Background: Next-generation sequencing (NGS) methods and especially 16S rRNA gene amplicon sequencing have become indispensable tools in microbial ecology. While they have opened up new possibilities for studying microbial communities, they also have one drawback, namely providing only relative abundances and thus compositional data. Quantitative PCR (qPCR) has been used for years for the quantification of bacteria. However, this method requires the development of specific primers and has a low throughput. The constraint of low throughput has recently been overcome by the development of high-throughput qPCR (HT-qPCR), which allows for the simultaneous detection of the most prevalent bacteria in moderately complex systems, such as cheese and other fermented dairy foods. In the present study, the performance of the two approaches, NGS and HT-qPCR, was compared by analyzing the same DNA samples from 21 Raclette du Valais protected designation of origin (PDO) cheeses. Based on the results obtained, the differences, accuracy, and usefulness of the two approaches were studied in detail. Results: The results obtained using NGS (non-targeted) and HT-qPCR (targeted) show considerable agreement in determining the microbial composition of the cheese DNA samples studied, albeit the fundamentally different nature of these two approaches. A few inconsistencies in species detection were observed, particularly for less abundant ones. The detailed comparison of the results for 15 bacterial species/groups measured by both methods revealed a considerable bias for certain bacterial species in the measurements of the amplicon sequencing approach. We identified as probable origin to this PCR bias due to primer mismatches, variations in the number of copies for the 16S rRNA gene, and bias introduced in the bioinformatics analysis. Conclusion: As the normalized microbial composition results of NGS and HT-qPCR agreed for most of the 21 cheese samples analyzed, both methods can be considered as complementary and reliable for studying the microbial composition of cheese. Their combined application proved to be very helpful in identifying potential biases and overcoming methodological limitations in the quantitative analysis of the cheese microbiota.
