LAUSR

Amplicon sequencing approach is commonly employed in microbiome studies and sequencing depth is considered as a major factor influencing the outcome of data analyses. As of now, the effect of amplicon sequencing depth in environmental microbiome analyses is not explicitly illustrated. In this study, microbiome data of nine aquatic samples from Sundarbans mangrove region, obtained from SRA, were analyzed to explain the influence of sequencing depth variation in environmental microbiome data analyses. Briefly, four groups based on number of reads (NOR) were created comprising of, total NOR, 75 k, 50 k and 25 k, followed by data analyses. The results showed that the observed ASVs among four groups were significantly different (P value 1.094e-06). The Bray-Curtis dissimilarity analysis showed differences in microbiome composition and also, each group exhibited slightly different core-microbiome structure. Importantly, the variation in sequencing depth was found to affect the predictions of environmental drivers associated with microbiome composition. Thus, this study emphasizes that the microbiome data are compositional and the NOR in the data could affect the microbial composition. In summary, this study demonstrates the consequences of sequencing depth variation on microbiome data analyses and suggests the researchers to take proper cautions to avoid misleading results due to sequencing depth variation.