mokume

A comprehensive proteomics quantification library for the quantms ecosystem.

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.

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• Multiple Quantification Methods

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  iBAQ, TopN, MaxLFQ, DirectLFQ, Sum, Ratio — choose the right method for your experiment.

  Quantification methods

• Flexible Normalization

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  Feature-level, sample-level, hierarchical, and TMM normalization with a unified pipeline.

  Normalization

• Batch Correction

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  Remove technical variation while preserving biological signal using ComBat.

  Batch correction

• IRS for Multi-plex TMT

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  Internal Reference Scaling with automatic reference detection from SDRF.

  IRS normalization

• Preprocessing Filters

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  Comprehensive QC filters configurable via YAML or CLI.

  Preprocessing

• One-Step Pipeline

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  Go from feature parquet to protein intensities in a single command.

  Quick start

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Quick Example

CLIPython

# MaxLFQ quantification with normalization
mokume features2proteins \
    -p features.parquet \
    -o proteins.csv \
    -s experiment.sdrf.tsv \
    --quant-method maxlfq

from mokume.pipeline import QuantificationPipeline, PipelineConfig
from mokume.pipeline.config import InputConfig, QuantificationConfig

config = PipelineConfig(
    input=InputConfig(parquet="features.parquet", sdrf="experiment.sdrf.tsv"),
    quantification=QuantificationConfig(method="maxlfq"),
)
pipeline = QuantificationPipeline(config)
proteins = pipeline.run()

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Part of the quantms Ecosystem

mokume is a core component of the quantms proteomics analysis platform, providing the quantification engine that powers protein-level analysis from mass spectrometry data.

Ecosystem Tool	Purpose
quantms	Nextflow pipeline for quantitative proteomics
qpx	Data format and conversion tools
mokume	Protein quantification and normalization