LLMs-Universal-Life-Science-and-Clinical-Skills- proteomics-data-import
install
source · Clone the upstream repo
git clone https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills- "$T" && mkdir -p ~/.claude/skills && cp -r "$T/Skills/Proteomics/proteomics-data-import" ~/.claude/skills/mdbabumiamssm-llms-universal-life-science-and-clinical-skills-proteomics-data-im && rm -rf "$T"
manifest:
Skills/Proteomics/proteomics-data-import/SKILL.mdsource content
📥 Proteomics Data Import
Import and convert proteomics data from various formats (MaxQuant, DIA-NN, Spectronaut output) into standardised tables.
CLI Reference
python omicsclaw.py run proteomics-data-import --demo python omicsclaw.py run proteomics-data-import --input <proteinGroups.txt> --output <dir>
Why This Exists
- Without it: Each search engine (MaxQuant, DIA-NN, FragPipe) outputs completely different table structures
- With it: Raw vendor and search outputs are unified into a standard long-format intensity matrix
- Why OmicsClaw: Provides a single universal ingestion point before statistical testing
Workflow
- Calculate: Parse header shapes and metadata dictionaries.
- Execute: Melt and reshape raw search engine text files.
- Assess: Perform basic missing value logic checks.
- Generate: Output normalized H5AD or standard CSV objects.
- Report: Tabulate key protein/peptide groups parsed.
Example Queries
- "Convert my MaxQuant proteinGroups.txt into a standard format"
- "Import DIA-NN evidence tables"
Output Structure
output_directory/ ├── report.md ├── result.json ├── processed.csv ├── figures/ │ └── intensity_distribution.png ├── tables/ │ └── import_summary.csv └── reproducibility/ ├── commands.sh ├── environment.yml └── 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:
— Downstream quality profilingms-qc
— Downstream statistical executiondifferential-abundance