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.

Transforming clinical microbiology with bacterial genome sequencing

Nature Reviews Genetics volume 13, pages 601–612 (2012)Cite this article

Subjects

Key Points

  • Whole-genome sequencing of bacterial isolates is becoming more and more widespread, paving the way for a transformation of many current procedures in clinical microbiology.

  • Identifying the species of an isolate is currently a complex laboratory process. A few methods have already been proposed for doing this using the genome sequence, which could result in a re-evaluation of the bacterial species concept.

  • Testing antibiotic resistance properties is often crucial for determining the appropriate treatment. As resistance is encoded by specific genes, this susceptibility assessment could be performed in silico using the genome sequence.

  • The same is true for determining virulence properties of a strain, but with the difference that correlations between the genotype and phenotype is often more complex (involving several genes) than for resistance. However, association-mapping techniques can be used to detect such complex correlations, leading to a better understanding of pathogenicity.

  • Several studies have already demonstrated the great potential of whole-genome sequencing in epidemiological investigations. These have so far been carried out after the course of an outbreak. In the future, with improving technology, this could be carried on an ongoing basis to detect epidemiological risks as they arise and react accordingly.

  • Bacteria culturing is a pre-requirement even for whole-genome sequencing as it is currently carried out. This represents an important bottleneck as some bacteria are slow-growing and others cannot be cultured. Metagenomics approaches could provide a solution to this long-standing issue.

Abstract

Whole-genome sequencing of bacteria has recently emerged as a cost-effective and convenient approach for addressing many microbiological questions. Here, we review the current status of clinical microbiology and how it has already begun to be transformed by using next-generation sequencing. We focus on three essential tasks: identifying the species of an isolate, testing its properties, such as resistance to antibiotics and virulence, and monitoring the emergence and spread of bacterial pathogens. We predict that the application of next-generation sequencing will soon be sufficiently fast, accurate and cheap to be used in routine clinical microbiology practice, where it could replace many complex current techniques with a single, more efficient workflow.

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

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

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

Figure 1: Principles of current processing of bacterial pathogens.
Figure 2: Hypothetical workflow based on whole-genome sequencing.

References

  1. Burlage, R. S. Principles of Public Health Microbiology (Jones & Bartlett Learning, 2012).

    Google Scholar 

  2. Relman, D. A. Microbial genomics and infectious diseases. N. Engl. J. Med. 365, 347–357 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Parkhill, J. & Wren, B. W. Bacterial epidemiology and biology — lessons from genome sequencing. Genome Biol. 12, 230 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Mandell, G. L., Bennett, J. E. & Dolin, R. Mandell, Douglas, and Bennett's Principles and Practice of Infectious Diseases (Churchill Livingstone/Elsevier, 2010).

    Google Scholar 

  5. Murray, P. R., Rosenthal, K. S. & Pfaller, M. A. Medical Microbiology (Mosby/Elsevier, 2009).

    Google Scholar 

  6. Warrell, D. A., Cox, T. M. & Firth, J. D. Oxford Textbook of Medicine (Oxford Univ. Press, 2010).

    Book  Google Scholar 

  7. Janda, J. M. & Abbott, S. L. 16S rRNA gene sequencing for bacterial identification in the diagnostic laboratory: pluses, perils, and pitfalls. J. Clin. Microbiol. 45, 2761–2764 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Clarridge, J. E. Impact of 16S rRNA gene sequence analysis for identification of bacteria on clinical microbiology and infectious diseases. Clin. Microbiol. Rev. 17, 840–862, (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Seng, P. et al. Ongoing revolution in bacteriology: routine identification of bacteria by matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Clin. Infect. Dis. 49, 543–551 (2009).

