By 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
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
Gene Selection: Evidence-based gene prioritization for disease areas.
Target Region Definition: Specify exons, introns, UTRs, promoters to include.
Probe Design: In silico probe/primer design for capture or amplicon.
Coverage Prediction: Estimate uniformity and dropout risk.
Validation Planning: Design positive controls and performance metrics.
Cost Optimization: Balance panel size with clinical utility.
Workflow
Input: Disease focus, required genes, platform choice, size constraints.
Gene Prioritization: Rank genes by clinical evidence level.
Region Definition: Define target coordinates.
Probe Design: Generate capture probes or primers.
Coverage Simulation: Predict sequencing performance.
Optimization: Iterate design for uniformity.
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