se7en69 / bioinformatics-data-analysis

This repository contains the course materials for the "Bioinformatics Data Analysis from Scratch" course, which covers the fundamentals of bioinformatics programming using Python, R, and Linux. The course is designed for beginners with no prior experience in bioinformatics, providing a comprehensive introduction to biological programming.

Bioinformatics-Data-Analysis-from-Scratch

This repository contains the course materials for the "Bioinformatics Data Analysis from Scratch" course, which covers the fundamentals of bioinformatics programming using Python, R, and Linux. The course is designed for beginners with no prior experience in bioinformatics, providing a comprehensive introduction to biological programming.

Course Modules:

Introduction to Biological Programming: Learn the basics of programming in Python, R, and Linux, focusing on bioinformatics applications.

Python Language for Bioinformatics: Explore the Biopython library for bioinformatics, covering sequence analysis, file parsing, and more.

Python for Bioinformatics Application Development: Develop bioinformatics tools and applications using Tkinter, including alignment tools, genome annotation tools, and more.

Bash for Bioinformatics: Learn how to use Linux commands for bioinformatics tasks, including data manipulation and analysis.

Understanding Bioinformatics Pipeline: Gain insights into the design and implementation of bioinformatics pipelines for data analysis.

NGS Data Analysis on Bash: Dive into gene expression analysis using command-line tools for Next-Generation Sequencing (NGS) data.

Variant Calling on Bash: Learn about variant calling and how to perform it using Bash scripts.

R for Bioinformatics: Explore bioinformatics data analysis and visualization using R, focusing on microarray analysis.

Microarray Analysis on R: Learn how to analyze and interpret microarray data using R.

GitHub Guide for Students: Get a practical guide on using GitHub for version control and collaboration in bioinformatics projects.

Requirements:

Basic understanding of biology and genetics. Access to a computer with Python, R, and Linux installed.

Who is This Course For?

Students and professionals interested in bioinformatics and biological data analysis. Anyone looking to learn programming for bioinformatics applications.

Final Project:

The final project involves aligning given sequences using Python, R, and Linux, providing a practical application of the skills learned throughout the course.