---

name: design-dsrna description: Design dsRNA candidates using sliding window algorithm

Design dsRNA Candidates Skill

When to Use This Skill

Use after identifying essential genes to design 3 dsRNA candidates per gene for the top 5 genes (15 total candidates).

Data Storage Structure

Reads from:

• output/{run}/essential_genes.json - From identify-genes step

Writes to:

• output/{run}/candidates.json - Designed dsRNA candidates
• output/{run}/figures/ - Visualization plots

Instructions

Step 1: Select Top 5 Genes

Use shell with jq:

Copyjq '.[0:5]' output/{run}/essential_genes.json > /tmp/top5_genes.json

Step 2: Design Candidates

Run the sliding window design script:

Copypython .deepagents/skills/design-dsrna/scripts/sliding_window.py \
  --genes output/{run}/essential_genes.json \
  --num-genes 5 \
  --candidates-per-gene 3 \
  --length 300 \
  --output output/{run}/candidates.json

This processes top 5 genes and generates 3 candidates each = 15 total.

Step 3: Verify Output

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

Should show 15 (or fewer if some genes are too short)

Step 4: Generate Visualization

Create plots showing candidate design quality:

Copypython .deepagents/skills/design-dsrna/scripts/plot_candidates.py \
  --candidates output/{run}/candidates.json \
  --genes output/{run}/essential_genes.json \
  --output-dir output/{run}/figures/

This creates:

• candidate_locations.png - Genomic positions of candidates along each gene
• candidate_gc_distribution.png - Histogram of GC content across all candidates
• candidate_scores_heatmap.png - Heatmap showing design score components

Step 5: Present Results

Output this summary to the user:

Copy## Design dsRNA Complete

**Summary:**
- {candidate_count} candidates designed from {gene_count} genes
- GC content range: {min_gc}% - {max_gc}%
- All candidates are 300bp

**Candidates by Gene:**
| Gene | Candidate | Position | GC% | Score |
|------|-----------|----------|-----|-------|
| vATPase | vATPase_1 | 150-450 | 42.3% | 5 |
| ... | ... | ... | ... | ... |

**Files Created:**
- `output/{run}/candidates.json`
- `output/{run}/figures/candidate_locations.png`

**Figures:** [Show candidate locations plot]

---
Proceed to blast-screen? (yes/no)

Design Algorithm

Sliding Window Parameters

• Window length: 300 bp (optimal for dsRNA synthesis)
• Step size: 50 bp
• Candidates per gene: 3 (non-overlapping)

Window Scoring (0-5 points)

Selection Process

1. Score all windows
2. Sort by score (descending)
3. Select highest-scoring non-overlapping windows
4. Take top 3 per gene

Output Format

output/{run}/candidates.json:

Copy[
  {
    "id": "vATPase_1",
    "gene_name": "vATPase",
    "gene_id": "lcl|NC_XXX_cds_XP_XXX",
    "sequence": "ATGCGTACG...",
    "start": 150,
    "end": 450,
    "length": 300,
    "gc_content": 0.423,
    "has_poly_n": false,
    "design_score": 5
  }
]

Expected Output

All outputs go in output/{run}/:

• candidates.json
• figures/candidate_locations.png
• figures/candidate_gc_distribution.png
• figures/candidate_scores_heatmap.png

Available Tools

• shell - Run Python script, jq, and plotting
• read_file / write_file - Handle JSON

Notes

• If a gene is shorter than 300bp, it will be skipped
• Overlapping candidates are avoided to maximize coverage
• Lower-scoring candidates may be selected if needed for non-overlap