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.

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

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

1. Coverage Prediction: Estimate uniformity and dropout risk.

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

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

Workflow

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

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

1. Region Definition: Define target coordinates.

1. Probe Design: Generate capture probes or primers.

1. Coverage Simulation: Predict sequencing performance.

1. Optimization: Iterate design for uniformity.

1. 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

Gene Prioritization Sources

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

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

Author

AI Group - Biomedical AI Platform