FScanpy-package/README.md

# 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)](https://en.wikipedia.org/wiki/Ribosomal_frameshift) sites in nucleotide sequences. By integrating advanced machine learning approaches (Gradient Boosting and BiLSTM-CNN) with the established [FScanR](https://github.com/seanchen607/FScanR.git) framework, FScanpy provides robust and accurate PRF site predictions. The package requires input sequences to be in the positive (5' to 3') orientation.

![FScanpy Architecture](/tutorial/image/structure.jpeg)

For detailed documentation and usage examples, please refer to our [tutorial](tutorial/tutorial.md).

## Core Features
- **Sequence Feature Extraction**: Support for extracting features from nucleic acid sequences, including base composition, k - mer features, and positional features.
- **Frameshift Hotspot Region Prediction**: Predict potential PRF sites in nucleotide sequences using machine learning models.
- **Feature Extraction**: Extract relevant features from sequences to assist in prediction.
- **Cross - Species Support**: Built - in databases for viruses, marine phages, Euplotes, etc., enabling PRF prediction across various species.

## Main Advantages
- **High Accuracy**: Integrates multiple machine learning models to provide accurate PRF site predictions.
- **Efficiency**: Utilizes a sliding window approach and feature extraction techniques to rapidly scan sequences.
- **Versatility**: Supports PRF prediction across various species and can be combined with the [FScanR](https://github.com/seanchen607/FScanR.git) framework for enhanced accuracy.
- **User - Friendly**: Comes with detailed documentation and usage examples, making it easy for researchers to use.
- **Flexible**: Provides different resolutions to suit different using situations.

## Installation Requirements
- Python ≥ 3.7
- Dependencies are automatically handled during installation

### Option 1: Install via pip
```bash
pip install FScanpy
```

### Option 2: Install from source
```bash
git clone git@60.204.158.188:yyh/FScanpy-package.git
cd FScanpy-package
pip install -e .
```

## Authors


## Citation
If you utilize FScanpy in your research, please cite our work:

```bibtex
[Citation details will be added upon publication]
```