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This is a Python implementation of an off-target prediction pipeline for CRISPR-based gene therapy using CasOFFinder and machine learning techniques.

The pipeline consists of four main steps:

1. Input guide RNA sequence: Provide the guide RNA sequence of interest to target a specific genomic locus.

2. Off-target site prediction: Use CasOFFinder to identify potential off-target sites for the guide RNA sequence. Extract relevant features from the off-target sites, such as mismatch positions, number of mismatches, chromatin accessibility, and DNA methylation.

3. Machine learning model training: Train a machine learning model using the extracted features and labels (cleavage or no cleavage) for the off-target sites. The model can be tuned to optimize accuracy, specificity, or other performance metrics.

4. Off-target site evaluation: Use the trained machine learning model to predict the likelihood of off-target cleavage for new off-target sites based on their features. The predictions can be used to assess the safety and efficacy of CRISPR-based gene therapy approaches.

To use this pipeline, you need to have the following dependencies installed:

• Python 3.x
• CasOFFinder (https://github.com/snugel/cas-offinder)
• scikit-learn (https://scikit-learn.org/stable/)
• pandas (https://pandas.pydata.org/)
• numpy (https://numpy.org/)

You can install the Python libraries using pip or conda:

pip install scikit-learn pandas numpy

To use the pipeline, follow these steps:

1. Clone or download the repository to your local machine.

2. Open off_target_prediction.py in a Python IDE or text editor.

3. Modify the input_sequence variable to provide the guide RNA sequence of interest.

4. Run the script using a Python interpreter.

5. After the script completes, examine the generated files in the output directory. These files include:

• off_target_sites.txt: a list of potential off-target sites identified by CasOFFinder.
• off_target_features.csv: a table of extracted features for the off-target sites.
• off_target_labels.csv: a table of labels (cleavage or no cleavage) for the off-target sites.
• trained_model.pkl: a serialized machine learning model trained on the extracted features and labels.

6. To evaluate the model on new off-target sites, modify test_prediction.py and execute it using the trained model and the new off-target site features.

• CasOFFinder: https://github.com/snugel/cas-offinder
• scikit-learn: https://scikit-learn.org/stable/
• pandas: https://pandas.pydata.org/
• numpy: https://numpy.org/