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---
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lineage_type: import
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upstream_source: https://github.com/mims-harvard/ToolUniverse/blob/701afa3a/skills/tooluniverse-biomedical-fact-lookup/SKILL.md
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upstream_sha: 701afa3a
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imported_at: 2026-07-07
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prompt_class: unknown
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upstream_changes: accepted
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name: tooluniverse-biomedical-fact-lookup
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description: "Answer biomedical FACTUAL / recall / multiple-choice questions by querying ToolUniverse database tools instead of answering from memory. Triggers on any 'which gene/drug/variant/disease/pathway/miRNA/TF...' lookup, any question phrased 'according to <database>' (DisGeNet, OMIM, MSigDB, miRDB, GTRD, MGI, Ensembl, ClinVar, ChEMBL, OpenTargets, Reactome, GtoPdb, UniProt...), and multiple-choice biology/medicine knowledge questions where one option must be verified against an authoritative source. NOT for analyzing user-supplied data files (CSV/VCF/h5ad → use the data-analysis router) and NOT for open-ended literature synthesis. Use whenever a single correct answer exists in a public biomedical database and could be looked up rather than guessed."
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when_to_use: "A factual biomedical question has a single database-checkable answer — especially MCQ of the form 'which of the following X is associated-with / contained-in / a-target-of / located-at Y according to <database>'. Reach for this before answering from memory."
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---
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# Biomedical Fact Lookup (tool-grounded answering)
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Factual biomedical questions — "which gene is in set X", "which gene is associated with disease Y according to DisGeNet", "which gene has a TF binding site per GTRD" — have an authoritative answer in a public database. Guessing from memory is unreliable (≈chance on niche annotations); the matching ToolUniverse tool returns the ground truth.
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## RULE ZERO: Look it up, never guess
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If a question names a database, a gene set, or any annotation that lives in a database, you MUST query the tool before answering. Answering a "according to <database>" question from memory is a failure mode — these annotations (predicted miRNA targets, ChIP-seq binding, curated gene sets, disease associations) are exactly what models hallucinate. A tool-verified answer beats any recalled fact.
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## Multiple-choice procedure
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Most of these questions are MCQ with an "Insufficient information to answer the question." distractor. Do this:
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1. **Parse** the question for: the **named database/collection**, the **anchor entity** (the gene set, disease, miRNA, TF, locus…), and the **candidate options**.
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2. **Resolve** the anchor to the right tool + identifier (see Routing table).
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3. **Query** the tool once to get the authoritative member list / association set.
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4. **Check each option** against that result. Exactly one option should be supported.
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5. **Answer** with that option's letter. Only choose "Insufficient information" if the tool genuinely returns nothing for a valid query (not because you skipped the query).
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## Routing table — question pattern → tool
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| Question mentions… | Tool(s) (verified) | How |
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|---|---|---|
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| a named **gene set** / **oncogenic signature** (MSigDB C6, e.g. `ATM_DN.V1_DN`) | `MSigDB_get_gene_set_members` | list members, check which option is in it |
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| **miRNA target** "according to miRDB" (e.g. MIR186-3p) | `MSigDB_get_gene_set_members` (collection C3:MIR:MIRDB) | set name = `MIR<number>_<3P\|5P>`, e.g. `MIR186_3P` |
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| **TF binding site / target** "according to GTRD" (e.g. PGM3) | `MSigDB_check_gene_in_set` (collection C3:TFT:GTRD) | set name = `<TF>_TARGET_GENES`, e.g. `PGM3_TARGET_GENES`; pass `gene` per option |
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| **pathway / hallmark** membership | `MSigDB_get_hallmark_geneset`, `MSigDB_get_geneset` | `HALLMARK_<NAME>` or exact set name |
