Welcome to the Bioinformatics Training Internship repository! This repository contains all the materials, code, and resources used during the bioinformatics training program. The training is designed to equip interns with essential skills and knowledge to thrive in the field of bioinformatics.

1. Introduction
2. Curriculum
3. Getting Started
4. Folder Structure
5. License
6. Contributing
7. Contact

Bioinformatics is a rapidly evolving field that combines biology, computer science, and data analysis. This internship training aims to provide hands-on experience and theoretical knowledge in various bioinformatics tools and techniques. By the end of this program, interns will have gained proficiency in analyzing biological data, interpreting results, and using computational tools to address biological research questions.

The training curriculum covers a diverse range of topics, including:

• Introduction to Slack, Git, and GitHub
• Introduction to UNIX command-line
• Shell scripting
• Introduction to Conda
• QC of raw sequence data and preprocessing (Trimming)
• Genome assembly
• Strain typing
• Database querying (AMR, Virulence)
• Phylogenetic analysis
• Miniproject: Working on reproducing a research article
• Introduction to Python and machine learning for bioinformatics
• Nextflow and Docker
• Introduction to R programming for Visualization
• Soft skills https://figshare.com/s/eda6d720b994efac1cf3

To get started with the training materials, follow these steps:

1. Clone this repository to your local machine using git clone [email protected]:AMR-Bioinformatics/Bioinformatics-Training.git.

2. Navigate to the specific topic folders to access the slides, scripts, and datasets related to each topic.

3. Review the README.md files in each folder for additional instructions and explanations.

The repository is organized as follows:

• Slides/: Presentation slides for each topic.
• Scripts/: Code examples and scripts used during the training.
• Datasets/: Example datasets and raw sequence data.
• Mini_Project/: Files related to the mini-project.
• Python_and_ML/: Jupyter notebooks and Python scripts for Python and machine learning topics.
• R_Visualization/: R scripts for data visualization.
• Nextflow_and_Docker/: Nextflow workflow and Dockerfile for reproducible analyses.
• Images/: Images used in the repository.

This project is licensed under the MIT License.

Contributions to this repository are welcome! If you have any improvements, suggestions, or additional resources to share, please feel free to create pull requests.

If you have any questions or need further assistance, please contact us at [email protected].

Happy learning and exploring the fascinating world of bioinformatics!