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

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.

The Gut Microbiome and Schizophrenia: The Current State of the Field and Clinical Applications

“…Additionally, one study used shotgun metagenomic sequencing on throat swab samples (20). Aside from the problem of differences between oropharyngeal and intestinal microbiomes, an important caveat of this approach is that the abundance of host DNA in swab samples may impair the accuracy of microbiome profiling (29). Considering the intrinsic variations in microbiome profiles, methodological consistency is necessary to generate data which will allow useful and accurate comparisons across studies.…”

Internal control for process monitoring of clinical metagenomic next-generation sequencing of urine samples

“…When assessing the clinical relevance of species present at very low relative abundance, the likelihood that these reads are present as a result of cross-contamination should be taken into consideration. Relative abundances of 0.05-2.78% have been described for droplet cross-contamination during sample preparation(28). Barcode hopping, the incorrect assignment of library molecules from the expected barcode to a different barcode in a multiplexed pool, was observed in biological mock community samples at a rate of 0.033% on the IonTorrent PGM platform which has similar technology to the IonTorrent Proton(29).…”

“…The incongruent sample A consisted of >95% human reads, leaving only a median of 2,442 (range 686-11,769) classified bacterial reads per sequenced aliquot. The low bacterial read count can explain the poor correlation between sequenced aliquots and this data may help set guidelines for a minimum sequencing depth for clinical samples and stresses the need for removal of host cells prior to sequencing(28, 29). Despite the variation in RA of bacterial species in sample A, the IC was detected in all aliquots, indicating sequencing and library preparation processes had been technically successful.…”

mbImpute: an accurate and robust imputation method for microbiome data

“…In contrast, both technical and sampling zeros are non-biological zeros with different origins: technical zeros arise from presequencing experimental artifacts (e.g., DNA degradation during library preparation and inefficient sequence amplification due to factors such as GC content bias) [22], while sampling zeros are due to limited sequencing depths. Although WGS data have much larger per-sample total read counts than 16S data have, they still suffer from excess zeros because they sequence more nucleic acid sequences (microbial genomes instead of 16S rRNAs) and widespread host DNA contamination reduces the effective sequencing depths for microbial genomes [23][24][25].…”