Comparison of 16S rRNA Gene Based Microbial Profiling Using Five Next-Generation Sequencers and Various Primers

Microbial community analysis based on the 16S rRNA-gene is used to investigate both beneficial and harmful microorganisms in various fields and environments. Recently, the next-generation sequencing (NGS) technology has enabled rapid and accurate microbial community analysis. Despite these advantages of NGS based metagenomics study, sample transport, storage conditions, amplification, library preparation kits, sequencing, and bioinformatics procedures can bias microbial community analysis results. In this study, eight mock communities were pooled from genomic DNA of Lactobacillus acidophilus KCTC 3164(T), Limosilactobacillus fermentum KCTC 3112(T), Lactobacillus gasseri KCTC 3163(T), Lacticaseibacillus paracasei subsp. paracasei KCTC 3510(T), Limosilactobacillus reuteri KCTC 3594(T), Lactococcus lactis subsp. lactis KCTC 3769(T), Bifidobacterium animalis subsp. lactis KCTC 5854(T), and Bifidobacterium breve KCTC 3220(T). The genomic DNAs were quantified by droplet digital PCR (ddPCR) and were mixed as mock communities. The mock communities were amplified with various 16S rRNA gene universal primer pairs and sequenced by MiSeq, IonTorrent, MGIseq-2000, Sequel II, and MinION NGS platforms. In a comparison of primer-dependent bias, the microbial profiles of V1-V2 and V3 regions were similar to the original ratio of the mock communities, while the microbial profiles of the V1-V3 region were relatively biased. In a comparison of platform-dependent bias, the sequence read from short-read platforms (MiSeq, IonTorrent, and MGIseq-2000) showed lower bias than that of long-read platforms (Sequel II and MinION). Meanwhile, the sequences read from Sequel II and MinION platforms were relatively biased in some mock communities. In the data of all NGS platforms and regions, L. acidophilus was greatly underrepresented while Lactococcus lactis subsp. lactis was generally overrepresented. In all samples of this study, the bias index (BI) was calculated and PCA was performed for comparison. The samples with biased relative abundance showed high BI values and were separated in the PCA results. In particular, analysis of regions rich in AT and GC poses problems for genome assembly, which can lead to sequencing bias. According to this comparative analysis, the development of reference material (RM) material has been proposed to calibrate the bias in microbiome analysis.