    Article  CAS  PubMed  Google Scholar 

  10. van Veen, S. Q., Claas, E. C. & Kuijper, E. J. High-throughput identification of bacteria and yeast by matrix-assisted laser desorption ionization-time of flight mass spectrometry in conventional medical microbiology laboratories. J. Clin. Microbiol. 48, 900–907 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Cherkaoui, A. et al. Comparison of two matrix-assisted laser desorption ionization-time of flight mass spectrometry methods with conventional phenotypic identification for routine identification of bacteria to the species level. J. Clin. Microbiol. 48, 1169–1175 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Gaillot, O. et al. Cost-effectiveness of switch to matrix-assisted laser desorption ionization-time of flight mass spectrometry for routine bacterial identification. J. Clin. Microbiol. 49, 4412 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Stevenson, L. G., Drake, S. K. & Murray, P. R. Rapid identification of bacteria in positive blood culture broths by matrix-assisted laser desorption ionization-time of flight mass spectrometry. J. Clin. Microbiol. 48, 444–447 (2010).

    Article  CAS  PubMed  Google Scholar 

  14. Shitikov, E. et al. Mass spectrometry based methods for the discrimination and typing of mycobacteria. Infect. Genet. Evol. 12, 838–845 (2012).

    Article  CAS  PubMed  Google Scholar 

  15. Lorian, V. Antibiotics in Laboratory Medicine (Lippincott Williams & Wilkins, 2005).

    Google Scholar 

  16. Wain, J. et al. Quinolone-resistant Salmonella typhi in Viet Nam: molecular basis of resistance and clinical response to treatment. Clin. Infect. Dis. 25, 1404–1410 (1997).

    Article  CAS  PubMed  Google Scholar 

  17. Cavaco, L. M. Hasman, H., Xia, S. & Aarestrup, F. M. qnrD, a novel gene conferring transferable quinolone resistance in Salmonella enterica serovar Kentucky and Bovismorbificans strains of human origin. Antimicrob. Agents Chemother. 53, 603–608 (2009).

    Article  CAS  Google Scholar 

  18. Bode, L. G., van Wunnik, P., Vaessen, N., Savelkoul, P. H. & Smeets, L. C. Rapid detection of methicillin-resistant Staphylococcus aureus in screening samples by relative quantification between the mecA gene and the SA442 gene. J. Microbiol. Methods 89, 129–132 (2012).

    Article  CAS  PubMed  Google Scholar 

  19. Cosgrove, S. E. et al. Comparison of mortality associated with methicillin-resistant and methicillin-susceptible Staphylococcus aureus bacteremia: a meta-analysis. Clin. Infect. Dis. 36, 53–59 (2003).

    Article  PubMed  Google Scholar 

  20. Barnard, M., Albert, H., Coetzee, G., O'Brien, R. & Bosman, M. E. Rapid molecular screening for multidrug-resistant tuberculosis in a high-volume public health laboratory in South Africa. Am. J. Respir. Crit. Care Med. 177, 787–792 (2008).

    Article  PubMed  Google Scholar 

  21. Knetsch, C. W. et al. Comparison of real-time PCR techniques to cytotoxigenic culture methods for diagnosing Clostridium difficile infection. J. Clin. Microbiol. 49, 227–231 (2011).

    Article  CAS  PubMed  Google Scholar 

  22. Lindstedt, B. A. Multiple-locus variable number tandem repeats analysis for genetic fingerprinting of pathogenic bacteria. Electrophoresis 26, 2567–2582 (2005).

    Article  CAS  PubMed  Google Scholar 

  23. Goering, R. V. Pulsed field gel electrophoresis: a review of application and interpretation in the molecular epidemiology of infectious disease. Infect. Genet. Evol. 10, 866–875 (2010).

    Article  CAS  PubMed  Google Scholar 

  24. Maiden, M. C. Multilocus sequence typing of bacteria. Annu. Rev. Microbiol. 60, 561–588 (2006).

    Article  CAS  PubMed  Google Scholar 

  25. Medini, D., Donati, C., Tettelin, H., Masignani, V. & Rappuoli, R. The microbial pan-genome. Curr. Opin. Genet. Dev. 15, 589–594 (2005).

