bio-proteomics-protein-inference
Protein grouping and inference from peptide identifications. Use when resolving protein ambiguity from shared peptides. Handles protein groups and protein-level FDR control using parsimony and probabilistic approaches.
Profile →npx skills add majiayu000/claude-skill-registry --skill protein-inference-gptomics-bioskills --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Protein Inference ## The Protein Inference Problem Peptides can map to multiple proteins (shared peptides), making protein identification ambiguous. ```python # Example: Peptide mapping peptide_to_proteins = { 'PEPTIDEK': ['P12345', 'P67890'], # Shared between paralogs 'UNIQUER': ['P12345'], # Unique to P12345 'ANOTHERONE': ['P12345'], # Unique to P12345 'SHAREDK': ['P67890', 'P11111'], # Shared } # P12345 has 2 unique peptides -> confident identification # P67890 has 0 unique peptides -> subset, may be grouped with P12345 ``` ## Parsimony Principle ```python def apply_parsimony(peptide_protein_map): '''Find minimal set of proteins explaining all peptides''' proteins = set() for prots in peptide_protein_map.values(): proteins.update(prots) protein_peptides = {p: set() for p in proteins} for pep, prots in peptide_protein_map.items(): for p in prots: protein_peptides[p].add(pep) covered_peptides = set() selected_proteins = [] # Greedy: select protein covering most uncovered peptides while covered_peptides != set(peptide_protein_map.keys()): best_protein = max(protein_peptides.keys(), key=lambda p: len(protein_peptides[p] - covered_peptides)) new_coverage = protein_peptides[best_pro
- The Protein Inference Problem
- Parsimony Principle
- Protein Groups
- pyOpenMS Protein Inference
- R: Protein Inference with ProteinInference
- Protein-Level FDR
- Related Skills
What does the bio-proteomics-protein-inference skill do?
Protein grouping and inference from peptide identifications. Use when resolving protein ambiguity from shared peptides. Handles protein groups and protein-level FDR control using parsimony and probabilistic approaches.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill protein-inference-gptomics-bioskills --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.