Shallow shotgun sequencing of the microbiome recapitulates 16S amplicon results and provides functional insights.
Mason R StothartPhilip D McLoughlinJocelyn PoissantPublished in: Molecular ecology resources (2022)
Prevailing 16S rRNA gene-amplicon methods for characterizing the bacterial microbiome of wildlife are economical, but result in coarse taxonomic classifications, are subject to primer and 16S copy number biases, and do not allow for direct estimation of microbiome functional potential. While deep shotgun metagenomic sequencing can overcome many of these limitations, it is prohibitively expensive for large sample sets. Here we evaluated the ability of shallow shotgun metagenomic sequencing to characterize taxonomic and functional patterns in the faecal microbiome of a model population of feral horses (Sable Island, Canada). Since 2007, this unmanaged population has been the subject of an individual-based, long-term ecological study. Using deep shotgun metagenomic sequencing, we determined the sequencing depth required to accurately characterize the horse microbiome. In comparing conventional vs. high-throughput shotgun metagenomic library preparation techniques, we validate the use of more cost-effective laboratory methods. Finally, we characterize similarities between 16S amplicon and shallow shotgun characterization of the microbiome, and demonstrate that the latter recapitulates biological patterns first described in a published amplicon data set. Unlike for amplicon data, we further demonstrate how shallow shotgun metagenomic data provide useful insights regarding microbiome functional potential which support previously hypothesized diet effects in this study system.
Keyphrases
- copy number
- single cell
- antibiotic resistance genes
- high throughput
- mitochondrial dna
- electronic health record
- big data
- genome wide
- physical activity
- dna methylation
- gene expression
- climate change
- systematic review
- deep learning
- molecularly imprinted
- data analysis
- weight loss
- wastewater treatment
- transcription factor
- molecular dynamics simulations