    Article  CAS  PubMed  Google Scholar 

  26. Jolley, K. A. et al. Ribosomal multi-locus sequence typing: universal characterisation of bacteria from domain to strain. Microbiology 158, 1005–1015 (2012). This is a database system for whole genomes that provides a smooth transition for users from working with MLST to working with genomes.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Jolley, K. A. & Maiden, M. C. BIGSdb: scalable analysis of bacterial genome variation at the population level. BMC Bioinformatics 11, 595 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Larsen, M. V. et al. Multilocus sequence typing of total-genome-sequenced bacteria. J. Clin. Microbiol. 50, 1355–1361 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Hanage, W. P. et al. Using multilocus sequence data to define the pneumococcus. J. Bacteriol. 187, 6223–6230 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Sheppard, S. K., McCarthy, N. D., Falush, D. & Maiden, M. C. Convergence of Campylobacter species: implications for bacterial evolution. Science 320, 237–239 (2008).

    Article  CAS  PubMed  Google Scholar 

  32. Priest, F. G., Barker, M., Baillie, L. W., Holmes, E. C. & Maiden, M. C. Population structure and evolution of the Bacillus cereus group. J. Bacteriol. 186, 7959–7970 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Hanage, W. P., Fraser, C. & Spratt, B. G. Fuzzy species among recombinogenic bacteria. BMC Biology 3, 6 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Konstantinidis, K. T. & Tiedje, J. M. Genomic insights that advance the species definition for prokaryotes. Proc. Natl Acad. Sci. USA 102, 2567–2572 (2005). This was the first description of computational criteria to define bacterial species on the basis of whole-genome sequencing.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Goris, J. et al. DNA-DNA hybridization values and their relationship to whole-genome sequence similarities. Int. J. Syst. Evol. Microbiol. 57, 81–91 (2007).

    Article  CAS  PubMed  Google Scholar 

  36. McAdam, P. R., Holmes, A., Templeton, K. E. & Fitzgerald, J. R. Adaptive evolution of Staphylococcus aureus during chronic endobronchial infection of a cystic fibrosis patient. PLoS ONE 6, e24301 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Mutreja, A. et al. Evidence for several waves of global transmission in the seventh cholera pandemic. Nature 477, 462–465 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Lieberman, T. D. et al. Parallel bacterial evolution within multiple patients identifies candidate pathogenicity genes. Nature Genet. 43, 1275–1280 (2011).

    Article  CAS  PubMed  Google Scholar 

  39. Wolk, D. M. et al. Multicenter evaluation of the Cepheid Xpert methicillin-resistant Staphylococcus aureus (MRSA) test as a rapid screening method for detection of MRSA in nares. J. Clin. Microbiol. 47, 758–764 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Hilleman, D. et al. Use of the genotype MTBDR assay for rapid detection of rifampin and isoniazid resistance in Mycobacterium tuberculosis complex isolates. J. Clin. Microbiol. 43, 3699–3703 (2005).

    Article  CAS  Google Scholar 

  41. Livermore, D. M. β-lactamases in laboratory and clinical resistance. Clin. Microbiol. Rev. 8, 557–584 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Boehme, C. C. et al. Rapid molecular detection of tuberculosis and rifampin resistance. N. Engl. J. Med. 363, 1005–1015 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Caroff, N., Espaze, E., Gautreau, D., Richet, H. & Reynaud, A. Analysis of the effects of -42 and -32 ampC promoter mutations in clinical isolates of Escherichia coli hyperproducing ampC. J. Antimicrob. Chemother. 45, 783–788 (2000).

    Article  CAS  PubMed  Google Scholar 

  44. Devasia, R. et al. High proportion of fluoroquinolone-resistant Mycobacterium tuberculosis isolates with novel gyrase polymorphisms and a gyrA region associated with fluoroquinolone susceptibility. J. Clin. Microbiol. 50, 1390–1396 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Walsh, T. R., Weeks, J., Livermore, D. M. & Toleman, M. A. Dissemination of NDM-1 positive bacteria in the New Delhi environment and its implications for human health: an environmental point prevalence study. Lancet Infect. Dis. 11, 355–362 (2011).

