Bisulfite Conversion Efficiency Analysis Pipeline

This repository provides a complete and modular pipeline to simulate, analyze, validate, and visualize bisulfite sequencing data for accurate estimation of bisulfite conversion efficiency. It supports multiple measurement strategies, validation metrics, and context-specific analyses.

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Repository Structure

• bisulfite_simulation.py – Data simulation engine
• bisulfite_metrics.py – Efficiency analysis and metric calculations
• bisulfite_validation.py – Validation and visualization tools
• bisulfite_complete_pipeline.py – Full pipeline integration
• bisulfite_demo.py – Demonstration script

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Pipeline Components

1. Data Simulation (bisulfite_simulation.py)

• BisulfiteSimulator class with biological realism
• Simulates genomes with custom GC content
• Context-specific methylation (CpG, CHG, CHH)
• Bisulfite conversion with variable efficiency
• Realistic sequencing reads with quality scores and error models

2. Metrics and Analysis (bisulfite_metrics.py)

• ConversionEfficiencyAnalyzer supports:
  1. Non-CpG method – CHG/CHH cytosines as proxies
  2. Lambda DNA method – Simulates unmethylated spike-ins
  3. CHH-specific method – Focus on rarely methylated sites
  4. Position-specific rates – Genome-wide patterns
  5. Context-dependent analysis – Local sequence effects
  6. Bootstrap CI – Confidence interval estimation

3. Validation and Visualization (bisulfite_validation.py)

• ValidationFramework for performance evaluation
• VisualizationSuite for high-quality plots
• Metrics include R², MAE, RMSE, correlation, and bias
• Method agreement, cross-validation, and reporting

4. Complete Integration (bisulfite_complete_pipeline.py)

• CompleteBisulfitePipeline class for end-to-end execution
• Automates simulation → analysis → validation → output
• Includes benchmarking and method comparison
• Saves results, plots, and summary reports

5. Demonstration (bisulfite_demo.py)

• Full working example of pipeline usage
• Step-by-step walkthrough of each module
• Visualizations and interpretation of results
• Best practices and tips

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Conversion Efficiency Methods

Method	Description	Notes
Non-CpG	Uses CHG/CHH cytosines (rarely methylated)	General-purpose, robust
Lambda DNA	Simulates unmethylated control (spike-in) DNA	Good for bias correction
CHH Context	Focuses on CHH sites (<5% methylation in mammals)	Higher variance, good for validation
Confidence Intervals	Bootstrap-based uncertainty estimation	Adds statistical rigor

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Validation Metrics

• Accuracy: R², MAE, RMSE, correlation
• Bias: Systematic over/under-estimation
• Precision: Within ±1%, ±2%, ±5% thresholds
• Consistency: Method agreement (coefficient of variation)
• Coverage Effect: Accuracy vs. sequencing depth
• Range Sensitivity: Efficiency-range-specific accuracy

Quality Thresholds

Category	R²	MAE
Acceptable	> 0.90	< 0.02
Good	> 0.95	< 0.01
Excellent	> 0.98	< 0.005

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Experimental Recommendations

1. Primary Method: Use Non-CpG analysis for routine estimation
2. Validation: Combine Lambda DNA spike-ins with other methods
3. Coverage: Aim for ≥ 20x sequencing depth
4. Controls: Include positive (unmethylated) and negative (methylated)
5. Replication: Use technical replicates for sensitive samples

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Quick Start

from bisulfite_complete_pipeline import CompleteBisulfitePipeline

# Initialize pipeline
pipeline = CompleteBisulfitePipeline(output_dir="Results_Demo")

# Run the complete analysis
results = pipeline.run_complete_analysis(
    genome_length=10000,
    efficiency_range=(0.90, 0.999),
    n_efficiency_points=8,
    coverage=30
)

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Contact: 🤚

Important

For any questions please contact: 👉 Ashok K. Sharma; ashoks773@gmail.com or compbiosharma@gmail.com