| name | proteomics-identification |
| description | Load when summarising peptide identifications (PSM count, unique peptide count, distinct protein count, score / charge distributions) from a peptide-level CSV produced by MaxQuant / FragPipe / DIA-NN. Skip when raw spectra are the input (run a search engine first) or when working with protein-quantification tables (use `proteomics-ms-qc`). |
| version | 0.5.0 |
| author | OmicsClaw |
| license | MIT |
| tags | ["proteomics","identification","peptides","psm","maxquant","msgf"] |
| requires | ["pandas","numpy"] |
proteomics-identification
When to use
The user has a peptide-level CSV (from MaxQuant peptides.txt,
FragPipe combined_peptide.tsv, DIA-NN, or any peptide table with
columns including peptide / protein / optionally score /
charge) and wants identification summary statistics: total PSM
count, unique peptide count, distinct protein count, optional
median score, optional charge distribution.
This skill does NOT run a search engine — it summarises a peptide
table that already exists. The --fdr flag is recorded as
metadata only (no FDR re-thresholding is performed).
Inputs & Outputs
| Input | Format | Required |
|---|
| Peptide table | .csv with at least peptide and protein columns; optional score, charge, and any one of qvalue / q-value / q_value / PEP / pep / fdr for FDR filtering | yes (unless --demo) |
| FDR cutoff | --fdr <float> (default 0.01, ACTIVELY filters when an FDR column exists) | no |
| Spectra count | --n-spectra <int> (demo size hint only — not a CSV column) | no |
| Output | Path | Notes |
|---|
| Peptides | tables/peptides.csv | FDR-filtered peptide table |
| Report | report.md + result.json | summary["n_psms"], summary["n_unique_peptides"], summary["n_proteins"], summary["id_rate"]; summary["median_score"] (if score present); summary["charge_distribution"] (if charge present) |
Flow
- Load CSV (
--input <peptides.csv>) or generate a demo peptide table (--demo).
- Filter by FDR via
filter_by_fdr (proteomics_identification.py:105-126) — searches columns in order qvalue → q-value → q_value → PEP → pep → fdr; if NONE found, logs a warning at :117 and passes through unchanged.
- Compute n_psms, n_unique_peptides, n_proteins, id_rate; optionally median
score (proteomics_identification.py:147) and charge distribution (:151).
- Write
tables/peptides.csv (proteomics_identification.py:235) + report.md + result.json (:241).
Gotchas
- No search engine is invoked. This skill summarises an existing peptide CSV — it does NOT run MaxQuant / MS-GF+ / Comet / Mascot. Run a search engine upstream and feed the peptide-level CSV here.
--fdr ACTIVELY filters when an FDR column is present. proteomics_identification.py:229 calls filter_by_fdr(peptides, fdr_threshold=args.fdr). The helper (:105-126) tries columns in order qvalue → q-value → q_value → PEP → pep → fdr. With NONE present, the run only logs a warning at :117 and passes the input through unchanged.--input REQUIRED unless --demo. proteomics_identification.py:223 raises ValueError("--input required when not using --demo").- Optional columns are silently skipped when absent. A CSV without
score omits summary["median_score"]; without charge omits summary["charge_distribution"]. Inspect the JSON before writing downstream consumers that assume those keys exist.
- Column names must match exactly (lowercase):
peptide, protein, score, charge. MaxQuant evidence.txt ships with Sequence / Proteins / Score / Charge — rename to lowercase first (e.g. df.rename(columns={"Sequence": "peptide", "Proteins": "protein", "Score": "score", "Charge": "charge"})).
Key CLI
python omicsclaw.py run proteomics-identification --demo --output /tmp/id_demo
python omicsclaw.py run proteomics-identification \
--input peptides.csv --output results/ --fdr 0.01
See also
references/parameters.md — every CLI flagreferences/methodology.md — PSM / peptide / protein semantics, FDR conventionsreferences/output_contract.md — tables/peptides.csv schema- Adjacent skills:
proteomics-data-import (upstream — protein-level table normalisation), proteomics-ms-qc (parallel — protein-table QC), proteomics-quantification (downstream — LFQ / iBAQ / spectral count), proteomics-de (downstream — differential abundance)
