proteomics-quantification

name	proteomics-quantification
description	Protein/peptide quantification (LFQ, TMT, DIA) using MaxQuant LFQ, DIA-NN, or Skyline.
version	0.1.0
author	OmicsClaw
license	MIT
tags	["proteomics","quantification","LFQ","TMT","DIA","DIA-NN"]
metadata	{"omicsclaw":{"domain":"proteomics","emoji":"📏","trigger_keywords":["protein quantification","LFQ","TMT","DIA","DIA-NN","Skyline"],"allowed_extra_flags":[],"legacy_aliases":["quantification"],"saves_h5ad":false}}

📏 Protein Quantification

Protein and peptide quantification for label-free (LFQ), isobaric labelling (TMT), and DIA workflows.

CLI Reference

python omicsclaw.py run proteomics-quantification --demo
python omicsclaw.py run proteomics-quantification --input <data.csv> --output <dir>

Why This Exists

• Without it: Peak heights vary wildly due to ion suppression, ionization efficiency, and LC drift
• With it: Powerful algorithms (MaxLFQ, DIA-NN) normalize intensities across large cohorts
• Why OmicsClaw: Provides a standard programmatic interface to multiple quantification paradigms (LFQ, TMT, DIA)

Workflow

1. Calculate: Map identified sequences to MS1 or MS2 extraction windows.
2. Execute: Integrate peak areas and apply cross-run retention time alignment.
3. Assess: Perform global normalization (median centering, quantile).
4. Generate: Output structural intensity matrices.
5. Report: Tabulate key quantification yield metrics.

Example Queries

• "Quantify proteins using MaxQuant LFQ"
• "Run DIA-NN on these wiff files"

Output Structure

output_directory/
├── report.md
├── result.json
├── quantified.csv
├── figures/
│   └── normalization_boxplot.png
├── tables/
│   └── intensity_matrix.csv
└── reproducibility/
    ├── commands.sh
    ├── requirements.txt
    └── checksums.sha256

Safety

• Local-first: Strict offline processing without external upload.
• Disclaimer: Requires OmicsClaw reporting structures and disclaimers.
• Audit trail: Hyperparameters and operational flow states are logged fully.

Integration with Orchestrator

Trigger conditions:

• Automatically invoked dynamically based on tool metadata and user intent matching.

Chaining partners:

• peptide-id — Upstream sequence identification
• differential-abundance — Downstream statistical execution

Citations

• MaxQuant LFQ
• DIA-NN