    Article  PubMed  Google Scholar 

  46. Bolan, G. A., Sparling, P. F. & Wasserheit, J. N. The emerging threat of untreatable gonococcal infection. N. Engl. J. Med. 366, 485–487 (2012).

    Article  PubMed  Google Scholar 

  47. Bille, E. et al. A chromosomally integrated bacteriophage in invasive meningococci. J. Exp. Med. 201, 1905–1913 (2005). This was the first example of an association-mapping study to determine virulence factors in N. meningitidis.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Young, B. C. et al. Evolutionary dynamics of Staphylococcus aureus during progression from carriage to disease. Proc. Natl Acad. Sci. USA 109, 4550–4555 (2012). This was a detailed investigation of S. aureus within-host genomic diversification over a period of time that revealed probable evolution towards increased virulence.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Rohde, H. et al. Open-source genomic analysis of Shiga-toxin-producing E. coli O104:H4. N. Engl. J. Med. 365, 718–724 (2011).

    CAS  PubMed  Google Scholar 

  50. Rasko, D. A. et al. Origins of the E. coli strain causing an outbreak of hemolytic-uremic syndrome in Germany. N. Engl. J. Med. 365, 709–717 (2011). This was an epidemiological investigation based on whole-genome sequencing for the 2011 outbreak of E. coli in Germany.

    CAS  PubMed  Google Scholar 

  51. Rothberg, J. M. et al. An integrated semiconductor device enabling non-optical genome sequencing. Nature 475, 348–352 (2011).

    Article  CAS  PubMed  Google Scholar 

  52. Suerbaum, S. No tech gaps in E. coli outbreak. Nature 476, 33 (2011).

    Article  CAS  PubMed  Google Scholar 

  53. Mellmann, A. et al. Prospective genomic characterization of the German enterohemorrhagic Escherichia coli O104:H4 outbreak by rapid next generation sequencing technology. PLoS ONE 6, e22751 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Korlach, J. et al. Real-time DNA sequencing from single polymerase molecules. Methods Enzymol. 472, 431–455 (2010).

    Article  CAS  PubMed  Google Scholar 

  55. Chin, C. S. et al. The origin of the Haitian cholera outbreak strain. N. Engl. J. Med. 364, 33–42 (2011). This is a study of the origin of the ongoing Haitian outbreak of Vibrio cholerae based on whole-genome comparison with other strains.

    Article  CAS  PubMed  Google Scholar 

  56. Harris, S. R. et al. Whole-genome analysis of diverse Chlamydia trachomatis strains identifies phylogenetic relationships masked by current clinical typing. Nature Genet. 44, 413–419 (2012). This is an example of how current typing techniques can be misleading compared to whole-genome sequencing.

    Article  CAS  PubMed  Google Scholar 

  57. Harris, S. R. et al. Evolution of MRSA during hospital transmission and intercontinental spread. Science 327, 469–474 (2010). This was one of the first studies to demonstrate the great potential of whole-genome sequencing to reconstruct person-to-person transmission pathways within a hospital.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Golubchik, T. et al. Pneumococcal genome sequencing tracks a vaccine escape variant formed through a multi-fragment recombination event. Nature Genet. 44, 352–355 (2012). This is an example of the great evolutionary potential that highly recombinogenic bacteria have in order to escape epidemiological interventions.

    Article  CAS  PubMed  Google Scholar 

  59. Truman, R. W. et al. Probable zoonotic leprosy in the southern United States. N. Engl. J. Med. 364, 1626–1633 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Monot, M. et al. Comparative genomic and phylogeographic analysis of Mycobacterium leprae. Nature Genet. 41, 1282–1289 (2009).

    Article  CAS  PubMed  Google Scholar 

  61. Morelli, G. et al. Yersinia pestis genome sequencing identifies patterns of global phylogenetic diversity. Nature Genet. 42, 1140–1143 (2010).

