MetaUmbra: Statistically Controlled Genome-Level Presence Inference from Metaproteomic Peptides

Abstract

Taxonomic interpretation of metaproteomic peptides remains difficult because many peptide sequences are present in proteins from different organisms, reducing taxonomic specificity. Current peptide-centric workflows can report taxonomic summaries or taxon-level confidence scores, but they do not directly provide formal genome-level statistical inference for user-defined genome collections. Here we present MetaUmbra, a tool that derives genome-level statistical evidence from identified peptides. MetaUmbra builds theoretical peptide lists by in silico digestion of genome proteins, matches observed peptides against these references, and evaluates unique and shared peptide evidence within a formal statistical framework that accounts for peptide sharing across the reference panel. In the defined community benchmark SIHUMIx, MetaUmbra recovered the expected genomes without additional significant calls after embedding them in a large gut reference background. In the single-strain benchmark Mix24X, all expected genomes were recovered under near-neighbor and full-background expansion, with additional calls mainly associated with closely related genomes. In a hamster gut genome panel, MetaUmbra further preserved an interpretable ranking of candidate genomes in a dense real-data setting. Together, these results show that MetaUmbra provides statistically controlled genome-level presence inference while accounting for ambiguity arising from shared peptides. MetaUmbra therefore provides a practical framework for converting peptide evidence into genome-level statistical inference in metaproteomics.