The impact of sequencing depth on the inferred taxonomic composition and AMR gene content of metagenomic samples
2019
Gweon, H. Soon | Shaw, Liam P. | Swann, Jeremy | De Maio, Nicola | AbuOun, Manal | Niehus, Rene | Hubbard, Alasdair T. M. | Bowes, Mike J. | Bailey, Mark J. | Peto, Tim E. A. | Hoosdally, Sarah J. | Walker, A. Sarah | Sebra, Robert P. | Crook, Derrick W. | Anjum, Muna F. | Read, Daniel S. | Stoesser, Nicole | REHAB Consortium
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’.
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