Dental Plaque Microbiota Sequence Counts for Microbial Profiling and Resistance Genes Detection

Shotgun metagenomics sequencing experiments are finding a wide range of applications nonetheless, there are still limited guidelines regarding the number of sequences needed to acquire meaningful information for taxonomic profiling and antimicrobial resistance genes (ARGs) identification. In this study, we explored this issue in the context of oral microbiota by sequencing with a very high number of sequences (~100 million) 4 human plaque samples and 1 microbial community standard, and by evaluating the performance of microbial identification and ARGs detection through a downsampling procedure. When investigating the impact of a decreasing number of sequences on quantitative taxonomic profiling in the microbial community standard datasets, we found some discrepancies in the identified microbial species and their abundances when compared to the expected ones. Such differences were consistent throughout downsampling, suggesting their link to taxonomic profiling methods limitations. Overall, results showed that the number of sequences has a great impact on metagenomic samples at the qualitative (i.e., presence/absence) level in terms of loss of information, especially in experiments having less than 40 million reads; whereas only small variations affecting low abundant species affected abundance estimation. The presence of ARGs was also assessed: a total of 133 ARGs were identified and 23% of them did not consistently result as present or absent across downsampling datasets of the same sample, with more than half of ARGs lost in datasets having less than 20 million reads. This study highlights the importance of carefully considering sequencing aspects and suggests some guidelines for designing shotgun metagenomics experiments with the final goal of maximizing oral microbiome analyses. Our findings suggest varying optimized sequence numbers according to different study aims: 40 million for microbiota profiling, 50 million for low abundant species detection, and 20 million for ARGs identification.