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Abstract

The taxonomy and associated nomenclature of many taxa of rumen bacteria
are poorly defined within databases of 16S rRNA genes. This lack of
resolution results in inadequate definition of microbial community
structures, with large parts of the community designated as incertae
sedis, unclassified, or uncultured 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 partial bacterial 16S rRNA gene
sequences amplified from 684 rumen samples and representing a wide range
of animal hosts and diets. Representative sequences from the 8,985
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 (mainly at genus or family
level) of abundant rumen bacteria in the ARB SILVA 119 framework. A
total of 99 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. 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 the sequence data to be assigned to 751
uniquely identifiable genus-level groups. The improved framework
increased this to 87.1% of all sequences being assigned to one of 871
uniquely identifiable genus-level groups. The new designations were
included in the SILVA 123 release
(https://www.arb-silva.de/documentation/release-123/) and will be
perpetuated in future releases.