tooluniverse-comparative-genomics

What this skill does

# Comparative Genomics & Ortholog Analysis

Cross-species gene comparison, ortholog identification, sequence retrieval, and functional conservation analysis integrating Ensembl Compara, NCBI, UniProt, OLS, Monarch, and OpenTargets.

## LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first (PubMed, UniProt, ChEMBL, ClinVar, etc.) rather than reasoning from memory. A database-verified answer is always more reliable than a guess.

## 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.

## When to Use This Skill

**Triggers**:
- "Find the mouse ortholog of [human gene]"
- "Compare [gene] across species"
- "Is [gene] conserved in [organism]?"
- "What are the orthologs of [gene]?"
- "Cross-species comparison of [gene/protein]"
- "Evolutionary conservation of [gene]"
- "Compare GO annotations between human and mouse [gene]"

**Use Cases**:
1. **Ortholog Discovery**: Find equivalent genes in other species for a human gene
2. **Conservation Analysis**: Assess how conserved a gene is across evolutionary distance
3. **Functional Comparison**: Compare GO terms, domains, and annotations across orthologs
4. **Model Organism Selection**: Determine which model organism best recapitulates human gene function
5. **Gene Tree Analysis**: Visualize evolutionary history of a gene family
6. **Cross-Species Phenotype Bridging**: Link human disease phenotypes to model organism phenotypes via orthologs

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## Conservation Reasoning Framework

Understanding conservation requires distinguishing between types of evolutionary patterns and what they imply about function.

**High conservation signals functional constraint.** When a gene is maintained as a 1:1 ortholog from yeast to humans, purifying selection has prevented sequence divergence — the gene's function is essential and cannot be easily altered. Highly conserved positions within a protein sequence (high PhastCons scores > 0.8, or GERP RS > 4) are under strong constraint; mutations at these positions are disproportionately pathogenic. For non-coding regions, conservation in mammals at PhastCons > 0.5 suggests a candidate regulatory element.

**Low conservation in one lineage has two possible explanations: relaxed selection or positive selection.** Use the dN/dS ratio (nonsynonymous to synonymous substitution rate) to distinguish them. A dN/dS ratio near 1 suggests neutral evolution — the gene is no longer under purifying selection (relaxed constraint, possibly reflecting loss of function in that lineage). A dN/dS ratio > 1 indicates positive selection — the gene is diverging faster than neutral expectation, often because it is adapting to a new environment or function. A dN/dS ratio << 1 is the signature of purifying selection (functional constraint). When a vertebrate gene shows high divergence in a specific branch of the tree, ask which explanation applies before concluding that function is lost.

**Computing dN/dS** (no TU tool does this — use the bundled script). The data path: `ensembl_get_homology(...)` → the 1:1 ortholog IDs → `EnsemblSeq_get_id_sequence(id=..., type="cds")` for each → codon-align the two CDS (orthologous CDS are usually directly alignable; for divergent pairs align the protein and back-translate) → run `scripts/dnds.py`:

```bash
python scripts/dnds.py human_CDS.fasta mouse_CDS.fasta   # or --seq1 ATG... --seq2 ATG...
```
It implements the Nei-Gojobori estimator with Jukes-Cantor correction (dN validated against Biopython NG86) and returns dN, dS, dN/dS, and an interpretation (>1 positive, ~1 neutral/relaxed, <<1 purifying). `dN/dS` is `null` when dS is 0 or uncorrectable (too few/too many substitutions) — do not over-interpret a single high-divergence pair without enough synonymous sites.

**Ortholog relationship type shapes interpretation.** A 1:1 ortholog (one gene in human, one in mouse) is the highest-confidence functional equivalent — it has not been duplicated in either lineage, so it most likely performs the same ancestral role. A 1:many relationship (one gene in human, multiple in mouse) means the target species has duplicated the gene; the copies may have subfunctionalized (each copy performs a subset of the original roles) or neofunctionalized (one copy gained a new role). Do not assume both copies retain full ancestral function. A many:many relationship reflects complex duplication history in both species and requires analyzing each paralog pair individually.

**Conservation depth predicts essentiality.** A gene conserved across all vertebrates suggests a fundamental cellular process. A gene conserved only in mammals suggests a more specialized vertebrate innovation. A gene present only in primates or only in humans is likely a recent evolutionary acquisition, possibly involved in human-specific biology but often lacking the depth of functional characterization available for deeply conserved genes.

**Absence of an ortholog is a finding, not an error.** Lineage-specific genes exist and are biologically meaningful. Before concluding a gene is lineage-specific, check: (1) whether BLAST with relaxed thresholds finds distant homologs, (2) whether a highly divergent ortholog exists that Ensembl Compara missed, and (3) whether the gene belongs to a rapidly evolving family (immune genes, olfactory receptors, reproductive proteins) where turnover is expected.

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## Workflow Overview

```
Input (gene symbol/ID + reference species)
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Phase 1: Gene Identification & Validation
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Phase 2: Ortholog Discovery (Ensembl Compara + OpenTargets)
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Phase 3: Sequence Retrieval (NCBI + Ensembl)
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Phase 4: Functional Annotation Comparison (UniProt + OLS GO terms)
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Phase 5: Cross-Species Phenotype Bridging (Monarch)
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Phase 6: Gene Tree & Evolutionary Context (Ensembl Compara)
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Report: Conservation summary, ortholog evidence, functional comparison, phenotype bridging
```

---

## Phase 1: Gene Identification & Validation

`ensembl_lookup_gene` takes `gene_id` (symbol or Ensembl ID). The `species` parameter is REQUIRED when using gene symbols (e.g., `species="homo_sapiens"`); omitting it causes errors. Extract the Ensembl gene ID, description, biotype, and chromosomal coordinates for downstream queries. For non-human references, adjust `species` accordingly (e.g., "mus_musculus", "danio_rerio").

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## Phase 2: Ortholog Discovery

`EnsemblCompara_get_orthologues` is the primary tool. It takes `gene` (symbol or Ensembl ID), `species` (source species, default "human"), and optionally `target_species` (e.g., "mouse", "zebrafish") or `target_taxon` (NCBI taxon ID). Omit `target_species` to get all orthologs across the tree; filter client-side for specific species. It returns homology type (one2one, one2many, many2many) and the taxonomy divergence level for each ortholog.

`ensembl_get_homology` is the alternative when you need sequence-level data alongside the ortholog mapping. Use `sequence="protein"` and `aligned=true` for aligned sequence comparison across species.

`OpenTargets_get_target_homologues_by_ensemblID` (takes `ensemblId`) provides supplementary ortholog data from OpenTargets, which can add druggability context and cross-reference with model organism phenotype data.

**Reasoning**: Prioritize 1:1 orthologs as high-confidence functional equivalents. For 1:many cases, report all copies and flag the need for paralog-specific functional analysis. If no Ensembl Compara entry exists, try BLAST as a last resort (note: BLAST protein search against swissprot is slow, 5-30 minutes; against nr may take longer).

Key model organisms to check: mouse (taxon 10090), rat (10116), zebrafish (7955), fruit fly (7227), C. elegans (6239), S. cerevisiae (4932).

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