| Autor: |
H. Soon Gweon, Liam P. Shaw, Jeremy Swann, Nicola De Maio, Manal AbuOun, Rene Niehus, Alasdair T. M. Hubbard, Mike J. Bowes, Mark J. Bailey, Tim E. A. Peto, Sarah J. Hoosdally, A. Sarah Walker, Robert P. Sebra, Derrick W. Crook, Muna F. Anjum, Daniel S. Read, Nicole Stoesser, on behalf of the REHAB consortium |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
Environmental Microbiome, Vol 14, Iss 1, Pp 1-15 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
2524-6372 |
| DOI: |
10.1186/s40793-019-0347-1 |
| Popis: |
Abstract Background Shotgun metagenomics is increasingly used to characterise microbial communities, particularly for the investigation of antimicrobial resistance (AMR) in different animal and environmental contexts. There are many different approaches for inferring the taxonomic composition and AMR gene content of complex community samples from shotgun metagenomic data, but there has been little work establishing the optimum sequencing depth, data processing and analysis methods for these samples. In this study we used shotgun metagenomics and sequencing of cultured isolates from the same samples to address these issues. We sampled three potential environmental AMR gene reservoirs (pig caeca, river sediment, effluent) and sequenced samples with shotgun metagenomics at high depth (~ 200 million reads per sample). Alongside this, we cultured single-colony isolates of Enterobacteriaceae from the same samples and used hybrid sequencing (short- and long-reads) to create high-quality assemblies for comparison to the metagenomic data. To automate data processing, we developed an open-source software pipeline, ‘ResPipe’. Results Taxonomic profiling was much more stable to sequencing depth than AMR gene content. 1 million reads per sample was sufficient to achieve |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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