1887

Abstract

Metagenomic methods enable the simultaneous characterization of microbial communities without time-consuming and bias-inducing culturing. Metagenome-assembled genome (MAG) binning methods aim to reassemble individual genomes from this data. However, the recovery of mobile genetic elements (MGEs), such as plasmids and genomic islands (GIs), by binning has not been well characterized. Given the association of antimicrobial resistance (AMR) genes and virulence factor (VF) genes with MGEs, studying their transmission is a public-health priority. The variable copy number and sequence composition of MGEs makes them potentially problematic for MAG binning methods. To systematically investigate this issue, we simulated a low-complexity metagenome comprising 30 GI-rich and plasmid-containing bacterial genomes. MAGs were then recovered using 12 current prediction pipelines and evaluated. While 82–94 % of chromosomes could be correctly recovered and binned, only 38–44 % of GIs and 1–29 % of plasmid sequences were found. Strikingly, no plasmid-borne VF nor AMR genes were recovered, and only 0–45 % of AMR or VF genes within GIs. We conclude that short-read MAG approaches, without further optimization, are largely ineffective for the analysis of mobile genes, including those of public-health importance, such as AMR and VF genes. We propose that researchers should explore developing methods that optimize for this issue and consider also using unassembled short reads and/or long-read approaches to more fully characterize metagenomic data.

Funding
This study was supported by the:
  • Fiona S.L. Brinkman , Natural Sciences and Engineering Research Council of Canada
  • Robert G Beiko , Genome Canada
  • Fiona S.L. Brinkman , Simon Fraser University , (Award Distinguished Professorship)
  • Wing Yin Venus Lau , Simon Fraser University , (Award Omics and Data Sciences fellowship)
  • Kristen Gray , Simon Fraser University , (Award Omics and Data Sciences fellowship)
  • Baofeng Jia , Simon Fraser University , (Award Omics and Data Sciences fellowship)
  • Kristen Gray , Natural Sciences and Engineering Research Council of Canada , (Award Collaborative Research and Training Experience (CREATE) Bioinformatics scholarship)
  • Wing Yin Venus Lau , Canadian Institutes of Health Research , (Award Doctoral Scholarship)
  • Finlay Maguire , Donald Hill Family Fellowship
  • Baofeng Jia , Canadian Institutes of Health Research , (Award Doctoral Scholarship)
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2020-10-01
2020-12-04
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References

  1. Breitbart M, Salamon P, Andresen B, Mahaffy JM, Segall AM et al. Genomic analysis of uncultured marine viral communities. Proc Natl Acad Sci USA 2002; 99:14250–14255 [CrossRef]
    [Google Scholar]
  2. Quince C, Walker AW, Simpson JT, Loman NJ, Segata N. Shotgun metagenomics, from sampling to analysis. Nat Biotechnol 2017; 35:833–844 [CrossRef]
    [Google Scholar]
  3. Donia MS, Cimermancic P, Schulze CJ, Wieland Brown LC, Martin J et al. A systematic analysis of biosynthetic gene clusters in the human microbiome reveals a common family of antibiotics. Cell 2014; 158:1402–1414 [CrossRef]
    [Google Scholar]
  4. D’Costa VM, Griffiths E, Wright GD. Expanding the soil antibiotic resistome: exploring environmental diversity. Curr Opin Microbiol 2007; 10:481–489 [CrossRef]
    [Google Scholar]
