The R_Python_Bioinformatics_Scripts repository, is a collection of bioinformatics scripts developed in R and Python. These scripts are designed to assist researchers and practitioners in performing various bioinformatics analyses and visualizations.

Repository Structure

The repository is organized into several directories, each containing scripts and resources for specific bioinformatics tasks:

• Automagical-R-Script-Plotting-main: This directory includes R scripts that automate the generation of various plots commonly used in bioinformatics, facilitating efficient data visualization.

• GEO_Dataset_Python: Contains Python scripts for accessing and analyzing datasets from the Gene Expression Omnibus (GEO) database, enabling users to retrieve and process gene expression data.

• python_for_bioinformaticians: Offers Python scripts tailored for bioinformatics applications, covering tasks such as sequence analysis, data parsing, and other computational biology operations.

Getting Started

To utilize the scripts in this repository:

1. Clone the Repository:

   git clone https://github.com/pritampanda15/R_Python_Bioinformatics_Scripts.git

2. Navigate to a Directory:

   cd R_Python_Bioinformatics_Scripts/Automagical-R-Script-Plotting-main

3. Review Documentation: Each directory may contain its own README or documentation detailing the purpose of the scripts, installation requirements, and usage instructions.

Contributing

Contributions to enhance the repository are welcome. To contribute:

1. Fork the Repository: Click on the 'Fork' button at the top right corner of the repository page.

2. Create a New Branch: For your feature or bug fix.

   git checkout -b feature-name

3. Make Changes: Implement your feature or fix.

4. Commit Changes:

   git commit -m "Description of changes"

5. Push to Your Fork:

   git push origin feature-name

6. Submit a Pull Request: Navigate to your forked repository on GitHub and click on 'New Pull Request'.

License

The repository does not specify a license. It's advisable to contact the repository owner for clarification before using the code in commercial or open-source projects.

For more details, visit the R_Python_Bioinformatics_Scripts repository.