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Volume 6, Issue 5

Accelerating surveillance and research of antimicrobial resistance – an online repository for sharing of antimicrobial susceptibility data associated with whole-genome sequences Open Access

Abstract

Antimicrobial resistance (AMR) is an emerging threat to modern medicine. Improved diagnostics and surveillance of resistant bacteria require the development of next-generation analysis tools and collaboration between international partners. Here, we present the ‘AMR Data Hub’, an online infrastructure for storage and sharing of structured phenotypic AMR data linked to bacterial whole-genome sequences. Leveraging infrastructure built by the European COMPARE Consortium and structured around the European Nucleotide Archive (ENA), the AMR Data Hub already provides an extensive data collection of more than 2500 isolates with linked genome and AMR data. Representing these data in standardized formats, we provide tools for the validation and submission of new data and services supporting search, browse and retrieval. The current collection was created through a collaboration by several partners from the European COMPARE Consortium, demonstrating the capacities and utility of the AMR Data Hub and its associated tools. We anticipate growth of content and offer the hub as a basis for future research into methods to explore and predict AMR.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): antimicrobial resistance , data sharing , database , surveillance and whole-genome sequencing
Funding
This study was supported by the:
  • Horizon 2020 (Award 643476)
    • Principle Award Recipient: Not Applicable
© 2020 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution NonCommercial License.
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2020-04-07
2024-04-16
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References

  1. Aminov RI. A brief history of the antibiotic era: lessons learned and challenges for the future. Front Microbiol 2010; 1:134 [View Article][PubMed]
     [Google Scholar]
  2. WHO 2014; Antimicrobial resistance: global report on surveillance [Internet]. http://apps.who.int/iris/handle/10665/112642
  3. van Dijk EL, Auger H, Jaszczyszyn Y, Thermes C. Ten years of next-generation sequencing technology. Trends Genet 2014; 30:418–426 [View Article][PubMed]
     [Google Scholar]
  4. Kavvas ES, Catoiu E, Mih N, Yurkovich JT, Seif Y et al. Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance. Nat Commun 2018; 9:4306 [View Article][PubMed]
     [Google Scholar]
  5. Niehaus KE, Walker TM, Crook DW, Peto TEA, Clifton DA. Machine learning for the prediction of antibacterial susceptibility in Mycobacterium tuberculosis. 2014 IEEE-EMBS Int Conf Biomed Heal Informatics, BHI 2014 2014618–621
     [Google Scholar]
  6. Davis JJ, Boisvert S, Brettin T, Kenyon RW, Mao C et al. Antimicrobial resistance prediction in PATRIC and RAST. Sci Rep 2016; 6:1–12 [View Article][PubMed]
     [Google Scholar]
  7. Otto M. Next-Generation sequencing to monitor the spread of antimicrobial resistance. Genome Med 2017; 9:68 [View Article][PubMed]
     [Google Scholar]
  8. van Panhuis WG, Paul P, Emerson C, Grefenstette J, Wilder R et al. A systematic review of barriers to data sharing in public health. BMC Public Health 2014; 14:1144 [View Article][PubMed]
     [Google Scholar]
  9. Amid C, Pakseresht N, Silvester N, Jayathilaka S, Lund O et al. The compare data hubs. Database 2019; 2019: 01 01 2019 [View Article][PubMed]
     [Google Scholar]
  10. Amid C, Alako BTF, Balavenkataraman Kadhirvelu V, Burdett T, Burgin J et al. The European nucleotide Archive in 2019. Nucleic Acids Res 2020; 48:D70–D76 [View Article][PubMed]
     [Google Scholar]
  11. Tyson GH, McDermott PF, Li C, Chen Y, Tadesse DA et al. WGS accurately predicts antimicrobial resistance in Escherichia coli. J Antimicrob Chemother 2015; 70:2763–2769 [View Article][PubMed]
     [Google Scholar]
  12. Tyson GH, Zhao S, Li C, Ayers S, Sabo JL et al. Establishing genotypic cutoff values to measure antimicrobial resistance in Salmonella. Antimicrob Agents Chemother 2017; 61: 23 02 2017 [View Article][PubMed]
     [Google Scholar]
  13. Kim J, Greenberg DE, Pifer R, Jiang S, Xiao G et al. VAMPr: variant mapping and prediction of antibiotic resistance via explainable features and machine learning. bioRxiv 2019537381
     [Google Scholar]
  14. Dos S Ribeiro C, Koopmans MP, Haringhuizen GB. Threats to timely sharing of pathogen sequence data. Science 2018; 362:404–406 [View Article][PubMed]
     [Google Scholar]
  15. Durinx C, McEntyre J, Appel R, Apweiler R, Barlow M et al. Identifying ELIXIR core data resources. F1000Res 2017; 5:2422 [View Article]
     [Google Scholar]
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Accelerating surveillance and research of antimicrobial resistance – an online repository for sharing of antimicrobial susceptibility data associated with whole-genome sequences
M Gen 6, e000342 (2020); https://doi.org/10.1099/mgen.0.000342
/content/journal/mgen/10.1099/mgen.0.000342
/content/journal/mgen/10.1099/mgen.0.000342
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