Phylogeny-guided curation reveals widespread misannotation of Asgard archaeal 16S rRNA gene sequences in public databases.

Accurate taxonomic assignment of 16S rRNA gene sequences is essential for the reliable interpretation of microbial community studies based on amplicon sequence data. Yet, it critically depends on the reliability of reference databases such as the Genome Taxonomy Database (GTDB) and the SILVA ribosomal RNA database. Here, we evaluate the consistency of taxonomic annotations within the Asgardarchaeota phylum, a lineage of major evolutionary and ecological interest. Using a phylogenetically curated set of GTDB-derived 16S rRNA gene sequences, we show that most of the affiliations of these sequences were consistent with the phylogenomic placement of their corresponding metagenome-assembled genomes (MAGs), although a small fraction of them exhibited clear inconsistencies likely resulting from erroneous binning to MAGs. In contrast, phylogenetic analyses of SILVA-derived 16S rRNA gene sequences including curated reference sequences revealed widespread taxonomic misannotation and/or limited resolution of taxon assignment. Specifically, many sequences annotated as Odinarchaeales robustly clustered within Lokiarchaeia, Heimdallarchaeia, Hermodarchaeia, or Sifarchaeia, leading to an artificial inflation of Odinarchaeales assignments and potentially biased ecological interpretations. To mitigate these issues, we constructed a curated reference dataset of Asgardarchaeota 16S rRNA gene sequences and generated phylogenetically validated taxonomic assignments across clustered entries, providing a resource for improved classification of environmental sequences. Our results demonstrate that widely used reference databases can contain systematic annotation errors that propagate across studies and distort ecological inference. Although illustrated using Asgard archaea, these limitations are likely pervasive across understudied microbial diversity, highlighting the need for routine phylogenetic validation and systematic curation of reference datasets.