proteomics-data-import

name	proteomics-data-import
description	Load when ingesting a MaxQuant `proteinGroups.txt`, FragPipe `combined_protein.tsv`, DIA-NN report, or generic CSV / TSV protein-quantification table — normalises columns to a standard schema, emits `tables/proteins.csv`. Skip when raw spectra are the input (run the search engine first) or when the file is already OmicsClaw schema.
version	0.5.0
author	OmicsClaw
license	MIT
tags	["proteomics","import","maxquant","fragpipe","diann","spectronaut"]
requires	["pandas","numpy"]

proteomics-data-import

When to use

The user has a search-engine output (MaxQuant proteinGroups.txt, FragPipe combined_protein.tsv, DIA-NN main report, or a generic CSV / TSV protein table) and wants it normalised into OmicsClaw's standard schema (lowercase protein_id plus LFQ_<sample> / Int_<sample> intensity columns derived from MaxQuant's LFQ intensity ... / Intensity ... headers). Pick the format with --format {maxquant,fragpipe,diann,generic} (default maxquant).

For raw MS spectra (mzML / RAW), run a search engine first (MaxQuant / FragPipe / DIA-NN) and feed THIS skill the resulting table.

Inputs & Outputs

Input	Format	Required
Search-engine output	proteinGroups.txt (MaxQuant), combined_protein.tsv (FragPipe), report.tsv (DIA-NN), or generic .csv / .tsv	yes (unless --demo)
Format	--format {maxquant,fragpipe,diann,generic} (default maxquant)	no

Output	Path	Notes
Normalised proteins	tables/proteins.csv	OmicsClaw schema: lowercase protein_id, gene_name, plus LFQ_<sample> / Int_<sample> intensity columns (proteomics_data_import.py:85)
Report	report.md + result.json	always

Flow

1. Load input (--input <file>) or generate a demo MaxQuant-shaped file (--demo).
2. Dispatch to the format-specific importer (proteomics_data_import.py:164-174 _dispatch_import); supported keys are maxquant, fragpipe, diann, generic.
3. Rename columns: LFQ intensity <sample> → LFQ_<sample> and Intensity <sample> → Int_<sample> (proteomics_data_import.py:85); Majority protein IDs → protein_id; Gene names → gene_name; etc.
4. Write tables/proteins.csv (proteomics_data_import.py:284) + report.md + result.json (:299).

Gotchas

• --format value must match _dispatch_import keys exactly. proteomics_data_import.py:166-171 registers maxquant, fragpipe, diann, generic. An unknown value raises ValueError("Unsupported format: ... Supported: ['maxquant', 'fragpipe', 'diann', 'generic']") at :173. There is no spectronaut importer despite the legacy SKILL.md mention — use --format generic for Spectronaut and rename columns yourself.
• --input REQUIRED unless --demo. proteomics_data_import.py:275 raises ValueError("--input required when not using --demo"). Non-existent paths raise FileNotFoundError from pd.read_csv.
• Output schema is LOWERCASE. Column renaming targets protein_id, intensity_<sample>, gene_name etc. Downstream skills (proteomics-quantification, proteomics-de) assume this casing. Verify after import with head tables/proteins.csv.
• No deduplication of contaminants / decoys. Contaminant (CON_*) and decoy (REV_*) rows are passed through unchanged. Filter them upstream with the search engine's --keep-contaminants false flag, or add a downstream df = df[~df["protein_id"].str.startswith(("CON_", "REV_"))] step.

Key CLI

# Demo (synthetic MaxQuant-style)
python omicsclaw.py run proteomics-data-import --demo --output /tmp/import_demo

# Real MaxQuant output
python omicsclaw.py run proteomics-data-import \
  --input proteinGroups.txt --output results/ --format maxquant

# FragPipe combined_protein
python omicsclaw.py run proteomics-data-import \
  --input combined_protein.tsv --output results/ --format fragpipe

# DIA-NN main report
python omicsclaw.py run proteomics-data-import \
  --input report.tsv --output results/ --format diann

# Generic / Spectronaut (rename columns yourself first)
python omicsclaw.py run proteomics-data-import \
  --input my_table.csv --output results/ --format generic

See also

• references/parameters.md — every CLI flag
• references/methodology.md — per-format column-mapping rules
• references/output_contract.md — tables/proteins.csv schema
• Adjacent skills: proteomics-ms-qc (downstream — QC the imported table), proteomics-quantification (downstream — compute LFQ / iBAQ / spectral count), proteomics-identification (parallel — peptide-level summary), proteomics-de (downstream — differential abundance after import)