Improved taxonomic assignment of rumen bacterial 16S rRNA sequences using a revised SILVA taxonomic framework

Many taxa of rumen bacteria are poorly defined within taxonomic databases, being designated as unclassified, uncultured, or incertae sedis. This lack of resolution results in inadequate definition of microbial community structures, with large parts of the community reported as unclassified within families, orders, or even classes. We have begun resolving these poorly-defined groups of rumen bacteria, based on our desire to name these for use in microbial community profiling. We used the previously-reported Global Rumen Census (GRC) dataset consisting of >4.5 million bacterial 16S rRNA gene sequences amplified from 684 rumen samples and representing a range of animal hosts and diets. Representative sequences from the 8985 largest operational units (groups of sequence sharing >97% sequence similarity, and covering 97.8% of all sequences in the GRC dataset) were used to identify 241 pre-defined clusters of abundant rumen bacteria in the ARB SILVA 119 framework. Ninety-nine of these clusters (containing 63.8% of all GRC sequences) had no unique or had inadequate taxonomic identifiers, and each was given a unique nomenclature. The new designations were included in the SILVA 123 release [https://www.arb-silva.de/documentation/release-123/] and will be perpetuated, until updated, in future releases. We assessed this improved framework by comparing taxonomic assignments of bacterial 16S rRNA gene sequence data in the GRC dataset with those made using the original SILVA 119 framework, and three other frameworks. The two SILVA frameworks performed best at assigning sequences to genus-level taxa. The SILVA 119 framework allowed 55.4% of sequence data to be assigned to 751 uniquely identifiable genus-level groups. The improved SILVA 123 framework increased this to 87.1% of all sequences being assigned to one of 871 uniquely identifiable genus-level groups. We also showed that the more refined SILVA 123 framework improved resolution of diet- and species-associated differences between microbial communities in the GRC dataset.