proteomics-data-import

Documents

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.

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How to use this skill

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Source SKILL.md: https://github.com/TianGzlab/OmicsClaw/blob/HEAD/skills/proteomics/proteomics-data-import/SKILL.md

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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

Load input (--input <file>) or generate a demo MaxQuant-shaped file (--demo).
Dispatch to the format-specific importer (proteomics_data_import.py:164-174 _dispatch_import); supported keys are maxquant, fragpipe, diann, generic.
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.
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)