The impact of sequencing depth on the inferred taxonomic composition and AMR gene content of metagenomic samples
Gweon HS., Shaw LP., Swann J., De Maio N., Abuoun M., Niehus R., Hubbard ATM., Bowes MJ., Bailey MJ., Peto TEA., Hoosdally SJ., Walker AS., Sebra RP., Crook DW., Anjum MF., Read DS., Stoesser N., Abuoun M., Anjum M., Bailey MJ., Barker L., Brett H., Bowes MJ., Chau K., Crook DW., Gilson D., Gweon HS., Hubbard ATM., Hoosdally S., Kavanagh J., Jones H., Peto TEA., Read DS., Sebra R., Shaw LP., Sheppard AE., Smith R., Stubberfield E., Swann J., Walker AS., Woodford N.
© 2019 The Author(s). 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- A nd 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 < 1% dissimilarity to the full taxonomic composition. However, at least 80 million reads per sample were required to recover the full richness of different AMR gene families present in the sample, and additional allelic diversity of AMR genes was still being discovered in effluent at 200 million reads per sample. Normalising the number of reads mapping to AMR genes using gene length and an exogenous spike of Thermus thermophilus DNA substantially changed the estimated gene abundance distributions. While the majority of genomic content from cultured isolates from effluent was recoverable using shotgun metagenomics, this was not the case for pig caeca or river sediment. Conclusions: Sequencing depth and profiling method can critically affect the profiling of polymicrobial animal and environmental samples with shotgun metagenomics. Both sequencing of cultured isolates and shotgun metagenomics can recover substantial diversity that is not identified using the other methods. Particular consideration is required when inferring AMR gene content or presence by mapping metagenomic reads to a database. ResPipe, the open-source software pipeline we have developed, is freely available (https://gitlab.com/hsgweon/ResPipe).