Cross-species genetic analysis using model organism databases (MGI mouse, ZFIN zebrafish, FlyBase fruit fly, WormBase worm, SGD yeast, RGD rat, GBIF taxonomy). Maps human genes to orthologs, retrieves phenotype/expression/functional data, assesses gene function conservation, and identifies the best animal model for studying a human gene or disease.
Cross-species genetic analysis using model organism databases (MGI mouse, ZFIN zebrafish, FlyBase fruit fly, WormBase worm, SGD yeast, RGD rat, GBIF taxonomy). Maps human genes to orthologs, retrieves phenotype/expression/functional data, assesses gene function conservation, and identifies the best animal model for studying a human gene or disease.
COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
Model Organism Genetics Pipeline
Map human genes to model organism orthologs and retrieve phenotype, expression, and functional data across six species. Synthesize cross-species evidence to assess gene function conservation and identify the best animal models for studying human genes and diseases.
Not for: human variant interpretation (tooluniverse-variant-analysis), drug target validation (tooluniverse-drug-target-validation), human disease characterization (tooluniverse-multiomic-disease-characterization).
LOOK UP, DON'T GUESS: When asked about a species' taxonomy, ecology, or biology, search GBIF/NCBI Taxonomy first. For GBIF: use GBIF_search_species(query="species name"), then use the nubKey (not key) from the result to call GBIF_get_species(speciesKey=nubKey) for full taxonomy (kingdom, phylum, class, order, family). The nubKey is the GBIF backbone key; the key is dataset-specific and often lacks higher taxonomy.
Reasoning Principles
Ortholog Reasoning
Sequence conservation across species implies functional conservation — but not always. A highly conserved gene in mouse and human likely has the same function. But regulatory differences (when/where a gene is expressed) can cause different phenotypes even from the same gene. Always check: is the protein domain conserved, or just raw sequence? Are there known regulatory differences? A 40% identity ortholog with a conserved catalytic domain can be more functionally equivalent than a 90% identity paralog in the same species.
Paralog contamination is a common pitfall. Gene families (e.g., FOXP1/2/3/4, HOX clusters) generate false ortholog hits. Distinguish true orthologs from paralogs by checking synteny (conserved gene neighborhood) and homology type: 1:1 = likely true ortholog; 1:many or many:many = likely paralog expansion. If the target species has a single gene where humans have multiple (e.g., one fly FoxP vs four human FOXPs), it is the co-ortholog of all human paralogs — note this explicitly.
Model Organism Selection
Choose your model by the question:
Mouse: mammalian physiology, drug testing, immune system, CNS disease — best when you need human-like biology
Fly: genetic screens, signaling pathways (Notch, Wnt, Hh first characterized here), neural circuits, aging — best for rapid genome-wide genetics
Worm: cell lineage, apoptosis, RNAi screens, aging — best when you need single-cell resolution and mapped connectome
Zebrafish: development, organ formation, live imaging, cardiac biology — best when you need vertebrate biology with optical access
Yeast: cell cycle, DNA repair, metabolism, protein trafficking, chromatin — best for fundamental cell biology
Frog (Xenopus): early development, cell signaling, oocyte biochemistry — note X. laevis is allotetraploid (two homeologs: .L and .S)
Invertebrates (fly, worm, yeast) lack adaptive immunity and many vertebrate-specific organs — if the question involves those systems, they will be uninformative.
Phenotype Transfer Reasoning
A knockout phenotype in mouse does not automatically predict the human phenotype. Ask three questions before inferring cross-species relevance:
Is the pathway conserved? A mouse cardiac phenotype only predicts human cardiac disease if the same developmental pathway operates in both hearts.
Are there compensating paralogs? If the mouse has one gene but humans have three paralogs, a mouse knockout can be more severe than loss of a single human paralog. Conversely, if humans lost a paralog that mice retain, the mouse KO may overpredict human phenotype.
Is the gene dosage-sensitive? Haploinsufficiency in mouse (heterozygous phenotype) is a stronger predictor of human dominant disease than phenotypes seen only in homozygous knockouts.
When phenotypes differ across species, consider regulatory divergence: the coding sequence may be conserved while the expression pattern has shifted. This can produce organisms with the "same gene" but different tissues of expression and therefore different phenotypes.
Pipeline
Phase 0: Human Gene Disambiguation (ALWAYS FIRST)
MyGene_query_genes(query="<gene>") — get Ensembl ID, Entrez ID, UniProt, symbol (filter by symbol match; first hit may be a pseudogene)
NCBIDatasets_get_orthologs(gene_id="<entrez_id>") — broad, all vertebrates
For fly: FlyMine_search(query="<human_gene_symbol>") — text search finds distant orthologs that automated tools miss; confirm with FlyBase_get_gene_orthologs
For worm: WormBase_get_gene(gene_id="<gene_symbol>") — gene record often contains ortholog info
Cross-reference via Monarch:
Monarch_search_gene(query="<gene_symbol>") — get Monarch gene entity
MonarchV3_get_associations(subject="HGNC:<id>", category="biolink:GeneHomologAssociation") — all orthologs
Note: "No ortholog found by tools" is not the same as "no ortholog exists." Sequence divergence does not equal functional divergence. Try manual search before concluding absence.
Phase 2: Mouse Phenotypes (MGI)
MGI_search_genes(query="<mouse_symbol>") — confirm MGI ID
MGI_get_gene(gene_id="MGI:XXXXXXX") — full gene details
Phase 4b: Rat (RGD) — physiology & disease-model strains
Rat is the premier mammalian model for cardiovascular, metabolic, behavioral, and toxicology physiology. RGD's distinctive asset is its curated strain catalog (inbred/congenic/consomic lines) annotated as disease models — data not found in MGI or the Alliance.
