Influence of empirically derived filtering parameters, ASV, and OTU pipelines on assessing rumen microbial diversity.

Microbes play an important role in human and animal health as well as animal productivity. The host microbial interactions within ruminants play a critical role in animal health and productivity and provide up to 70% of the animal's energy need in the form of fermentation products. As such, many studies have investigated microbial community composition to understand microbial community changes and factors that affect microbial colonization and persistence. The advances in next generation sequencing (NGS) technologies and low cost of sequencing have gravitated many studies to utilize 16S rDNA-based analysis tools for interrogation of microbiomes at a much finer scale than traditional culturing. However, such methods that rely on single base pair differences for bacterial taxa clustering may inflate or underestimate diversity leading to inaccurate identification of bacterial diversity. Therefore, in this study, we sequenced mock communities of known membership and abundance to establish filtration parameters to reduce inflation of microbial diversity due to PCR and sequencing errors. Additionally, we evaluated the effect of the resulting filtering parameters proposed using established bioinformatic pipelines on a study consisting of Holstein and Jersey cattle to identify bread and treatment effects on the bacterial community composition and the impact of the filtering on global microbial community structure analysis and results. Filtration resulted in a sharp reduction in bacterial taxa identified, yet retain most sequencing data (retaining > 79% of sequencing reads) when analyzed using 3 different microbial analysis pipelines (DADA2, Mothur, USEARCH). After filtration, conclusions from α and β-diversity tests show very similar results across all analysis methods. The mock community-based filtering parameters proposed in this study help provide a more realistic estimation of bacterial diversity. Additionally, the filtration reduced the variation between microbiome analysis methods and help identify microbial community differences that could have been missed due to large animal to animal variation observed in the unfiltered data. As such, we believe, the new filtering parameters described in this study will help obtain diversity estimates closer to realistic values and will improve the ability of detecting microbial community differences and help better understand microbial community changes in 16S rDNA-based studies.