    Article  CAS  PubMed  Google Scholar 

  62. Koser, C. U. et al. Rapid whole-genome sequencing for investigation of a neonatal MRSA outbreak. N. Engl. J. Med. 366, 2267–2275 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Eyre, D. W. et al. A pilot study of rapid benchtop sequencing of Staphylococcus aureus and Clostridium difficile for outbreak detection and surveillance. BMJ Open 2, e001124 (2012). In this paper, a demonstration is provided of the usefulness of bench-top sequencing to answer epidemiological questions in near real-time.

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  65. Cardoso Oelemann, M. et al. The forest behind the tree: phylogenetic exploration of a dominant Mycobacterium tuberculosis strain lineage from a high tuberculosis burden country. PLoS ONE 6, e18256 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Aanensen, D. M., Huntley, D. M., Feil, E. J., al- Own, F. & Spratt, B. G. EpiCollect: linking smartphones to web applications for epidemiology, ecology and community data collection. PLoS ONE 4, e6968 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Cho, I. & Blaser, M. J. The human microbiome: at the interface of health and disease. Nature Rev. Genet. 13, 260–270 (2012).

    Article  CAS  PubMed  Google Scholar 

  68. Kuczynski, J. et al. Experimental and analytical tools for studying the human microbiome. Nature Rev. Genet. 13, 47–58 (2012).

    Article  CAS  Google Scholar 

  69. Margulies, M. et al. Genome sequencing in microfabricated high-density picolitre reactors. Nature 437, 376–380 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Loman, N. J. et al. Performance comparison of benchtop high-throughput sequencing platforms. Nature Biotech. 30, 562 (2012).

    Article  CAS  Google Scholar 

  71. Check Hayden, E. Nanopore genome sequencer makes its debut. Nature 17 Feb 2012 (doi:10.1038/nature.2012.10051).

  72. Didelot, X. in Bacterial Population Genetics in Infectious Disease 37–60 (John Wiley & Sons, 2010).

    Book  Google Scholar 

  73. Drummond, A. J. & Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7, 214 (2007).

    Google Scholar 

  74. Rodrigo, A. G. et al. Coalescent estimates of HIV-1 generation time in vivo. Proc. Natl Acad. Sci. USA 96, 2187–2191 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Drummond, A. J., Rambaut, A., Shapiro, B. & Pybus, O. G. Bayesian coalescent inference of past population dynamics from molecular sequences. Mol. Biol. Evol. 22, 1185–1192 (2005).

    Article  CAS  PubMed  Google Scholar 

  76. Drummond, A. J., Ho, S. Y., Phillips, M. J. & Rambaut, A. Relaxed phylogenetics and dating with confidence. PLoS Biol. 4, e88 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Lemey, P., Rambaut, A., Drummond, A. J. & Suchard, M. A. Bayesian phylogeography finds its roots. PLoS Comput. Biol. 5, e1000520 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Suchard, M. A. & Rambaut, A. Many-core algorithms for statistical phylogenetics. Bioinformatics 25, 1370–1376 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Vos, M. & Didelot, X. A comparison of homologous recombination rates in bacteria and archaea. ISME J. 3, 199–208 (2009).

    Article  CAS  PubMed  Google Scholar 

  80. Schierup, M. H. & Hein, J. Consequences of recombination on traditional phylogenetic analysis. Genetics 156, 879–891 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Didelot, X., Achtman, M., Parkhill, J., Thomson, N. R. & Falush, D. A bimodal pattern of relatedness between the Salmonella Paratyphi A and Typhi genomes: convergence or divergence by homologous recombination? Genome Res. 17, 61–68 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Croucher, N. J. et al. Rapid pneumococcal evolution in response to clinical interventions. Science 331, 430–434 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Didelot, X. & Falush, D. Inference of bacterial microevolution using multilocus sequence data. Genetics 175, 1251–1266 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Didelot, X., Lawson, D., Darling, A. & Falush, D. Inference of homologous recombination in bacteria using whole-genome sequences. Genetics 186, 1435–1449 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Pybus, O. G. & Rambaut, A. Evolutionary analysis of the dynamics of viral infectious disease. Nature Rev. Genet. 10, 540–550 (2009).

