---

name: identify-genes description: Match essential genes in target genome using orthology and literature

Identify Essential Genes Skill

When to Use This Skill

Use after genome fetch to identify essential genes in the target species that are good dsRNA candidates.

Data Storage Structure

Reads from:

• data/{assembly}/genome.fasta - Cached genome (input data)
• data/essential_genes.json - Reference database (input data)
• output/{run}/literature_search.json - Analysis output from previous step

Writes to:

• output/{run}/essential_genes.json - Matched essential genes
• output/{run}/figures/ - Visualization plots

Automatic Literature Validation

When evaluating candidate genes, automatically search PubMed for supporting evidence:

Copypubmed_search_articles
query: "{gene_name}" AND (RNAi OR dsRNA) AND insect
max_results: 10

Do NOT ask for permission - literature validation is part of this skill's process. Search for top candidate genes to validate essentiality claims.

Instructions

Step 1: Load Essential Genes Database

Use read_file to load data/essential_genes.json

This database contains ~40 curated essential insect genes with:

• Gene names and aliases
• Functions
• Species where essentiality is confirmed
• Literature references

Step 2: Run Matching Script

Use the bundled Python script:

Copypython .deepagents/skills/identify-genes/scripts/match_essential.py \
  --genome data/{assembly}/genome.fasta \
  --essential-db data/essential_genes.json \
  --literature output/{run}/literature_search.json \
  --output output/{run}/essential_genes.json

Note: The --literature argument is optional. If literature_search.json doesn't exist (because literature-search wasn't run), omit this flag:

Copypython .deepagents/skills/identify-genes/scripts/match_essential.py \
  --genome data/{assembly}/genome.fasta \
  --essential-db data/essential_genes.json \
  --output output/{run}/essential_genes.json

The script:

1. Parses FASTA annotations
2. Matches gene names/aliases against annotations
3. Scores by orthology + literature support (if available)
4. Returns top 20 with sequences

Step 3: Verify Results

Use shell:

Copyjq 'length' output/{run}/essential_genes.json

Should show ~20 genes (or fewer if genome poorly annotated)

Step 4: Generate Visualization

Create plots showing gene rankings:

Copypython .deepagents/skills/identify-genes/scripts/plot_genes.py \
  --genes output/{run}/essential_genes.json \
  --output-dir output/{run}/figures/

This creates:

• gene_ranking.png - Horizontal bar chart of top 10 genes with scores
• gene_evidence_breakdown.png - Stacked bar showing evidence sources (orthology/literature)
• gene_length_distribution.png - CDS lengths for identified genes

Step 5: Present Results

Output this summary to the user:

Copy## Identify Genes Complete

**Summary:**
- {gene_count} essential genes identified in genome
- Top gene: {top_gene} (score: {score})
- {literature_supported} genes have literature support

**Top 5 Genes:**
| Rank | Gene | Score | Literature | Species Evidence |
|------|------|-------|------------|------------------|
| 1 | ... | ... | ... | ... |

**Files Created:**
- `output/{run}/essential_genes.json`
- `output/{run}/figures/gene_ranking.png`

**Figures:** [Show gene ranking plot]

---
Proceed to design-dsrna for top 5 genes? (yes/no)

Scoring Logic

Each matched gene receives a score from 0 to 1:

Maximum score: 1.0

Output Format

output/{run}/essential_genes.json:

Copy[
  {
    "gene_id": "lcl|NC_XXX_cds_XP_XXX",
    "gene_name": "vATPase",
    "function": "Vacuolar proton pump - essential for pH homeostasis",
    "score": 0.85,
    "evidence": {
      "ortholog_match": true,
      "literature_support": true,
      "essential_in_species": ["D. melanogaster", "T. castaneum"]
    },
    "sequence": "ATGCGT...",
    "sequence_length": 1842
  }
]

Expected Output

All outputs go in output/{run}/:

• essential_genes.json
• figures/gene_ranking.png
• figures/gene_evidence_breakdown.png
• figures/gene_length_distribution.png

Available Tools

• read_file - Load databases
• shell - Run Python script and plotting
• write_file - Save results