Accessibility navigation


The impact of sequencing depth on the inferred taxonomic composition and AMR gene content of metagenomic samples

Gweon, H. S., Shaw, L. P., Swann, J., De Maio, N., AbuOun, M., Niehus, R., Hubbard, A. T. M., Bowes, M. J., Bailey, M. J., Peto, T. E. A., Hoosdally, S. J., Walker, A. S., Sebra, R. P., Crook, D. W., Anjum, M. F., Read, D. S., Stoesser, N. and REHAB Consortium, (2019) The impact of sequencing depth on the inferred taxonomic composition and AMR gene content of metagenomic samples. Environmental Microbiome, 14 (1). 7. ISSN 2524-6372

[img]
Preview
Text (Open Access) - Published Version
· Available under License Creative Commons Attribution.
· Please see our End User Agreement before downloading.

2MB

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1186/s40793-019-0347-1

Abstract/Summary

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’.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Life Sciences > School of Biological Sciences > Biomedical Sciences
Faculty of Life Sciences > School of Biological Sciences > Ecology and Evolutionary Biology
ID Code:87036
Publisher:Springer Nature

Downloads

Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation