Impact of Host DNA and Sequencing Depth on the Taxonomic Resolution of Whole Metagenome Sequencing for Microbiome Analysis.

Joana Pereira-Marques,Leen-Jan Van Doorn,Ines Pinto-Ribeiro,Rui M Ferreira,Michiel Weber,Ceu Figueiredo,Anne Hout,Cornelis Willem Knetsch

doi:10.3389/fmicb.2019.01277

Abstract

The amount of host DNA poses a major challenge to metagenome analysis. However, there is no guidance on the levels of host DNA, nor on the depth of sequencing needed to acquire meaningful information from whole metagenome sequencing (WMS). Here, we evaluated the impact of a wide range of amounts of host DNA and sequencing depths on microbiome taxonomic profiling using WMS. Synthetic samples with increasing levels of host DNA were created by spiking DNA of a mock bacterial community, with DNA from a mouse-derived cell line. Taxonomic analysis revealed that increasing proportions of host DNA led to decreased sensitivity in detecting very low and low abundant species. Reduction of sequencing depth had major impact on the sensitivity of WMS for profiling samples with 90% host DNA, increasing the number of undetected species. Finally, analysis of simulated datasets with fixed depth of 10 million reads confirmed that microbiome profiling becomes more inaccurate as the level of host DNA increases in a sample. In conclusion, samples with high amounts of host DNA coupled with reduced sequencing depths, decrease WMS coverage for characterization of the microbiome. This study highlights the importance of carefully considering these aspects in the design of WMS experiments to maximize microbiome analyses.

Highlights

The collection of microorganisms present in a defined environment is known as the microbiota (Marchesi and Ravel, 2015)
The relative low number of pre-processed reads in SS90 and SS99 samples was due to high number of host DNA sequences removed rather than to reads dropped during quality filtering (Supplementary Table S2)
Samples with a high amount of host DNA remains a major challenge in whole metagenome analysis, affecting the efficiency of microbiome profiling (Quince et al, 2017)

Summary

Introduction

The collection of microorganisms present in a defined environment is known as the microbiota (Marchesi and Ravel, 2015). In comparison with targeted 16S rRNA sequencing, WMS typically yields a more detailed taxonomic resolution, at the species or even strainlevel. It provides a more accurate insight into the functional composition of the microbiome (Qin et al, 2010; Abubucker et al, 2012; Truong et al, 2015; Truong et al, 2017). Still, this approach has been less implemented since it is more expensive than 16S rRNA profiling, it requires a greater depth of coverage, and the data analysis is more complex (Knight et al, 2012)

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Conclusion