jean — Molecular Biology & Genomics

bayesian_protein_inference

Read-only

Bayesian protein inference, Epifany (validated against OpenMS BayesianProteinInferenceAlgorithm on PXD000001): the posterior probability that each protein, each indistinguishable group and each peptide-spectrum match is present, from the matches' posterior error probabilities under a network in which present proteins emit their peptides. Shared peptides are weighed between their proteins instead of being counted for each.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`beta`	No	spurious emission probability β: the chance a peptide is reported with none of its proteins present.
`psms`	Yes	the matches as a table with a header row naming spectrum, peptide, charge and a posterior error probability column (tab- or comma-separated); peptides are ProForma.
`alpha`	No	peptide emission probability α: the chance a present protein yields a given one of its peptides.
`gamma`	No	protein prior γ: the chance a protein is present before any evidence.
`_files`	No	The project: record references shaped like _subject. Their records become library documents a tool names in sequences/parts/document; a trace or alignment is found by file name.
`_subject`	No	The record to act on: exactly one of sequence, accession (+source), sample (an id from list_samples) or file ({name, text} or {name, base64}); record_index picks one record of several.
`database`	Yes	the protein database searched, as FASTA; the accession is the header up to the first space.
`psmCutoff`	No	drop matches whose probability (1 − PEP) is below this.
`gridSearch`	No	choose α, β and γ instead: run OpenMS's grid (α 0.1–0.8 × β 0.01–0.4 × γ 0.2–0.7) and keep the combination whose protein posteriors best agree with target-decoy FDR and separate targets from decoys; needs decoy proteins.
`decoyPrefix`	No	accession prefix marking a decoy protein, for the grid search.	DECOY_
`scoreColumn`	No	the column holding each match's posterior error probability (PEP, 0 to 1).	pep
`ilEquivalent`	No	treat isoleucine and leucine (and a database J) as the same residue when mapping peptides.
`peptidePrior`	No	peptide prior π, dividing each match's probability back out of its evidence.
`appendReversedDecoys`	No	add the reversed sequence of every database protein as a decoy (accession decoyPrefix + accession), for a database searched with reversed decoys that holds only targets.
`maxAmbiguousResidues`	No	how many ambiguous database residues (B, J, Z, X) one peptide match may resolve.

Output Schema

TableJSON Schema

Name	Required	Description
`grid`	Yes	with the grid search: every α, β, γ tried, with its FDR-curve difference, ROC-100 and objective (skipped when improbable)
`count`	Yes	proteins reported
`groups`	Yes	indistinguishable protein groups, most probable first: probability that at least one member is present, and accessions
`proteins`	Yes	one row per protein: accession, posterior probability, matches counted, sequence coverage (%)
`psmPosteriors`	Yes	the matches inference used (the best per peptide): spectrum, peptide, prior (1 − PEP) and posterior

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 20, 2026

TDQS

A3.7/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare the operation read-only, non-destructive, and not open-world. The description adds substantive behavioral context: it explains the probabilistic network model, how shared peptides are weighted, and that output consists of posterior probabilities. It does not discuss computational cost or caveats, but it goes well beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two dense sentences, front-loading the method and the output quantity before explaining the shared-peptide treatment. The parenthetical validation detail is useful but slightly expendable; overall it is efficient and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given an output schema exists, the description need not explain return values. It also benefits from complete schema coverage for the 15 parameters, which covers required inputs and defaults. The description supplies the conceptual model but could more explicitly point to required inputs like psms and database, though the schema handles that adequately.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents all 15 parameters, including the roles of alpha, beta, gamma, and gridSearch. The tool description adds no further parameter-specific semantics beyond what is already in the schema, so the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific method and output: Bayesian protein inference using Epifany, yielding posterior probabilities for proteins, indistinguishable groups, and peptide-spectrum matches. However, it does not explicitly distinguish this tool from the sibling infer_proteins, so an agent must infer the distinction from the method name.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Use is implied by the Bayesian inference framing and the parameter details for grid search, but the description never states when to choose this over alternatives such as infer_proteins. There is no explicit when-to-use or when-not-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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