Competing interests

P.S. was a consultant for Genoscreen. All other authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Human, Heredity and Health in Africa Consortium: https://h3abionet.org

ReSeqTB: http://www.reseqtb.org

TORCH consortium: https://torch-consortium.com/vliruos

ERLTB-Net: https://ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/erltb-net

Supplementary information

Glossary

Mycobacterium tuberculosis complex

(MTBC). The genetically related group of organisms within the genus Mycobacterium that cause tuberculosis in humans or animals.

Drug susceptibility testing

(DST). A procedure to determine if clinical isolates are resistant to antibiotics either by testing the inhibition in culture (phenotypic DST) or by identifying drug resistance-associated mutations (genotypic DST).

Source investigation

The first case in a group of related individuals that transmitted the disease. Usually, identified during the development of an epidemiological investigation.

Löwenstein–Jensen

A selective culture solid medium commonly used to isolate Mycobacterium tuberculosis complex strains.

Mycobacteria Growth Indicator Tube

A tube that contains mycobacteria-selective culture liquid medium and is usually coupled to an automated instrument to read the results.

PE and PPE gene families

Families of genes that encode virulence factors in Mycobacterium tuberculosis complex strains. They have signature (proline)–proline–glutamate ((P)PE) motifs at their amino terminus.

Core genome MLST

A scheme that converts genome-wide SNP data into an allele-numbering system using a preselected set of core genes.

Whole genome MLST

A scheme that converts genome-wide SNP data into an allele-numbering system using a preselected set of core genes and additional accessory genes.

Contact tracing

The identification of possible contacts that interacted with an infected person (index case), often through questionnaires and interviews.

Mycobacterial interspersed repetitive unit variable-number tandem repeat

Mycobacterium tuberculosis complex (MTBC)-specific variable tandem repeat locus used to genotype MTBC strains.

WGS pipelines

The bioinformatics section of the whole genome sequencing workflow, starting from raw sequencing files through to SNP calling and analyses.

WGS workflows

All steps involved (from culturing to SNP calling and analyses) for whole genome sequencing of an isolate.

BioCompute Object

(BCO). A framework for standardized reporting of computational parameters for a whole genome sequencing pipeline.

Spoligotyping

A PCR-based approach based on the amplification of spacers in the CRISPR region of the Mycobacterium tuberculosis complex (MTBC). It is used for genotyping MTBC strains.

Effective reproduction number

The average number of secondary cases per infectious case.

Microevolution

Genetic changes within a population, resulting in separate subpopulations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Meehan, C.J., Goig, G.A., Kohl, T.A. et al. Whole genome sequencing of Mycobacterium tuberculosis: current standards and open issues. Nat Rev Microbiol 17, 533–545 (2019). https://doi.org/10.1038/s41579-019-0214-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41579-019-0214-5

This article is cited by

Search

Advanced search

Quick links

Nature Briefing Microbiology

Sign up for the Nature Briefing: Microbiology newsletter — what matters in microbiology research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Microbiology