{"doi":"10.1128/msystems.01003-22","title":"Vaginal Microbiome Metagenome Inference Accuracy: Differential Measurement Error according to Community Composition","abstract":"Compared to taxonomic composition, the functional potential within a bacterial community is more relevant to establishing mechanistic understandings and causal relationships between the microbiome and health outcomes. Metagenome inference attempts to bridge the gap between 16S rRNA gene amplicon sequencing and whole-metagenome sequencing by predicting a microbiome's gene content based on its taxonomic composition and annotated genome sequences of its members. Metagenome inference methods have been evaluated primarily among gut samples, where they appear to perform fairly well. Here, we show that metagenome inference performance is markedly worse for the vaginal microbiome and that performance varies across common vaginal microbiome community types. Because these community types are associated with sexual and reproductive outcomes, differential metagenome inference performance will bias vaginal microbiome studies, obscuring relationships of interest. Results from such studies should be interpreted with substantial caution and the understanding that they may over- or underestimate associations with metagenome content.","journal":"mSystems","year":2023,"id":380304,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9514,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":29081,"name":"Anthony A. Fodor","orcid":"0000-0002-9305-4264","position":1,"is_corresponding":false},{"id":459404,"name":"Jennifer E. Balkus","orcid":"0000-0002-9950-2523","position":2,"is_corresponding":false},{"id":422498,"name":"Angela Zhang","orcid":"0000-0002-4953-982X","position":3,"is_corresponding":false},{"id":59345,"name":"Myrna G. Serrano","orcid":"0000-0003-4327-0193","position":4,"is_corresponding":false},{"id":29050,"name":"Gregory A. Buck","orcid":"0000-0003-4621-3987","position":5,"is_corresponding":false},{"id":339701,"name":"Stephanie M. Engel","orcid":"0000-0002-3942-1246","position":6,"is_corresponding":false},{"id":318790,"name":"Michael C. Wu","orcid":"0000-0002-3357-6570","position":7,"is_corresponding":false},{"id":404190,"name":"Shan Sun","orcid":"0000-0003-0349-2664","position":8,"is_corresponding":false},{"id":490002,"name":"Kayla A. Carter","orcid":"0000-0002-4836-0932","position":0,"is_corresponding":true}],"reference_count":58,"raw_metadata":null,"created_at":"2026-07-19T01:17:00.789848Z","pmid":"36975801","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}