    Article  CAS  PubMed  Google Scholar 

  86. Cottam, E. M. et al. Transmission pathways of foot-and-mouth disease virus in the United Kingdom in 2007. PLoS Pathog. 4, e1000050 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  88. Kennemann, L. et al. Helicobacter pylori genome evolution during human infection. Proc. Natl Acad. Sci. USA 108, 5033–5038 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Reeves, P. R. et al. Rates of mutation and host transmission for an Escherichia coli clone over 3 years. PLoS ONE 6, e26907 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Nielsen, R., Paul, J. S., Albrechtsen, A. & Song, Y. S. Genotype and SNP calling from next-generation sequencing data. Nature Rev. Genet. 12, 443–451 (2011).

    Article  CAS  PubMed  Google Scholar 

  91. Li, H., Ruan, J. & Durbin, R. Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res. 18, 1851–1858 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Lunter, G. & Goodson, M. Stampy: a statistical algorithm for sensitive and fast mapping of Illumina sequence reads. Genome Res. 21, 936–939 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Chaisson, M. J. & Pevzner, P. A. Short read fragment assembly of bacterial genomes. Genome Res. 18, 324–330 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Zerbino, D. R. & Birney, E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 18, 821–829 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. Darling, A. E., Miklos, I. & Ragan, M. A. Dynamics of genome rearrangement in bacterial populations. PLoS Genet. 4, e1000128 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Darling, A. C., Mau, B., Blattner, F. R. & Perna, N. T. Mauve: multiple alignment of conserved genomic sequence with rearrangements. Genome Res. 14, 1394–1403 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Darling, A. E. Mau, B. & Perna, N. T. progressive Mauve: multiple genome alignment with gene gain, loss and rearrangement. PLoS ONE 5, e11147 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Yarza, P. et al. The all-species living tree project: a 16S rRNA-based phylogenetic tree of all sequenced type strains. Syst. Appl. Microbiol. 31, 241–250 (2008).

    Article  CAS  PubMed  Google Scholar 

  100. Liu, B. & Pop, M. ARDB—Antibiotic Resistance Genes Database. Nucleic Acids Res. 37, D443–D447 (2009).

    Article  CAS  PubMed  Google Scholar 

  101. Wu, H.-J., Wang, A. H. J. & Jennings, M. P. Discovery of virulence factors of pathogenic bacteria. Curr. Opin. Chem. Biol. 12, 93–101 (2008).

    Article  CAS  PubMed  Google Scholar 

  102. Delcher, A. L., Bratke, K. A., Powers, E. C. & Salzberg, S. L. Identifying bacterial genes and endosymbiont DNA with Glimmer. Bioinformatics 23, 673–679 (2007).

    Article  CAS  PubMed  Google Scholar 

  103. Chaudhuri, R. R. et al. xBASE2: a comprehensive resource for comparative bacterial genomics. Nucleic Acids Res. 36, D543–546 (2008).

    Article  CAS  PubMed  Google Scholar 

  104. Stewart, A. C., Osborne, B. & Read, T. D. DIYA: a bacterial annotation pipeline for any genomics lab. Bioinformatics 25, 962–963 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

D.W.C. and T.A.E.P. are part-funded by the UK National Institute for Health Research (NIHR) Oxford Biomedical Research Centre and are NIHR Senior Investigators. X.D., D.J.W. and R.B. are funded by the UK Clinical Reasearch Collaboration. We thank J. Paul for helpful advice and suggestions.

Author information

Authors and Affiliations

  1. Department of Statistics, University of Oxford, 1 South Parks Road, Oxford, OX1 3TG, UK

    Xavier Didelot & Rory Bowden

  2. Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, UK

    Rory Bowden & Daniel J. Wilson

  3. NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, OX3 9DU, UK

    Rory Bowden, Tim E. A. Peto & Derrick W. Crook

  4. Nuffield Department of Clinical Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DU, UK

    Daniel J. Wilson, Tim E. A. Peto & Derrick W. Crook

Authors
  1. Xavier Didelot
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rory Bowden
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniel J. Wilson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tim E. A. Peto
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Derrick W. Crook
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Derrick W. Crook.

