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v0.1.0 - First release of the mokume

28 Jan 10:30
@ypriverol ypriverol
v0.1.0
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🎉 mokume v0.1.0 — First Release

We're excited to announce the first official release of mokume, a comprehensive proteomics quantification library for the quantms ecosystem!

What is mokume?

The name comes from mokume-gane (木目金), a Japanese metalworking technique that fuses multiple metal layers into distinctive patterns — similar to how this library melds peptide intensities into unified protein expression profiles.

mokume is the evolution of ibaqpy, now extended to support a broader range of protein quantification methods beyond iBAQ.

✨ Key Features

Multiple Quantification Methods

  • iBAQ — Intensity-Based Absolute Quantification
  • Top3 / TopN — Average of N most intense peptides
  • MaxLFQ — Delayed normalization with parallelization (auto-uses DirectLFQ when available)
  • DirectLFQ — Intensity traces with hierarchical alignment (optional)
  • Sum — Sum of all peptide intensities

Comprehensive Normalization

  • Feature-level normalization (median, mean, IQR)
  • Peptide-level normalization (global median, condition median)
  • Batch correction via ComBat

Flexible Preprocessing Filters

  • Intensity-based filtering (min intensity, CV threshold, quantile)
  • Peptide-level filters (length, charge state, modifications, missed cleavages)
  • Protein-level filters (contaminants, decoys, FDR, coverage)
  • Run/sample QC filters (missing rate, correlation, min features)
  • YAML/JSON configuration support with CLI overrides

Additional Capabilities

  • TPA (Total Protein Approach) calculation
  • ProteomicRuler for copy number and concentration estimation
  • AnnData export for single-cell analysis workflows
  • QC report generation with visualizations

📦 Installation

# Basic installation
pip install mokume

# With optional dependencies
pip install mokume[directlfq]    # DirectLFQ support
pip install mokume[plotting]     # Visualization support
pip install mokume[all]          # All optional dependencies

🚀 Quick Start

CLI Usage

# iBAQ quantification
mokume peptides2protein --method ibaq -f proteome.fasta -p peptides.csv -o proteins.tsv

# MaxLFQ quantification
mokume peptides2protein --method maxlfq --threads 4 -p peptides.csv -o proteins.tsv

# Feature to peptide normalization
mokume features2peptides -p features.parquet -s experiment.sdrf.tsv --nmethod median -o peptides.csv

Python API

from mokume.quantification import MaxLFQQuantification

maxlfq = MaxLFQQuantification(min_peptides=2, threads=4)
result = maxlfq.quantify(
    peptides,
    protein_column="ProteinName",
    peptide_column="PeptideSequence",
    intensity_column="NormIntensity",
    sample_column="SampleID",
)

📚 Documentation

Full documentation including API reference, tutorials, and example configurations is available in the README.

🙏 Acknowledgments

This release was made possible by the contributions of:

📄 Citation

If you use mokume in your research, please cite:

Zheng P, Audain E, Webel H, Dai C, Klein J, Hitz MP, Sachsenberg T, Bai M, Perez-Riverol Y. Ibaqpy: A scalable Python package for baseline quantification in proteomics leveraging SDRF metadata. J Proteomics. 2025;317:105440. doi: 10.1016/j.jprot.2025.105440.

📝 License

mokume is released under the MIT License.

Full Changelog: https://github.com/bigbio/mokume/commits/v0.1.0

We welcome feedback, bug reports, and contributions! Please open an issue or pull request on GitHub.

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