Impact of sequencing depth on the characterization of the microbiome and resistome

Rahat Zaheer, Noelle Noyes, Rodrigo Ortega Polo, Shaun R. Cook, Eric Marinier, Gary Van Domselaar, Keith E. Belk, Paul S. Morley, Tim A. McAllister

Research output: Contribution to journalArticlepeer-review

59 Scopus citations

Abstract

Developments in high-throughput next generation sequencing (NGS) technology have rapidly advanced the understanding of overall microbial ecology as well as occurrence and diversity of specific genes within diverse environments. In the present study, we compared the ability of varying sequencing depths to generate meaningful information about the taxonomic structure and prevalence of antimicrobial resistance genes (ARGs) in the bovine fecal microbial community. Metagenomic sequencing was conducted on eight composite fecal samples originating from four beef cattle feedlots. Metagenomic DNA was sequenced to various depths, D1, D0.5 and D0.25, with average sample read counts of 117, 59 and 26 million, respectively. A comparative analysis of the relative abundance of reads aligning to different phyla and antimicrobial classes indicated that the relative proportions of read assignments remained fairly constant regardless of depth. However, the number of reads being assigned to ARGs as well as to microbial taxa increased significantly with increasing depth. We found a depth of D0.5 was suitable to describe the microbiome and resistome of cattle fecal samples. This study helps define a balance between cost and required sequencing depth to acquire meaningful results.

Original languageEnglish (US)
Article number5890
JournalScientific reports
Volume8
Issue number1
DOIs
StatePublished - Dec 1 2018
Externally publishedYes

Bibliographical note

Funding Information:
This study was supported by funding from the Beef Cattle Research Council (BCRC Project FOS 10.13) – Agriculture and Agri-Food Canada beef cluster. R.O.P. was supported by the Agricultural Greenhouse Gases Program funding to Dr. Erasmus Okine. The authors would like to thank Wendi Smart and Ruth Barbieri for technical support and the NML bioinformatics lab staff, especially Philip Mabon, Shane Thiessen and Paul Williams for help with software and tools installation in the NML computing cluster and Jessica Forbes for reviewing the manuscript.

Publisher Copyright:
© 2018 The Author(s).

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