Agent Skills › Runchuan-BU/BioClaw › proteomics

proteomics

GitHub

提供质谱蛋白质组学数据的质量控制、定量、差异分析及可视化工作流。支持DDA/DIA等实验模式，涵盖蛋白表清洗、缺失值处理及统计检验，生成标准化结果与图表。

container/skills/proteomics/SKILL.md Runchuan-BU/BioClaw

Trigger Scenarios

需要蛋白质组学数据质量控制进行蛋白质定量或差异丰度分析检查批次效应和重复性质量

Install

npx skills add Runchuan-BU/BioClaw --skill proteomics -g -y

More Options

Non-standard path

npx skills add https://github.com/Runchuan-BU/BioClaw/tree/main/container/skills/proteomics -g -y

Use without installing

npx skills use Runchuan-BU/BioClaw@proteomics

指定 Agent (Claude Code)

npx skills add Runchuan-BU/BioClaw --skill proteomics -a claude-code -g -y

安装 repo 全部 skill

npx skills add Runchuan-BU/BioClaw --all -g -y

预览 repo 内 skill

npx skills add Runchuan-BU/BioClaw --list

SKILL.md

Frontmatter

{
    "name": "proteomics",
    "tool_type": "python",
    "description": "Mass spectrometry proteomics QC, quantification, comparative analysis, and export for DDA, DIA, and protein-level result tables.",
    "primary_tool": "pyopenms"
}

Proteomics

Version Compatibility

Reference examples assume:

pyopenms 3.0+
pandas 2.2+
numpy 1.26+
seaborn 0.13+

Overview

Use this skill when the user needs:

proteomics QC
protein table cleanup
replicate review
differential abundance analysis
publication-ready proteomics figures

When To Use This Skill

MaxQuant, FragPipe, DIA-NN, or similar outputs exist
the task is protein-level quantification or comparative proteomics
missingness, batch effects, and replicate quality need review before interpretation

Quick Route

DDA and DIA should not be treated identically
protein-level tables should remain distinct from peptide-level tables
QC comes before differential analysis

Progressive Disclosure

Read technical_reference.md for assay branching, QC interpretation, and missingness handling.
Read commands_and_thresholds.md for table-loading patterns, QC thresholds, and output conventions.

Expected Inputs

protein or peptide result table
sample metadata
assay context: DDA, DIA, PTM-enriched, or targeted

Expected Outputs

results/protein_abundance.tsv
qc/proteomics_qc_summary.tsv
figures/correlation_heatmap.pdf
figures/missingness.pdf
results/differential_proteins.tsv

Starter Pattern

import pandas as pd

protein_df = pd.read_csv("protein_groups.tsv", sep="\t")
sample_cols = [c for c in protein_df.columns if c.startswith("LFQ intensity")]
matrix = protein_df[sample_cols].replace(0, pd.NA)
qc = pd.DataFrame({
    "n_proteins": matrix.notna().sum(),
    "missing_pct": matrix.isna().mean() * 100,
})
qc.to_csv("qc/proteomics_qc_summary.tsv", sep="\t")

Workflow

1. Clarify assay and table level

DDA versus DIA
peptide versus protein table
PTM-enriched versus unenriched data

2. Run QC before comparisons

Inspect:

missingness
replicate correlation
batch effects
intensity distributions

3. Normalize and summarize consistently

Keep the normalization approach explicit and do not collapse peptides into proteins without documenting the rule.

4. Perform comparative analysis

Use replicate-aware differential abundance with clear filtering and missingness policy.

5. Export interpretable artifacts

Save both the cleaned abundance matrix and the differential results table.

Output Artifacts

results/
├── protein_abundance.tsv
└── differential_proteins.tsv
qc/
└── proteomics_qc_summary.tsv
figures/
├── correlation_heatmap.pdf
├── missingness.pdf
└── intensity_density.pdf

Quality Review

overall missingness > 30% should trigger caution
technical replicate correlation should usually be > 0.9
biological replicate correlation much below 0.8 deserves review
do not trust differential calls before batch structure and missingness are understood