proteomics-identification

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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`).

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

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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

# Demo
python omicsclaw.py run proteomics-identification --demo --output /tmp/id_demo

# Real peptide CSV
python omicsclaw.py run proteomics-identification \
  --input peptides.csv --output results/ --fdr 0.01

See also

references/parameters.md — every CLI flag
references/methodology.md — PSM / peptide / protein semantics, FDR conventions
references/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)