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FScanpy

A Machine Learning-Based Framework for Programmed Ribosomal Frameshifting Prediction

FScanpy is a comprehensive Python package designed for the prediction of Programmed Ribosomal Frameshifting (PRF) sites in nucleotide sequences. By integrating advanced machine learning approaches (HistGradientBoosting and BiLSTM-CNN) with the established FScanR framework, FScanpy provides robust and accurate PRF site predictions.

🌟 Key Features

🎯 Dual-Model Architecture

Short Model (HistGradientBoosting): Fast screening with 33bp sequences
Long Model (BiLSTM-CNN): Deep analysis with 399bp sequences
Ensemble Prediction: Customizable model weights for optimal performance

🚀 Versatile Input Support

Single/Multiple Sequences: Sliding window prediction across full sequences
Region-Based Analysis: Direct prediction on pre-extracted 399bp regions
BLASTX Integration: Seamless workflow with FScanR pipeline
Cross-Species Compatibility: Built-in databases for viruses, marine phages, Euplotes, etc.

📊 Advanced Visualization

Interactive Heatmaps: FS site probability visualization
Prediction Plots: Combined probability and confidence displays
Customizable Thresholds: Separate filtering for each model
Export Options: PNG, PDF, and interactive formats

⚡ High Performance

Optimized Algorithms: Efficient sliding window scanning
Batch Processing: Handle multiple sequences simultaneously
Flexible Thresholds: Tunable sensitivity for different use cases
Memory Efficient: Optimized for large-scale genomic data

🔧 Installation

Prerequisites

Python ≥ 3.7
All dependencies are automatically installed

Install via pip (Recommended)

pip install FScanpy

Install from Source

git clone https://github.com/your-org/FScanpy-package.git
cd FScanpy-package
pip install -e .

🚀 Quick Start

Basic Usage

from FScanpy import predict_prf

# Simple sequence prediction
sequence = "ATGCGTACGTTAGC..." # Your DNA sequence
results = predict_prf(sequence=sequence)

# View top predictions
print(results[['Position', 'Ensemble_Probability', 'Short_Probability', 'Long_Probability']].head(10))

Visualization

from FScanpy import plot_prf_prediction

# Generate prediction plot
results, fig = plot_prf_prediction(
    sequence=sequence,
    short_threshold=0.65,    # HistGB threshold
    long_threshold=0.8,      # BiLSTM-CNN threshold
    ensemble_weight=0.4,     # 40% Short, 60% Long
    title="PRF Prediction Results"
)

Advanced Usage

from FScanpy import PRFPredictor
import pandas as pd

# Create predictor instance
predictor = PRFPredictor()

# Batch prediction on pre-extracted regions
data = pd.DataFrame({
    'Long_Sequence': ['ATGCGT...' * 60, 'GCTATAG...' * 57]  # 399bp sequences
})
results = predictor.predict_regions(data, ensemble_weight=0.4)

# Sequence-level prediction with custom parameters
results = predictor.predict_sequence(
    sequence=sequence,
    window_size=1,           # Step size for sliding window
    ensemble_weight=0.3,     # Model weighting
    short_threshold=0.5      # Filtering threshold
)

🎛️ Ensemble Weight Configuration

The ensemble_weight parameter controls the contribution of each model:

ensemble_weight	Short Model	Long Model	Best For
0.2-0.3	20-30%	70-80%	High sensitivity, detecting subtle sites
0.4-0.5	40-50%	50-60%	Balanced detection (recommended)
0.6-0.7	60-70%	30-40%	Fast screening, high specificity

Weight Selection Examples

# High sensitivity (Long model dominant)
sensitive_results = predict_prf(sequence, ensemble_weight=0.2)

# Balanced approach (recommended)
balanced_results = predict_prf(sequence, ensemble_weight=0.4)

# Fast screening (Short model dominant)  
screening_results = predict_prf(sequence, ensemble_weight=0.7)

📊 Core Functions

Main Prediction Interface

predict_prf(
    sequence=None,           # Single/multiple sequences or None
    data=None,              # DataFrame with 399bp sequences or None
    window_size=3,          # Sliding window step size
    short_threshold=0.1,    # Short model filtering threshold
    ensemble_weight=0.4,    # Short model weight (0.0-1.0)
    model_dir=None         # Custom model directory
)

