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Gene panel design agent

Skill rbr7/MedClawMini/skills/gene-panel-design-agent

A focused, production-minded library of 197 clinical-AI and healthcare data-science skills for the OpenClaw agent platform featuring data quality, clinical NLP, big-data ML, explainable AI, drug safety, and regulatory.

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npx -y skills add rbr7/MedClawMini --skill gene-panel-design-agent

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AI-powered design of targeted gene panels for clinical and research applications including cancer diagnostics, pharmacogenomics, and rare disease testing.

SKILL.md

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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

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

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