{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Krinos AI"],"funding":["U.S. Department of Energy (DOE)"],"pagination":["9873"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11564645"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["15(1)"],"pubmed_abstract":["Meta-omics is commonly used for large-scale analyses of microbial eukaryotes, including species or taxonomic group distribution mapping, gene catalog construction, and inference on the functional roles and activities of microbial eukaryotes in situ. Here, we explore the potential pitfalls of common approaches to taxonomic annotation of protistan meta-omic datasets. We re-analyze three environmental datasets at three levels of taxonomic hierarchy in order to illustrate the crucial importance of database completeness and curation in enabling accurate environmental interpretation. We show that taxonomic membership of sequence clusters estimates community composition more accurately than returning exact sequence labels, and overlap between clusters can address database shortcomings. Clustering"],"journal":["Nature communications"],"pubmed_title":["Missing microbial eukaryotes and misleading meta-omic conclusions."],"pmcid":["PMC11564645"],"funding_grant_id":["DE-AC02-05CH11231","DE-SC0020347"],"pubmed_authors":["Mars Brisbin M","Alexander H","Cohen NR","Hu SK","Follows MJ","Schulz F","Rynearson TA","Krinos AI"],"additional_accession":[]},"is_claimable":false,"name":"Missing microbial eukaryotes and misleading meta-omic conclusions.","description":"Meta-omics is commonly used for large-scale analyses of microbial eukaryotes, including species or taxonomic group distribution mapping, gene catalog construction, and inference on the functional roles and activities of microbial eukaryotes in situ. Here, we explore the potential pitfalls of common approaches to taxonomic annotation of protistan meta-omic datasets. We re-analyze three environmental datasets at three levels of taxonomic hierarchy in order to illustrate the crucial importance of database completeness and curation in enabling accurate environmental interpretation. We show that taxonomic membership of sequence clusters estimates community composition more accurately than returning exact sequence labels, and overlap between clusters can address database shortcomings. Clustering","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Nov","modification":"2026-06-03T03:12:27.77Z","creation":"2025-04-06T22:46:17.299Z"},"accession":"S-EPMC11564645","cross_references":{"pubmed":["39543100"],"doi":["10.1038/s41467-024-52212-w"]}}