metaumbra 1.3.5

Genome-level presence inference from metaproteomic peptide lists.

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.

For multi-sample long tables such as DIA-NN reports, metaumbra score --unit-aware can call peptide presence per raw sample using Precursor.Quantity, aggregate samples into analysis_unit_id groups from an optional metadata table, and export clean per-unit, cohort recurrence, sample mapping, unit call-count, and downstream genome-list outputs by default.

Output

The default output is a concise TSV table containing genome-level evidence and significance values. When unit-aware scoring is enabled, the requested output path contains the main unit-level genome presence result and uses a concise downstream-ready schema.

Default unit-aware output files:

• <requested output>.tsv: one row per analysis_unit_id x genome_id, including unit-specific qvalue and pass_q flags.
• <stem>_cohort_genome_summary.tsv: one row per genome, summarizing recurrence across units.
• <stem>_artifacts/unit_aware/<stem>_sample_unit_mapping.tsv: final sample-to-analysis-unit mapping used for the run.
• <stem>_artifacts/unit_aware/unit_call_counts.tsv: minimal per-unit QC counts.
• <stem>_artifacts/unit_aware/unit_specific_genome_list_q005.tsv: preferred downstream genome-list interface.
• <stem>_artifacts/unit_aware/unit_specific_genome_list_q001.tsv: stricter downstream genome-list interface.

Each scoring run creates <stem>_artifacts/ at startup and records run_parameters.json, run.log, and run_status.json for reproducibility and debugging. The parameter snapshot includes CPU model, logical CPU count, total memory, and platform/architecture metadata. Use --export-diagnostics to write heavier audit and figure-generation outputs such as run_summary.json, full_internal_metrics.tsv, knockoff diagnostics, pooled unit-aware results, redundant unit-level subsets, genome unions, and genome-by-unit matrices. Full unit-aware audit columns are written to unit_aware/unit_genome_presence_full.tsv with --export-diagnostics or unit-aware --return-full-table. In the CLI, use --save-cache to write matched_peptides.pkl; in the GUI, the matching "Save matched-peptide cache" option is enabled by default. When cache reuse is requested, the default matched_peptides.pkl cache is preserved during startup cleanup.

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:

Column	Description
genome_id	Candidate genome identifier
num_peptides_matched	Number of observed peptides matched to the genome
num_peptides_unique	Number of matched peptides unique to the genome
theoretical_unique_peptides	Theoretical peptides unique to this genome among the analyzed genome set, included for hypergeometric-opportunity output
expected_unique_null	Expected observed genome-unique peptides under the selected unique-evidence null
unique_depth_fold	Observed unique peptides divided by expected unique peptides under the null
has_unique_evidence	Whether the genome has at least one observed unique peptide
pvalue_shared	Shared-peptide knockoff p-value
pvalue_unique	Unique-evidence p-value
pvalue	Genome-level p-value
qvalue	BH-adjusted genome-level q-value
presence_score	Ranking score based on q-value

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.