  5. D’Costa VM, King CE, Kalan L, Morar M, Sung WWL et al. Antibiotic resistance is ancient. Nature 2011; 477:457–461 [CrossRef]
    [Google Scholar]
  6. Loman NJ, Constantinidou C, Christner M, Rohde H, Chan JZ-M et al. A culture-independent sequence-based metagenomics approach to the investigation of an outbreak of Shiga-toxigenic Escherichia coli O104:H4. JAMA 2013; 309:1502 [CrossRef]
    [Google Scholar]
  7. Mikheyev AS, Tin MMY. A first look at the Oxford Nanopore MinION sequencer. Mol Ecol Resour 2014; 14:1097–1102 [CrossRef][PubMed]
    [Google Scholar]
  8. Eid J, Fehr A, Gray J, Luong K, Lyle J et al. Real-time DNA sequencing from single polymerase molecules. Science 2009; 323:133–138 [CrossRef]
    [Google Scholar]
  9. Nicholls SM, Quick JC, Tang S, Loman NJ. Ultra-deep, long-read nanopore sequencing of mock microbial community standards. Gigascience 2019; 8:giz043 [CrossRef]
    [Google Scholar]
  10. Somerville V, Lutz S, Schmid M, Frei D, Moser A et al. Long-read based de novo assembly of low-complexity metagenome samples results in finished genomes and reveals insights into strain diversity and an active phage system. BMC Microbiol 2019; 19:143 [CrossRef]
    [Google Scholar]
  11. Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods 2015; 12:59–60 [CrossRef]
    [Google Scholar]
  12. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods 2012; 9:357–359 [CrossRef]
    [Google Scholar]
  13. Wheeler TJ, Eddy SR. nhmmer: DNA homology search with profile HMMs. Bioinformatics 2013; 29:2487–2489 [CrossRef]
    [Google Scholar]
  14. Ounit R, Wanamaker S, Close TJ, Lonardi S. CLARK: fast and accurate classification of metagenomic and genomic sequences using discriminative k-mers. BMC Genomics 2015; 16:236 [CrossRef][PubMed]
    [Google Scholar]
  15. Xu X, Lin D, Yan G, Ye X, Wu S et al. vanM, a new glycopeptide resistance gene cluster found in Enterococcus faecium . Antimicrob Agents Chemother 2010; 54:4643–4647 [CrossRef]
    [Google Scholar]
  16. Baker-Austin C, Wright MS, Stepanauskas R, McArthur JV. Co-selection of antibiotic and metal resistance. Trends Microbiol 2006; 14:176–182 [CrossRef]
    [Google Scholar]
  17. Stokes HW, Gillings MR. Gene flow, mobile genetic elements and the recruitment of antibiotic resistance genes into gram-negative pathogens. FEMS Microbiol Rev 2011; 35:790–819 [CrossRef]
    [Google Scholar]
  18. Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res 2017; 27:824–834 [CrossRef]
    [Google Scholar]
  19. Peng Y, Leung HCM, Yiu SM, Chin FYL. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 2012; 28:1420–1428 [CrossRef]
    [Google Scholar]
  20. Li D, Liu C-M, Luo R, Sadakane K, Lam T-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 2015; 31:1674–1676 [CrossRef]
    [Google Scholar]
  21. Tyson GW, Chapman J, Hugenholtz P, Allen EE, Ram RJ et al. Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature 2004; 428:37–43 [CrossRef]
    [Google Scholar]
  22. Breitwieser FP, Lu J, Salzberg SL. A review of methods and databases for metagenomic classification and assembly. Brief Bioinform 2019; 20:1125–1136 [CrossRef]
    [Google Scholar]
  23. YY L, Chen T, Fuhrman JA, Sun F. COCACOLA: binning metagenomic contigs using sequence COmposition, read CoverAge, CO-alignment and paired-end read LinkAge. Bioinformatics 2016btw290
    [Google Scholar]
  24. Kang DD, Li F, Kirton ES, Thomas A, Egan RS et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 2019; 7:e7359 [CrossRef][PubMed]