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| **gene ↔ disease** association (DisGeNet, OpenTargets, OMIM) | `umls_search_concepts` → `DisGeNET_get_disease_genes`/`DisGeNET_get_gda`; `OpenTargets_*`, `MyDisease_get_disease`, `OMIM_search`; **text-mined fallback:** `PubTator3_LiteratureSearch` / `PubTator3_GetEntityRelations` (`e1=@GENE_<sym>`), `EPMC_get_text_mined_annotations` | DisGeNET needs a **UMLS CUI** (resolve via `umls_search_concepts` → `C0152200`, then `disease=C0152200`) + `DISGENET_API_KEY`. See the "in X but not Y" recipe below |
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| **mouse phenotype** gene set (MGI / MP:xxxxx, e.g. "increased carcinoma incidence") | `MGI_search_genes` → `MGI_get_phenotypes` | for **each** candidate gene: search → take the `MGI:` id → `MGI_get_phenotypes`; the matching gene is the one whose `phenotype_statement` list contains the phenotype the question names (see interpretation note) |
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| **gene genomic location** (Ensembl band, e.g. chr7q34) | `Ensembl_*` / `NCBIDatasets_get_gene_by_symbol` | resolve each option, compare cytoband/coordinates |
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| **variant / sequence** pathogenicity ("which variant/sequence is pathogenic *or* benign per ClinVar") | (only when genuinely unsure) `annotate_variant_multi_source`, `VEP_predict_pathogenicity`, `UniProt_get_disease_variants_by_accession` | **Be efficient — do NOT query every option (that causes timeouts).** Identify the protein once, find each option's single substitution, and reason about the specific residue changes directly; the base model is usually reliable on well-characterized ClinVar variants. Make at most ONE targeted tool call to resolve a truly uncertain variant. **Watch the question's polarity** (benign vs pathogenic): for "most likely benign", a common/reference-matching variant is the answer; for "most likely pathogenic", a rare damaging one is. |
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| **drug / compound** target, MoA, approval | `ChEMBL_*`, `OpenFDA_*`, `GtoPdb_*`, `PubChem_*` | resolve drug, query the relation |
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| **protein** function / domain / sequence | `UniProt_*` | resolve accession, read annotation |
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When unsure which tool wraps a database, search the catalog by the *relation* (e.g. "gene disease association", "gene set members"), not the brand name — ToolUniverse usually already has it.
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## MSigDB set-name conventions (the most common LAB-Bench pattern)
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ToolUniverse's `MSigDB_*` tools cover several collections that LAB-Bench questions are built from. Get the set name right:
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- **C6 oncogenic signatures** — use the exact set name quoted in the question (e.g. `ATM_DN.V1_DN`, `KRAS.600_UP.V1_UP`).
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- **C3:MIR:MIRDB** (miRDB v6.0 predicted miRNA targets) — `MIR<number>_<3P|5P>` (e.g. `MIR186_3P`, `MIR675_3P`). This *is* miRDB; do not say "no access to miRDB".
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- **C3:TFT:GTRD** (GTRD TF target genes) — `<TF>_TARGET_GENES` (e.g. `PGM3_TARGET_GENES`). This *is* GTRD.
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- **Hallmark** — `HALLMARK_<NAME>`.
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`MSigDB_get_gene_set_members` (operation `get_gene_set`) returns `{genes:[...]}`; `MSigDB_check_gene_in_set` (operation `check_gene_in_set`, param `gene`) returns `{is_member: bool}`.
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## Gene–disease "in database X but NOT database Y" recipe
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These questions (e.g. "which gene is associated with disease D according to DisGeNet but **not** OMIM?") need a *differential* lookup, not a single query:
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1. Resolve D to a UMLS CUI (`umls_search_concepts`).
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2. **OMIM side:** `OMIM_search`/`OMIM_get_gene_map` for D → the set of OMIM-causal genes.
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3. **DisGeNet side:** `DisGeNET_get_disease_genes(disease=CUI)` (curated). Note the academic key is **curated-only**; DisGeNet *also* includes a text-mined tier the key can't see.
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4. **Text-mined fallback** (covers DisGeNet's text-mined tier when curated is empty): `PubTator3_LiteratureSearch("<GENE> <disease>")` or `PubTator3_GetEntityRelations(e1="@GENE_<sym>", type="associate")` — a gene with literature co-occurrence to D but **absent from OMIM-for-D** is the "in DisGeNet but not OMIM" answer.
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5. **Elimination:** rule out options that ARE OMIM-causal for D; among the rest, pick the one with a DisGeNet/text-mined association. If exactly one option is non-OMIM and has any association signal, that is the answer.
