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jean — Molecular Biology & Genomics

copy_number_loh

Read-only

Recompute loss of heterozygosity from an Affymetrix array's own per-marker genotype calls, rather than reading the runs the producer wrote: APT's window method, with the false-positive rate and the assumed genotyping error rate as parameters. Answers where a sample has lost one parental haplotype, including where copy number is normal. Needs a container that states genotype calls — an OncoScan OSCHP does; a CytoScan CYCHP states the set with no rows, and this says so rather than answering empty.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fileNowhich copy-number file in this project, by name. Unset, it is the project's only one — and it refuses rather than guessing when there are several.
alphaNoAPT's `lohCN_alpha`: the false-positive rate the heterozygote cutoff is set against. Raising it makes the window narrower and the method readier to call a run, which is the knob to move when comparing against another caller.
_filesNoThe 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.
_subjectNoThe 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.
error_rateNoAPT's `lohCN_errorrate`: the assumed genotyping error rate, which is what makes a heterozygote inside a homozygous stretch explicable. It is RE-ESTIMATED from the sample's own no-call rate when the genotype confidences reach the no-call threshold, and the output says which value was actually used.
min_genomic_spanNoAPT's `lohCN_minGenomicSpan`: a called run narrower than this many base pairs is dropped. The default is the vendor's 1,000,000.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
runsYesone entry per called run: chromosome (the file's own label), start, end, markers (how many genotyped markers it spans). Coordinates are the marker positions the container states, on whatever assembly it names
windowYeswhat the parameters solved to for THIS sample: markerCount (the window width), hetCutoff (heterozygotes at or above which the window is not LOH), and errorRate (the value used, which differs from the parameter when it was re-estimated from the no-call rate)
summaryYeshow many runs of loss of heterozygosity were called, over how many chromosomes and how many megabases, and how many markers the method could read
accountingYesmarkers (genotype calls read), noCalls (dropped before anything else), evaluated, inSegmentsTooShort (markers in a stretch shorter than the window, left UNCALLED rather than called clear), beyondLastChromosome, segments. The five ways a marker can be spent sum to markers
provenanceYeswhich data set the genotype calls came from, which one the coordinates came from, how many were paired, and the parameters in force. The pairing is checked against the marker each call names, never trusted

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already establish a safe read (readOnlyHint, destructiveHint=false), and the description still adds real behavior: the tool refuses rather than returning an empty answer on a no-row CYCHP, error_rate is re-estimated from the sample's no-call rate with the used value reported in output, and narrow runs are dropped.

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?

Purpose and the recompute-vs-read distinction are front-loaded, and every clause carries information. It is prose-heavy with several subordinate clauses, so it is dense rather than tight, but nothing is filler.

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

Completeness5/5

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

An output schema exists, so return values need no explanation, and the description still flags what the output reports (the actual error rate used) plus input-validity behavior. For a read-only analysis tool with nested project parameters, nothing an agent needs in order to call it correctly is absent.

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% and each parameter (alpha, error_rate, min_genomic_span, file) already carries rich APT-keyword documentation, so the baseline 3 applies. The description adds only a modest framing of alpha and error_rate as the method's two knobs.

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

Purpose5/5

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

States a specific verb+resource ('Recompute loss of heterozygosity') and the exact data source ('an Affymetrix array's own per-marker genotype calls'), immediately contrasting it with the producer-written runs. An agent can distinguish it from copy_number_regions or copy_number_findings without opening a schema.

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

Usage Guidelines4/5

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

Gives a clear applicability condition: the input container must state genotype calls, with OncoScan OSCHP as a working case and CytoScan CYCHP as a refusing case. It also frames the recompute-vs-read tradeoff, though it never names a sibling tool an agent should pick instead.

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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