    [Google Scholar]
  25. Y-W W, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 2016; 32:605–607
    [Google Scholar]
  26. Sieber CMK, Probst AJ, Sharrar A, Thomas BC, Hess M et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol 2018; 3:836–843 [CrossRef][PubMed]
    [Google Scholar]
  27. Brown CT, Hug LA, Thomas BC, Sharon I, Castelle CJ et al. Unusual biology across a group comprising more than 15% of domain Bacteria. Nature 2015; 523:208–211 [CrossRef]
    [Google Scholar]
  28. Parks DH, Rinke C, Chuvochina M, Chaumeil P-A, Woodcroft BJ et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol 2017; 2:1533–1542 [CrossRef][PubMed]
    [Google Scholar]
  29. Stewart RD, Auffret MD, Warr A, Walker AW, Roehe R et al. Compendium of 4,941 rumen metagenome-assembled genomes for rumen microbiome biology and enzyme discovery. Nat Biotechnol 2019; 37:953–961 [CrossRef][PubMed]
    [Google Scholar]
  30. Woodcroft BJ, Singleton CM, Boyd JA, Evans PN, Emerson JB et al. Genome-centric view of carbon processing in thawing permafrost. Nature 2018; 560:49–54 [CrossRef]
    [Google Scholar]
  31. Diamond S, Andeer PF, Li Z, Crits-Christoph A, Burstein D et al. Mediterranean grassland soil C–N compound turnover is dependent on rainfall and depth, and is mediated by genomically divergent microorganisms. Nat Microbiol 2019; 4:1356–1367 [CrossRef][PubMed]
    [Google Scholar]
  32. Meyer F, Hofmann P, Belmann P, Garrido-Oter R, Fritz A et al. AMBER: assessment of metagenome BinnERs. Gigascience 2018; 7:giy069 [CrossRef][PubMed]
    [Google Scholar]
  33. Yue Y, Huang H, Qi Z, Dou H-M, Liu X-Y et al. Evaluating metagenomics tools for genome binning with real metagenomic datasets and CAMI datasets. BMC Bioinformatics 2020; 21:334 [CrossRef][PubMed]
    [Google Scholar]
  34. Langille MGI, Hsiao WWL, Brinkman FSL. Detecting genomic islands using bioinformatics approaches. Nat Rev Microbiol 2010; 8:373–382 [CrossRef]
    [Google Scholar]
  35. Soucy SM, Huang J, Gogarten JP. Horizontal gene transfer: building the web of life. Nat Rev Genet 2015; 16:472–482 [CrossRef]
    [Google Scholar]
  36. Ho Sui SJ, Fedynak A, Hsiao WWL, Langille MGI, Brinkman FSL. The association of virulence factors with genomic islands. PLoS One 2009; 4:e8094 [CrossRef][PubMed]
    [Google Scholar]
  37. von Wintersdorff CJH, Penders J, van Niekerk JM, Mills ND, Majumder S et al. Dissemination of antimicrobial resistance in microbial ecosystems through horizontal gene transfer. Front Microbiol 2016; 7:173 [CrossRef][PubMed]
    [Google Scholar]
  38. Brown-Jaque M, Calero-Cáceres W, Muniesa M. Transfer of antibiotic-resistance genes via phage-related mobile elements. Plasmid 2015; 79:1–7 [CrossRef]
    [Google Scholar]
  39. Merkl R. SIGI: score-based identification of genomic islands. BMC Bioinformatics 2004; 5:22 [CrossRef]
    [Google Scholar]
  40. Bertelli C, Brinkman FSL. Improved genomic island predictions with IslandPath-DIMOB. Bioinformatics 2018; 34:2161–2167 [CrossRef]
    [Google Scholar]
  41. Dhillon BK, Laird MR, Shay JA, Winsor GL, Lo R et al. IslandViewer 3: more flexible, interactive genomic island discovery, visualization and analysis. Nucleic Acids Res 2015; 43:W104–W108 [CrossRef][PubMed]
    [Google Scholar]
  42. Bertelli C, Tilley KE, Brinkman FSL. Microbial genomic island discovery, visualization and analysis. Brief Bioinform 2019; 20:1685–1698 [CrossRef]