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6. Only answer "Insufficient information" if no option has any association in any source. If the gold gene appears in neither curated DisGeNet, OMIM, nor PubTator literature, it may rely on a DisGeNet-internal text-mined signal the academic tier can't reach — say so honestly rather than guessing.
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## Mouse-phenotype matching (MGI)
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`MGI_get_phenotypes` returns a list of `phenotype_statement` strings per gene. To answer "which gene is annotated to phenotype P" (e.g. an MP term like *increased carcinoma incidence*), query each candidate gene and pick the one whose statements include a phrase matching P (the statements are human-readable, e.g. "increased incidence of carcinoma", "tumor"). Match on the phenotype concept, not an exact MP id string. If several match, prefer the most specific statement.
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## Computational procedures (when the answer is COMPUTED, not looked up)
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Any question with a **single deterministic numeric/combinatorial answer** must be obtained by **RUNNING code**, never by estimating or doing it in your head. This covers sequence questions (ORF counts, restriction fragments/sizes, GC content, translation) **and** any other exactly-computable question — e.g. **genetics segregation / Mendelian or polyploid gamete ratios, combinatorial probabilities, stoichiometry, dosage/PK arithmetic, counting problems**. Mental arithmetic on these is the #1 avoidable error: the model reliably mis-counts or mis-multiplies. If a question reduces to "enumerate the cases / multiply the probabilities / count the objects", **write a short Python snippet, execute it, and report exactly what it returns** — even when the topic looks like a biology "reasoning" question, if the answer is a definite number, compute it rather than reason it out. Match the question's wording for conventions (which strand; linear vs circular; which cross/segregation model) and **state the convention you used** so the answer is auditable.
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**Final-answer discipline (avoid "computed right, answered wrong").** After the code returns the value, map it back to the option letters **carefully and explicitly**: quote the computed value, then find the option that matches it exactly (for a set of fragment sizes, match the whole multiset; for a count, match the integer). A surprising number of misses are cases where the computation was correct but the wrong letter was selected — do not let this happen; re-read each option against the computed result before emitting `[ANSWER]`.
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**Procedure: "how many ORFs encode proteins greater than N amino acids?"**
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Read the phrasing literally. "How many ORFs … **in the DNA sequence** `<X>`" asks about the **single strand you were given** — count that strand only (3 frames), NOT both strands. Do **not** "helpfully" add the reverse complement on the reasoning that DNA is double-stranded: the question hands you one sequence string and asks what is *in it*, so the reverse strand is out of scope unless the question **explicitly** says "both strands" / "double-stranded" / "either strand" / "reverse complement". Adding the reverse strand by default is the single most common way these items are missed — resist it. Count **every distinct start (ATG) that reaches an in-frame stop**; overlapping/nested ORFs each count (two ATGs in the same frame before one stop = two ORFs). Length rule is **strict**: protein length in aa = (stop_index − start_index); keep those with `aa_len > N` for "greater than N". Report the number your code returns for the given strand — if you also computed a both-strands figure, do not let it override the single-strand answer the question asked for.
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```python
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from Bio.Seq import Seq
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def count_orfs(dna, min_aa, both_strands=False):
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"""Count ORFs (ATG..in-frame-stop) encoding a protein STRICTLY longer than min_aa.
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Counts every qualifying ATG, including nested/overlapping ORFs. Forward strand
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by default; set both_strands=True only if the question asks for both strands."""
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dna = "".join(dna.split()).upper()
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strands = [Seq(dna)]
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if both_strands:
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strands.append(Seq(dna).reverse_complement())
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n = 0
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for s in strands:
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for off in range(3): # three reading frames per strand
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trimmed = s[off: len(s) - (len(s) - off) % 3]
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prot = str(trimmed.translate()) # '*' marks stop codons
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i = 0
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while i < len(prot):
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if prot[i] == "M": # ATG
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stop = prot.find("*", i)
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if stop != -1 and (stop - i) > min_aa:
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n += 1 # count this ATG; do NOT jump past stop
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i += 1
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return n
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# e.g. count_orfs(seq, 12) -> integer; report exactly that number.
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```
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**Procedure: restriction digest fragment count/sizes**
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```python
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# Count fragments after digesting with named enzyme(s).
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# LINEAR DNA is the default (a plain sequence string): fragments = cuts + 1.
