proteomics-identification

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

1. Load CSV (--input <peptides.csv>) or generate a demo peptide table (--demo).
2. 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.
3. Compute n_psms, n_unique_peptides, n_proteins, id_rate; optionally median score (proteomics_identification.py:147) and charge distribution (:151).
4. 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)