    [Google Scholar]
  43. San Millan A, Escudero JA, Gifford DR, Mazel D, MacLean RC. Multicopy plasmids potentiate the evolution of antibiotic resistance in bacteria. Nat Ecol Evol 2016; 1:10 [CrossRef][PubMed]
    [Google Scholar]
  44. San Millan A, Santos-Lopez A, Ortega-Huedo R, Bernabe-Balas C, Kennedy SP et al. Small-plasmid-mediated antibiotic resistance is enhanced by increases in plasmid copy number and bacterial fitness. Antimicrob Agents Chemother 2015; 59:3335–3341 [CrossRef][PubMed]
    [Google Scholar]
  45. Zhou F, Xu Y. cBar: a computer program to distinguish plasmid-derived from chromosome-derived sequence fragments in metagenomics data. Bioinformatics 2010; 26:2051–2052 [CrossRef]
    [Google Scholar]
  46. Davis JJ, Olsen GJ. Modal codon usage: assessing the typical codon usage of a genome. Mol Biol Evol 2010; 27:800–810 [CrossRef]
    [Google Scholar]
  47. Daubin V, Lerat E, Perrière G. The source of laterally transferred genes in bacterial genomes. Genome Biol 2003; 4:R57 [CrossRef][PubMed]
    [Google Scholar]
  48. Holmes AH, Moore LSP, Sundsfjord A, Steinbakk M, Regmi S et al. Understanding the mechanisms and drivers of antimicrobial resistance. Lancet 2016; 387:176–187 [CrossRef][PubMed]
    [Google Scholar]
  49. Williams KP. Integration sites for genetic elements in prokaryotic tRNA and tmRNA genes: sublocation preference of integrase subfamilies. Nucleic Acids Res 2002; 30:866–875 [CrossRef]
    [Google Scholar]
  50. Schmidt H, Hensel M. Pathogenicity islands in bacterial pathogenesis. Clin Microbiol Rev 2004; 17:14–56 [CrossRef]
    [Google Scholar]
  51. Acuña-Amador L, Primot A, Cadieu E, Roulet A, Barloy-Hubler F. Genomic repeats, misassembly and reannotation: a case study with long-read resequencing of Porphyromonas gingivalis reference strains. BMC Genomics 2018; 19:54 [CrossRef][PubMed]
    [Google Scholar]
  52. Sczyrba A, Hofmann P, Belmann P, Koslicki D, Janssen S et al. Critical assessment of metagenome interpretation — a benchmark of metagenomics software. Nat Methods 2017; 14:1063–1071 [CrossRef][PubMed]
    [Google Scholar]
  53. Arredondo-Alonso S, Willems RJ, van Schaik W, Schürch AC. On the (im)possibility of reconstructing plasmids from whole-genome short-read sequencing data. Microb Genom 2017; 3:e000128 [CrossRef][PubMed]
    [Google Scholar]
  54. Huang W, Li L, Myers JR, Marth GT. ART: a next-generation sequencing read simulator. Bioinformatics 2012; 28:593–594 [CrossRef]
    [Google Scholar]
  55. Joshi N, Fass J. Sickle: a sliding-window, adaptive, quality-based trimming tool for FastQ files. GitHub; 2011
    [Google Scholar]
  56. Mikheenko A, Saveliev V, Gurevich A. MetaQUAST: evaluation of metagenome assemblies. Bioinformatics 2016; 32:1088–1090 [CrossRef]
    [Google Scholar]
  57. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J et al. BLAST+: architecture and applications. BMC Bioinformatics 2009; 10:421 [CrossRef]
    [Google Scholar]
  58. Simão FA, Waterhouse RM, Ioannidis P, Kriventseva EV, Zdobnov EM. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 2015; 31:3210–3212 [CrossRef]
    [Google Scholar]
  59. Nakamura T, Yamada KD, Tomii K, Katoh K. Parallelization of MAFFT for large-scale multiple sequence alignments. Bioinformatics 2018; 34:2490–2492 [CrossRef]
    [Google Scholar]
  60. Capella-Gutierrez S, Silla-Martinez JM, Gabaldon T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 2009; 25:1972–1973 [CrossRef]
