MetaUmbra

Genome-level presence inference from metaproteomic peptides

MetaUmbra performs genome-level presence inference from metaproteomic peptide lists. It combines sample-depth-aware genome-unique peptide support with weighted shared peptide evidence to identify statistically supported microbial genomes and generate interpretable presence rankings.

Main features

• Evaluate candidate genome support from metaproteomic peptide tables
• Build genome-specific theoretical peptide references from protein FASTA files
• Support user-defined genome collections, including isolate genomes, strain panels, and MAG catalogs
• Use both unique and shared peptide evidence for genome presence inference
• Calibrate genome-unique peptide counts against sample-specific weak-background genomes by default
• Score multi-sample inputs per sample or user-defined analysis unit without repeated genome digest scans
• Report genome-level p-values, BH-adjusted q-values, and presence scores
• Provide GUI, command-line, and Python workflow support
• Support peptide tables from common metaproteomics workflows such as DIA-NN and MaxQuant

Workflow overview

Installation

MetaUmbra requires Python 3.10 or newer. Installation is available via pip from PyPI.

# Install with all features (GUI, parquet support)
pip install metaumbra[all]

The default GUI extra uses PySide6. To run the GUI with PyQt5 instead, install metaumbra[gui-pyqt5].

or

# Install with core features only
pip install metaumbra

Usage

MetaUmbra can be used through either the graphical interface or the command line.

For a detailed walkthrough, including input formats, CLI examples, output interpretation, and troubleshooting, see the MetaUmbra Usage Guide.

Graphical interface

metaumbra-gui

The GUI supports FASTA digestion, peptide table loading, genome presence scoring, and result export.

Command line

MetaUmbra provides separate commands for the main workflow steps:

metaumbra digest --help
metaumbra score --help
metaumbra extract-parquet --help

A typical workflow is:

metaumbra digest ...
metaumbra score ...

Use metaumbra extract-parquet ... to convert DIA-NN parquet reports to peptide TSV files before scoring.

Input

MetaUmbra requires:

• Protein FASTA files, with one FASTA file per genome
• An observed peptide table containing peptide sequences

Optional inputs include peptide scores, peptide-level error values, decoy flags, and genome lineage annotations.

All scoring uses one analysis-unit workflow. Select --unit-mode all-samples, per-sample, or metadata; each option builds a sample-to-unit assignment and invokes the same per-unit p/q-value engine. The cohort-wide all-samples mode uses a moderately more permissive empirical-background calibration profile, recorded in the run summary, so pooled strong signals do not inflate their own null background.

Output

The requested --output is a unified results directory.

Default output files:

• unit_genome_results.tsv: one row per analysis_unit_id x genome_id, including q-values and threshold flags.
• cohort_genome_summary.tsv: recurrence across analysis units.
• sample_unit_mapping.tsv: the validated sample assignment.
• genome_selection_manifest.json: the only downstream genome-selection interface.
• artifacts/: run metadata, logs, caches, and optional diagnostics.

genome_selection_manifest.json uses metaumbra.genome_selection_manifest.v1 and maps every unit to sample_ids, q<=0.05 genomes, and the stricter q<=0.01 subset. Detailed scores stay in unit_genome_results.tsv.

Each scoring run records artifacts/run_parameters.json, run.log, run_status.json, and run_summary.json. Use --export-diagnostics for heavier audit tables under artifacts/diagnostics/.

In the current implementation, peptide presence within an analysis unit is defined as the union of sample-level peptide presence across samples assigned to that unit. Unit-level p-values combine per-unit shared knockoff evidence (pvalue_shared) with the selected per-unit unique-evidence model (pvalue_unique) using Fisher's method, then apply BH correction separately within each analysis unit.

Key output columns include:

Citation

If you use MetaUmbra, please cite:

Wu Q, Ning Z, Zhang A, Cheng K, Figeys D. MetaUmbra: Statistically Controlled Genome-Level Presence Inference from Metaproteomic Peptides.[J]. bioRxiv, 2026.04.29.721689.

A formal citation will be added after publication.

Contact

For questions or issues, please use the GitHub issue tracker or contact the corresponding author listed in the associated manuscript.