Visualization Function

plot_prf_prediction(
    sequence,               # Input DNA sequence
    window_size=3,          # Scanning step size
    short_threshold=0.65,   # Short model threshold for plotting
    long_threshold=0.8,     # Long model threshold for plotting
    ensemble_weight=0.4,    # Model weighting
    title=None,            # Plot title
    save_path=None,        # Save file path
    figsize=(12,8),        # Figure size
    dpi=300               # Resolution for saved plots
)

PRFPredictor Class Methods

predictor = PRFPredictor()

# Sequence prediction (sliding window)
predictor.predict_sequence(sequence, ensemble_weight=0.4)

# Region prediction (batch processing)
predictor.predict_regions(dataframe, ensemble_weight=0.4)

# Feature extraction
predictor.extract_features(sequences)

# Model information
predictor.get_model_info()

📈 Output Fields

Prediction Results

Position: Position in the original sequence
Ensemble_Probability: Final ensemble prediction (main result)
Short_Probability: HistGradientBoosting prediction (0-1)
Long_Probability: BiLSTM-CNN prediction (0-1)
Ensemble_Weights: Model weight configuration used

Sequence Information

Short_Sequence: 33bp sequence for Short model
Long_Sequence: 399bp sequence for Long model
Codon: 3bp codon at the prediction position
Sequence_ID: Identifier for multi-sequence inputs

🔬 Integration with FScanR

FScanpy works seamlessly with the FScanR pipeline for comprehensive PRF analysis:

from FScanpy import fscanr, extract_prf_regions, predict_prf

# Step 1: BLASTX analysis with FScanR
blastx_results = fscanr(
    blastx_data,
    mismatch_cutoff=10,
    evalue_cutoff=1e-5,
    frameDist_cutoff=10
)

# Step 2: Extract PRF candidate regions
prf_regions = extract_prf_regions(original_sequence, blastx_results)

# Step 3: Predict with FScanpy
final_predictions = predict_prf(data=prf_regions, ensemble_weight=0.4)

📚 Documentation

Complete Tutorial: Comprehensive usage guide with examples
Demo Notebook: Interactive examples and workflows
Example Scripts: Ready-to-run code examples

🎯 Use Cases

1. Viral Genome Analysis

# Scan viral genome for PRF sites
viral_sequence = load_viral_genome()
prf_sites = predict_prf(viral_sequence, ensemble_weight=0.3)
high_confidence = prf_sites[prf_sites['Ensemble_Probability'] > 0.8]

2. Comparative Genomics

# Compare PRF patterns across species
species_data = pd.DataFrame({
    'Species': ['Virus_A', 'Virus_B'],
    'Long_Sequence': [seq_a_399bp, seq_b_399bp]
})
comparative_results = predict_prf(data=species_data)

3. High-Throughput Screening

# Fast screening of large sequence datasets
sequences = load_large_dataset()
screening_results = predict_prf(
    sequence=sequences,
    ensemble_weight=0.7,  # Fast screening mode
    short_threshold=0.3
)

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

📝 Citation

If you use FScanpy in your research, please cite:

@software{fscanpy2024,
  title={FScanpy: A Machine Learning Framework for Programmed Ribosomal Frameshifting Prediction},
  author={[Author names]},
  year={2024},
  url={https://github.com/your-org/FScanpy}
}

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

Documentation: Tutorial
Usage Example: Demo Notebook
Predict Result Explain: Predict Result Explain

🏗️ Dependencies

FScanpy automatically installs all required dependencies:

numpy>=1.24.3
pandas>=2.2.3
tensorflow>=2.10.1
scikit-learn>=1.6.0
matplotlib>=3.9.4
joblib>=1.4.2
biopython>=1.85
wrapt>=1.17.0

FScanpy - Advancing programmed ribosomal frameshifting research through machine learning 🧬

9.1 KiB Raw Blame History