    [Google Scholar]
  61. Nguyen L-T, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol 2015; 32:268–274 [CrossRef]
    [Google Scholar]
  62. Lanfear R, Calcott B, Ho SYW, Guindon S. PartitionFinder: combined selection of partitioning schemes and substitution models for phylogenetic analyses. Mol Biol Evol 2012; 29:1695–1701 [CrossRef]
    [Google Scholar]
  63. Letunic I, Bork P. Interactive tree of life (iTOL) v4: recent updates and new developments. Nucleic Acids Res 2019; 47:W256–W259 [CrossRef]
    [Google Scholar]
  64. Huerta-Cepas J, Serra F, Bork P. ETE 3: reconstruction, analysis, and visualization of phylogenomic data. Mol Biol Evol 2016; 33:1635–1638 [CrossRef]
    [Google Scholar]
  65. Waskom M, Botvinnik O, Ostblom J, Lukauskas S, Hobson P. mwaskom/seaborn: v0.10.0 (January 2020) Zenodo; 2020
  66. Seabold S, Perktold J. Statsmodels: econometric and statistical modeling with python. Proceedings of the 9th Python in Science Conference 2010;92–96
    [Google Scholar]
  67. Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 2010; 11:119 [CrossRef][PubMed]
    [Google Scholar]
  68. Alcock BP, Raphenya AR, Lau TTY, Tsang KK, Bouchard M et al. CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucleic Acids Res 2020; 48:D517-D525 [CrossRef][PubMed]
    [Google Scholar]
  69. Liu B, Zheng D, Jin Q, Chen L, Yang J. VFDB 2019: a comparative pathogenomic platform with an interactive web interface. Nucleic Acids Res 2019; 47:D687–D692 [CrossRef][PubMed]
    [Google Scholar]
  70. Pellow D, Zorea A, Probst M, Furman O, Segal A et al. SCAPP: an algorithm for improved plasmid assembly in metagenomes. bioRxiv 2020 [CrossRef]
    [Google Scholar]
  71. Giguere DJ, Bahcheli AT, Joris BR, Paulssen JM, Gieg LM et al. Complete and validated genomes from a metagenome. bioRxiv 202010.1101/2020.04.08.032540
    [Google Scholar]
  72. Suzuki Y, Nishijima S, Furuta Y, Yoshimura J, Suda W et al. Long-read metagenomic exploration of extrachromosomal mobile genetic elements in the human gut. Microbiome 2019; 7:119 [CrossRef][PubMed]
    [Google Scholar]
  73. Ravi A, Halstead FD, Bamford A, Casey A, Thomson NM et al. Loss of microbial diversity and pathogen domination of the gut microbiota in critically ill patients. Microb Genom 2019; 5:e000293 [CrossRef][PubMed]
    [Google Scholar]
  74. Liu Z, Klümper U, Liu Y, Yang Y, Wei Q et al. Metagenomic and metatranscriptomic analyses reveal activity and hosts of antibiotic resistance genes in activated sludge. Environ Int 2019; 129:208–220 [CrossRef]
    [Google Scholar]
  75. Newberry E, Bhandari R, Kemble J, Sikora E, Potnis N. Genome‐resolved metagenomics to study co‐occurrence patterns and intraspecific heterogeneity among plant pathogen metapopulations. Environ Microbiol 2020; 22:2693–2708 [CrossRef]
    [Google Scholar]
  76. Zhang Y, Kitajima M, Whittle AJ, Liu W-T. Benefits of genomic insights and CRISPR-Cas signatures to monitor potential pathogens across drinking water production and distribution systems. Front Microbiol 2017; 8:2036 [CrossRef][PubMed]
    [Google Scholar]
  77. Huang AD, Luo C, Pena-Gonzalez A, Weigand MR, Tarr CL et al. Metagenomics of two severe foodborne outbreaks provides diagnostic signatures and signs of coinfection not attainable by traditional methods. Appl Environ Microbiol 2017; 83:e02577-16 [CrossRef][PubMed]
    [Google Scholar]
  78. Klassen JL, Currie CR. Gene fragmentation in bacterial draft genomes: extent, consequences and mitigation. BMC Genomics 2012; 13:14 [CrossRef]