Ethics declarations

Competing interests

Derrick W. Crook and Tim E. A. Peto are part-funded by the UK National Institute for Health Research (NIHR) Oxford Biomedical Research Centre and are NIHR Senior Investigators. Xavier Didelot, Daniel J. Wilson and Rory Bowden are funded by the UK Clinical Reasearch Collaboration. This funding supports translational research for implementing genomic sequencing into clinical microbiology practice.

Related links

Related links

FURTHER INFORMATION

Modernising Medical Microbiology project

Nature Reviews Genetics Series on Applications of nextgeneration sequencing

Nature Reviews Genetics Series on Translational genetics

Glossary

Escherichia coli

A common inhabitant of the guts of many animals, but some strains can cause serious food poisoning, as reminded by the 2011 outbreak in Germany.

Mycobacterium tuberculosis

The causative agent of tuberculosis, it infects approximately one-third of the human population and claims over one million lives per year, making it the most deadly bacterial pathogen of humans.

Staphylococcus aureus

Found as a harmless colonizer of the skin in ~20% of the human population; it can cause life-threatening symptoms and be resistant to some antibiotics (for example, methicillin-resistant S. aureus (MRSA)).

Staphylococcus epidermidis

A normal part of the human skin flora; it can become pathogenic if introduced into deeper tissues following surgery.

16S ribosomal RNA gene

The 16S ribosomal RNA genes are transcribed into the 16S ribosomal RNA molecule, which is a major component of the bacterial small ribosomal subunit. The strong sequence conservation of this molecule makes it ideal for detecting large evolutionary distances between two organisms.

Minimum inhibitory concentration

(MIC). The minimum concentration of an antibiotic that is sufficient to inhibit growth of a bacterial culture.

Clostridium difficile

A leading cause of diarrhoea and more severe conditions, especially in the elderly following disruption of the normal gut flora through the use of antibiotics.

Serotyping

In this context, the classification of bacteria on the basis of their surface antigens.

Haemophilus influenzae

Responsible for a wide range of clinical diseases (but not influenza, as originally thought and as the name might still suggest) especially in young children, it was the first free living organism to have its genome completely sequenced.

Streptococcus pneumoniae

A major cause of pneumonia, it can also cause various other severe conditions and has recently developed resistance to some antibiotics. It causes ~1 million deaths per year, mostly in children.

Neisseria meningitidis

A commensal inhabitant of the nasopharynx in up to one-quarter of the human population; it occasionally gets into the blood, resulting in >100,000 deaths per year through meningitis and septicaemia.

Campylobacter jejuni

A natural colonizer of the digestive tracts of many birds and cattle; it is typically transmitted to humans by ingestion of contaminated food and results in severe diarrhoeal diseases.

Vibrio cholerae

The agent of cholera is transmitted via contaminated waters and can cause death through dehydration. It caused millions of deaths in Europe in the ninteenth century but has since mostly disappeared from industrialized countries. It still claims >100,000 lives per year in developing countries.

Salmonella enterica subsp. enterica serovar Typhi

All Salmonella cause disease, but the Typhi lineage is the main causative agent of typhoid fever, which claims hundreds of thousands of lives per annum.

New Delhi metallo-β-lactamase-1

An enzyme that confers extensive antibiotic resistance; first characterized in 2008.

Chlamydia trachomatis

The cause of >100 million sexually transmitted infections annually, as well as trachoma, which is an infection of the eye that can result in blindness.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Didelot, X., Bowden, R., Wilson, D. et al. Transforming clinical microbiology with bacterial genome sequencing. Nat Rev Genet 13, 601–612 (2012). https://doi.org/10.1038/nrg3226

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrg3226

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research