Phylogenetic microbiota profiling in fecal samples depends on combination of sequencing depth and choice of NGS analysis method

Abstract

The human gut microbiota is well established as an important factor in health and disease. Fecal sample microbiota are often analyzed as a proxy for gut microbiota, and characterized with respect to their composition profiles. Modern approaches employ whole genome shotgun next-generation sequencing as the basis for these analyses. Sequencing depth as well as choice of next-generation sequencing data analysis method constitute two main interacting methodological factors for such an approach. In this study, we used 200 million sequence read pairs from one fecal sample for comparing different taxonomy classification methods, using default and custom-made reference databases, at different sequencing depths. A mock community data set with known composition was used for validating the classification methods. Results suggest that sequencing beyond 60 million read pairs does not seem to improve classification. The phylogeny prediction pattern, when using the default databases and the consensus database, appeared to be similar for all three methods. Moreover, these methods predicted rather different species. We conclude that the choice of sequencing depth and classification method has important implications for taxonomy composition prediction. A multi-method-consensus approach for robust gut microbiota NGS analysis is recommended.

Multimodal Approach to Assessment of Fecal Microbiota Donors based on Three Complementary Methods

“…The last of the applied methods, i.e., next-generation sequencing, definitely brings the most abundant data, considering the bacterial diversity of the intestinal microbiota. The weakness of this method, however, is that the results are relative; we do not have absolute numbers of individual genera and the bacteria represented in a very low titer may be underrepresented in the sample [ 38 ]. Keeping in mind that this is a semi-quantitative method, not a quantitative one, and inferences based on the indicated percentages must be careful, we can see the differences that we observed between donor C and donors A and B may point to unique combinations of individual species.…”

Evaluation of 16S rRNA Hypervariable Regions for Bioweapon Species Detection by Massively Parallel Sequencing

“…In addition, the data obtained from these primers and MPS suggest they may be used as classifiers [ 43 ] to reveal the background bacteria of a sample, but the application may be limited depending on the data in the 16S rRNA gene database. Variation in the number of species reported by different methods could be attributed to both the differences in the taxonomy assignment strategy and the reference databases used by the methods [ 13 ]. It is likely that our 16S rRNA gene-targeted primers, in combination with MPS, can be used to detect bacteria from complex samples, as evidenced by testing with an environmental background.…”

“…Usually, after sample processing, the generated data are compared to a database to facilitate taxa identification [ 11 ]. Hugenholtz et al [ 12 ] showed that two or more 16S rRNA gene hypervariable regions could provide the phylogenetic division of microorganisms into monophyletic groups depending on the reference database and the different choices of classification [ 13 ]. Nevertheless, microbial composition data differ depending on the primers and sequencing platforms used [ 14 ].…”

Metagenomic Information Recovery from Human Stool Samples Is Influenced by Sequencing Depth and Profiling Method

“…These studies have shown that a sequencing depth of 0.5 M sequences per sample is sufficient to capture taxonomic and functional signals on par with those obtained with sequencing depths > 100 M sequences [ 3 , 10 ]. Other studies using reference materials have also found that taxonomic classification does not necessarily improve beyond 60 M paired sequences, and classification of eukaryotic communities can be estimated with 0.5 M reads [ 11 , 12 ]. Shallow SMS (SSMS) has recently emerged as a cost-effective and information-rich alternative to SMS and 16S sequencing [ 3 ].…”