Gene-level:
RGD_search_genes(query="<gene_symbol>") then RGD_get_gene(rgd_id=<id>) — rat gene details
RGD_get_annotations(rgd_id=<id>) — disease/phenotype/GO annotations for the gene
RGD_get_qtls_in_region(chromosome="1", start=1, stop=10000000, map_key=360) — QTLs in a region
Strain-level (rat disease models — use when the question is "which rat strain models disease X?"):
5. RGD_search_strains(query="hypertensive", strain_type="inbred") — find strains by keyword/type (types: inbred, congenic, consomic, transgenic, recombinant_inbred, ...)
6. RGD_get_strain(symbol="SHR") — full record for a named strain (e.g. SHR = spontaneously hypertensive rat, rgd_id 61000; BN = Brown Norway; SS = Dahl salt-sensitive; GK = Goto-Kakizaki diabetes)
7. RGD_get_strain_annotations(symbol="SHR", category="disease") — curated disease/phenotype annotations that define the strain as a model
Example — SHR is annotated to Left Ventricular Hypertrophy (DOID:9004616, qualifier MODEL: spontaneous) and arterial blood pressure trait (VT:2000000), making it the canonical model for essential hypertension and its cardiac sequelae.
Mouse "cardiac defects" + Zebrafish "heart edema" + Human "cardiomyopathy" → core: cardiac development
Step 3: Cross-species phenotype mapping
Different species use different ontologies (HPO, MP, FBcv, WBPhenotype, ZP). Use MonarchV3_phenotype_similarity_search to find equivalent phenotypes via the uPheno ontology. When automated mapping fails, use biological reasoning to find conceptual equivalents.
Step 4: Conservation assessment
Highly conserved: ortholog in all 6 species, consistent phenotypes, shared pathways
Vertebrate-specific: ortholog in mouse/fish/frog but not fly/worm/yeast
Metazoan-specific: ortholog in mouse/fish/fly/worm but not yeast
Human-specific: no clear ortholog in any model organism
Step 5: Pathway conservation check
STRING_get_network(identifiers="<human_gene> <mouse_ortholog> <fly_ortholog>", species=9606) — check if interaction partners are also conserved
Step 6: Organism recommendation
Recommend which organism(s) to use for further study. Consider: phenotype match to human condition, available genetic tools, complementary models (e.g., mouse for physiology + fly for genetic screens), practical considerations (cost, throughput, imaging).
HPO_search_terms(query="<disease_name>") — phenotype terms for cross-species comparison
Map HPO terms back to model organism phenotypes (Phase 6) to assess model fidelity.
Bacterial and Classical Genetics Reasoning
These problems require computation and logical deduction, not database lookups. Work through the logic step by step.
Hfr Conjugation and Chromosome Mapping
Time-of-entry mapping: In Hfr x F- crosses, genes transfer in a fixed linear order from the integrated F factor origin. Interrupted mating at different times reveals gene order and map distances (1 minute ~ 1 map unit on the circular E. coli chromosome, ~47 kb).
Key reasoning steps:
Gene order = order of appearance in recombinants as mating time increases
Map distance = difference in entry times (minutes) between consecutive markers
Directionality: Different Hfr strains have F integrated at different positions and orientations. Compare gene orders from multiple Hfr strains to construct the circular map. If Hfr1 transfers A-B-C and Hfr2 transfers C-B-A, their F factors are integrated at opposite orientations near the same site.
F' formation: Imprecise excision of F captures adjacent chromosomal genes. An F' carrying gene X means X was adjacent to the F integration site. F' x F- = partial diploid (merodiploid) for the carried region -- use for complementation/dominance tests.
Recombinant selection: Only recombinants that integrate donor markers by double crossover (or even number) are stable. The selected marker must be the LAST to enter (closest to Hfr origin = first to enter is WRONG -- the selected marker is the one you plate for, which requires full transfer or recombination).
Operon Regulation and Attenuation
lac operon logic (negative inducible):
Repressor (lacI) binds operator (lacO) in absence of inducer (allolactose)
lacI+ is trans-dominant over lacI- (repressor diffuses)
lacOc (operator constitutive) is cis-dominant (only affects genes on same DNA molecule)
In partial diploids: determine genotype of EACH DNA molecule separately, then combine
Leader transcript has 4 regions (1-2-3-4) that form alternative stem-loops
Region 1 encodes a short peptide rich in Trp codons
High Trp: ribosome translates quickly through region 1-2, region 3-4 forms TERMINATOR hairpin -> transcription stops
Low Trp: ribosome stalls at Trp codons in region 1, region 2-3 forms ANTITERMINATOR hairpin -> transcription continues
No ribosome (in vitro): region 1-2 pairs, then 3-4 pairs -> termination (default)
Key: the ribosome's position relative to the mRNA folding regions determines which stem-loops form
Catabolite repression: Even with inducer present, lac operon requires cAMP-CAP for full expression. High glucose -> low cAMP -> low expression. This is POSITIVE regulation layered on top of the negative repressor system.
Gene Mapping from Cross Data
Three-point cross (most common exam problem):
Identify the 8 phenotypic classes and their frequencies
Parentals = two most frequent classes
Double crossovers = two least frequent classes
Compare double crossovers to parentals to find the MIDDLE gene (the gene whose allele has switched relative to parentals in the DCO class)
Map distances: (single CO region 1 + DCO) / total = distance 1; (single CO region 2 + DCO) / total = distance 2