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name: 'gene-panel-design-agent' description: 'AI-powered design of targeted gene panels for clinical and research applications including cancer diagnostics, pharmacogenomics, and rare disease testing.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

• read_file
• run_shell_command

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Gene Panel Design Agent

The Gene Panel Design Agent provides AI-driven design of targeted sequencing panels for clinical diagnostics, cancer profiling, pharmacogenomics, and research applications.

When to Use This Skill

• When designing custom gene panels for clinical or research use.
• To optimize panel content for specific disease areas.
• For balancing panel size with diagnostic yield.
• When designing probes for hybrid capture or amplicon approaches.
• To validate panel performance computationally.

Core Capabilities

1. Gene Selection: Evidence-based gene prioritization for disease areas.

2. Target Region Definition: Specify exons, introns, UTRs, promoters to include.

3. Probe Design: In silico probe/primer design for capture or amplicon.

4. Coverage Prediction: Estimate uniformity and dropout risk.

5. Validation Planning: Design positive controls and performance metrics.

6. Cost Optimization: Balance panel size with clinical utility.

Workflow

1. Input: Disease focus, required genes, platform choice, size constraints.

2. Gene Prioritization: Rank genes by clinical evidence level.

3. Region Definition: Define target coordinates.

4. Probe Design: Generate capture probes or primers.

5. Coverage Simulation: Predict sequencing performance.

6. Optimization: Iterate design for uniformity.

7. Output: Panel BED file, probe sequences, validation plan.

Example Usage

User: "Design a comprehensive solid tumor panel covering actionable mutations and resistance markers."

Agent Action:

python3 Skills/Genomics/Gene_Panel_Design_Agent/panel_designer.py \
    --disease solid_tumor \
    --gene_sources nccn,civic,oncokb \
    --platform hybcap \
    --target_size 1.5mb \
    --include_fusions true \
    --include_cnv_backbone true \
    --output panel_design/

Panel Design Considerations

Factor	Impact	Optimization
Panel size	Cost, depth	Prioritize high-evidence genes
GC content	Coverage uniformity	Probe design, blockers
Repeat regions	Mapping challenges	Avoid or boost coverage
Homologous regions	Misalignment	Unique design, blockers
Structural variants	Detection	Intronic coverage, breakpoints
CNV detection	Require backbone	Tiled probes across genome

Gene Prioritization Sources

Source	Content	Evidence Level
OncoKB	Actionable alterations	FDA/guideline levels
CIViC	Clinical variants	Community-curated
ClinVar	Pathogenic variants	Classification criteria
NCCN	Guideline genes	Clinical practice
COSMIC	Cancer genes	Census tier 1/2

Panel Types

Comprehensive Cancer Panel (300-700 genes):

• All known cancer drivers
• Actionable mutations
• Resistance markers
• MSI/TMB estimation

Focused Tumor Panel (50-100 genes):

• Most actionable genes
• Cost-effective
• Higher depth possible

Pharmacogenomics Panel:

• CPIC/DPWG genes
• CYP450, HLA, transporters
• Star allele compatible design

Rare Disease Panel:

• Disease-specific genes
• Deep intronic variants
• CNV detection

AI/ML Components

Gene Ranking:

• Literature mining for evidence
• Mutation frequency weighting
• Actionability scoring

Probe Optimization:

• GC content balancing
• Tm normalization
• Off-target minimization

Coverage Prediction:

• ML models from historical data
• GC-coverage relationships
• Dropout prediction

Validation Planning

Performance Metrics:

• Coverage uniformity (CV)
• On-target rate
• Sensitivity by variant type
• Reproducibility

Reference Materials:

• Horizon Discovery cell lines
• SeraCare controls
• Well-characterized samples
• In silico spike-ins

Technical Specifications

Platform	Typical Size	Depth	CNV Capable
Hybrid capture	1-3 Mb	500-1000x	Yes (with backbone)
Amplicon	10-500 kb	1000-5000x	Limited
Anchored multiplex	Variable	Variable	Fusions

Prerequisites

• Python 3.10+
• BEDTools for coordinate manipulation
• Probe design algorithms
• Reference genome and annotations

Related Skills

• CRISPR_Design_Agent - For guide design
• Variant_Interpretation - For variant selection
• Tumor_Mutational_Burden_Agent - For TMB panel requirements

Output Files

File	Content	Purpose
panel.bed	Target coordinates	Sequencing design
probes.fa	Probe sequences	Manufacturing
genes.csv	Gene list with rationale	Documentation
validation.pdf	QC plan	Laboratory setup

Author

AI Group - Biomedical AI Platform

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