    [Google Scholar]
  79. Abayasekara LM, Perera J, Chandrasekharan V, Gnanam VS, Udunuwara NA et al. Detection of bacterial pathogens from clinical specimens using conventional microbial culture and 16S metagenomics: a comparative study. BMC Infect Dis 2017; 17:631 [CrossRef][PubMed]
    [Google Scholar]
  80. Rogers GB, Carroll MP, Serisier DJ, Hockey PM, Jones G et al. Characterization of bacterial community diversity in cystic fibrosis lung infections by use of 16S ribosomal DNA terminal restriction fragment length polymorphism profiling. J Clin Microbiol 2004; 42:5176–5183 [CrossRef]
    [Google Scholar]
  81. Freitas AC, Chaban B, Bocking A, Rocco M, Yang S et al. The vaginal microbiome of pregnant women is less rich and diverse, with lower prevalence of Mollicutes, compared to non-pregnant women. Sci Rep 2017; 7:9212 [CrossRef][PubMed]
    [Google Scholar]
  82. Gołębiewski M, Deja-Sikora E, Cichosz M, Tretyn A, Wróbel B. 16S rDNA pyrosequencing analysis of bacterial community in heavy metals polluted soils. Microb Ecol 2014; 67:635–647 [CrossRef]
    [Google Scholar]
  83. Youssef N, Sheik CS, Krumholz LR, Najar FZ, Roe BA et al. Comparison of species richness estimates obtained using nearly complete fragments and simulated pyrosequencing-generated fragments in 16S rRNA gene-based environmental surveys. Appl Environ Microbiol 2009; 75:5227–5236 [CrossRef][PubMed]
    [Google Scholar]
  84. Claesson MJ, O'Sullivan O, Wang Q, Nikkilä J, Marchesi JR et al. Comparative analysis of pyrosequencing and a phylogenetic microarray for exploring microbial community structures in the human distal intestine. PLoS One 2009; 4:e6669 [CrossRef][PubMed]
    [Google Scholar]
  85. Thomas M, Webb M, Ghimire S, Blair A, Olson K et al. Metagenomic characterization of the effect of feed additives on the gut microbiome and antibiotic resistome of feedlot cattle. Sci Rep 2017; 7:12257 [CrossRef][PubMed]
    [Google Scholar]
  86. Mulvey MR, Boyd DA, Olson AB, Doublet B, Cloeckaert A. The genetics of Salmonella genomic island 1. Microbes Infect 2006; 8:1915–1922 [CrossRef]
    [Google Scholar]
  87. Arora SK, Wolfgang MC, Lory S, Ramphal R. Sequence polymorphism in the glycosylation island and flagellins of Pseudomonas aeruginosa . J Bacteriol 2004; 186:2115–2122 [CrossRef][PubMed]
    [Google Scholar]
  88. Redondo-Salvo S, Fernández-López R, Ruiz R, Vielva L, de Toro M et al. Pathways for horizontal gene transfer in bacteria revealed by a global map of their plasmids. Nat Commun 2020; 11:3602 [CrossRef][PubMed]
    [Google Scholar]
  89. Fritz A, Hofmann P, Majda S, Dahms E, Dröge J et al. CAMISIM: simulating metagenomes and microbial communities. Microbiome 2019; 7:17 [CrossRef][PubMed]
    [Google Scholar]
  90. Bokulich NA, Rideout JR, Mercurio WG, Shiffer A, Wolfe B et al. mockrobiota: a public resource for microbiome bioinformatics benchmarking. mSystems 2016; 1:e00062-16 [CrossRef][PubMed]
    [Google Scholar]
  91. Karimzadeh M, Hoffman MM. Top considerations for creating bioinformatics software documentation. Brief Bioinform 2018; 19:693–699 [CrossRef]
    [Google Scholar]
  92. Hunt M, Mather AE, Sánchez-Busó L, Page AJ, Parkhill J et al. ARIBA: rapid antimicrobial resistance genotyping directly from sequencing reads. Microb Genom 2017; 3:e000131 [CrossRef][PubMed]
    [Google Scholar]
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