Missing microbial eukaryotes and misleading meta-omic conclusions

Abstract

Meta-omics has become commonplace in the study of microbial eukaryotes. The explosion of available data has inspired large-scale analyses, including species or taxonomic group distribution mapping, gene catalog construction, and inference on the functional roles and activities of microbial eukaryotes in situ. However, genome and transcriptome databases are prone to misannotation biases, and meta-omic inventories may have no recoverable taxonomic annotation for more than half of assembled contigs or predicted proteins. Direct mapping solely to organisms of interest might introduce a problematic misattribution bias, while full databases can annotate any cataloged organism but may be imbalanced between taxa. Here, we explore the potential pitfalls of common approaches to taxonomic annotation of protistan meta-omic datasets. We argue that ongoing curation of genetic resources is critical in accurately annotating protists in situ in meta-omic datasets. Moreover, we propose that precise taxonomic annotation of meta-omic data is a clustering problem rather than a feasible alignment problem. We show that taxonomic membership of sequence clusters demonstrates more accurate estimated community composition than returning exact sequence labels, and overlap between clusters can address database shortcomings. Clustering approaches can be applied to diverse environments while continuing to exploit the wealth of annotation data collated in databases, and database selection and evaluation is a critical part of correctly annotating protistan taxonomy in environmental datasets. We re-analyze three environmental datasets at three levels of taxonomic hierarchy in order to illustrate the critical importance of both database completeness and curation in enabling accurate environmental interpretation.

Microeukaryote metabolism across the western North Atlantic Ocean revealed through autonomous underwater profiling

“…In addition to these taxonomic distinctions between biochemical pools, we observed an unexpected difference in the proportion of the eukaryotic communities able to be confidently classified 31 . Approximately 21% of transcripts on average were associated with ORFs unclassified at the supergroup level, while an average of 38% protein spectral counts were associated with lineage-conflicted ORFs (“Not Annotated (NA)”; Fig.…”

“…2). These are ORFs that were similar to multiple, distantly related references in our taxonomic database such that they were not possible to confidently assign using our sequence homology and LCA classification approach 31 .…”

“…3B). It is also worth noting that we are less confident in fine-scale rhizaria taxonomic assignments given their poor representation in existing databases 31 .…”