Abstract
The reference databases play a pivotal role in amplicon microbiome research, however these databases differ in the sequence content and taxonomic information available. Studies on mock community and human health microbiome have revealed the problems associated with the choice of reference database on the outcome. Nonetheless, the influence of reference databases in environmental microbiome studies is not explicitly illustrated. This study analyzed the amplicon (V1V3, V3V4, V4V5 and V6V8) data of 128 soil samples and evaluated the impact of 16S rRNA databases, Genome Taxonomy Database (GTDB), Ribosomal Database Project (RDP), SILVA and Consensus Taxonomy (ConTax), on microbiome inference. The analyses showed that the distribution of observed amplicon sequence variants was significantly different (P-value < 2.647e-12) across four datasets, generated using different databases for each amplicon region. In addition, the beta diversity was also found to be altered by different databases. Further investigation revealed that the microbiome composition inferred by various databases differsignificantly (P-value = 0.001), irrespective of amplicon regions. This study, found that the core-microbiome structure in environmental studies is influenced by the type of reference database used. In summary, this present study illustrates that the choice of reference database could influence the outcome of environmental microbiome research.
Highlights
Environmental microorganisms render several ecosystem services but these organisms are often not cultivable in the laboratory conditions (Dick and Baker 2013; Steen et al 2019) due to which the identity and the functional importance of several microorganisms remain undetermined
The analyses showed that the distribution of observed amplicon sequence variants was significantly different (P-value < 2.647e-12) across four datasets, generated based on different databases for each amplicon region
In summary, this present study illustrates that the choice of reference database could influence the outcome of environmental microbiome research
Summary
Environmental microorganisms render several ecosystem services but these organisms are often not cultivable in the laboratory conditions (Dick and Baker 2013; Steen et al 2019) due to which the identity and the functional importance of several microorganisms remain undetermined. The generation sequencing based microbiome analysis greatly assists the scientific community to understand the diversity of cultivable as well as noncultivable environmental microorganisms (Handelsman 2004). The amplicon-based or marker-gene assisted microbiome approach has been popularly employed to study the diversity and role of microorganisms in different environments. The sequence content and taxonomic information available in common databases differ (Balvočiūtė and Huson 2017) and the comparison of reference databases revealed that the composition of databases could affect the results of microbiome studies (Robeson et al 2020). The earth microbiome project (EMP) hinted at the problem of inconsistency that could arise when different reference databases are employed in the analyses (Thompson et al 2017). This study aimed to explore different 16S rRNA reference databases and find how these databases could affect the microbiome results
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