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# Only use circular=True if the question says plasmid/circular.
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from Bio.Seq import Seq
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from Bio.Restriction import RestrictionBatch
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def digest(dna, enzymes, circular=False):
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dna = "".join(dna.split()).upper()
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rb = RestrictionBatch(enzymes) # e.g. ["EcoRI","BamHI"] or ["AluBI","MalI"]
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cut_positions = sorted(p for sites in rb.search(Seq(dna), linear=not circular).values() for p in sites)
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if not cut_positions:
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return 1, [] # uncut: one fragment (linear or circular)
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n_frag = len(cut_positions) if circular else len(cut_positions) + 1
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return n_frag, cut_positions
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```
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If `RestrictionBatch` raises on an enzyme name (isoschizomer / rare supplier name), resolve it via the DNA-digest tool (which has a Biopython fallback) or map it to its recognition site, then re-run — do not fall back to guessing.
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**Procedure: genetics segregation / gamete & progeny ratios (enumerate, don't recall)**
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Genetics questions that hinge on a ratio — gamete frequencies, offspring genotype proportions, polyploid segregation — are exactly computable by **enumerating equally-likely allele combinations**. Do not recall a memorized ratio; derive it. For a parent carrying a multiset of alleles at a locus, gametes under random chromosome segregation are all equally-likely ways to draw the gamete's allele count from the parent's alleles; count genotype classes with `Counter` + `combinations`.
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```python
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from itertools import combinations
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from collections import Counter
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def gamete_ratio(alleles, gamete_size):
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"""Genotype distribution of gametes under random segregation.
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e.g. tetraploid AAaa -> gametes carry 2 alleles: gamete_ratio(['A','A','a','a'], 2)."""
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classes = Counter("".join(sorted(c)) for c in combinations(alleles, gamete_size))
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return dict(classes) # e.g. {'AA':1, 'Aa':4, 'aa':1}
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def progeny_fraction(parent_alleles, gamete_size, target_gamete, selfing=True):
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"""Fraction of progeny that are homozygous target (e.g. 'aa' gamete x 'aa' gamete -> aaaa)."""
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g = gamete_ratio(parent_alleles, gamete_size); tot = sum(g.values())
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p = g.get(target_gamete, 0) / tot
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return p * p if selfing else p # selfing/self-cross: square the gamete frequency
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# tetraploid AAaa: gamete_ratio(['A','A','a','a'],2) = {'AA':1,'Aa':4,'aa':1};
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# recessive 'aa' gamete freq = 1/6, so aaaa progeny under selfing = (1/6)^2 = 1/36.
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```
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Interpret the enumerated ratio against the options (e.g. the scenario giving a 1:4:1 AA:Aa:aa gamete ratio maximizes the `aa` gamete and hence `aaaa` progeny). Report the computed fraction/ratio and pick the option matching it.
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Interpretation: report the **exact value the code returns** (ORF count; fragment count/sizes as the whole multiset; longest-ORF length in nt or aa; gamete ratio / progeny fraction — exactly as the question asks). Always say which convention you applied (forward vs both strands; linear vs circular; segregation model) so the choice is auditable. If two readings are plausible, compute both and pick the one that matches the question's literal phrasing. **Then match the computed value back to the options explicitly before answering** (see final-answer discipline above).
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## Interpretation
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- A tool result listing the anchor's members/associations is authoritative — pick the option present in it.
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- If a tool errors on a *name* (e.g. set not found), re-derive the name from the convention above before concluding "insufficient".
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- "Insufficient information" is correct only when the authoritative tool returns an empty result for a well-formed query — not when a query was never attempted.
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## Limitations (honest)
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- **Key-gated sources**: `DisGeNET_*` and OMIM tools need `DISGENET_API_KEY` / OMIM key. Without a key, fall back to `OpenTargets_*` / `MyDisease_*` (keyless) and state the source used. If no keyless source can answer and the question is database-specific, this is a genuine "Insufficient information" case — say so.
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- **Release mismatch**: a tool's snapshot of a database may differ slightly from the exact release a question cites; report the source and version when it matters.
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- This skill grounds *factual* lookups. For computing over user data files, use the data-analysis router skills instead.
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