R Language in Bioinformatics

Description:

This course introduces participants to the fundamentals of R programming with a focus on its applications in bioinformatics, particularly in handling and analyzing multi-omics data. Participants will learn key concepts in R language, data manipulation, visualization, and multi-omics integration. The course will include hands-on sessions with real-world bioinformatics datasets and practical tools such as MOFA for integrating multi-omics data. By the end of the course, students will be able to write custom functions, manipulate data efficiently, visualize results, and perform multi-omics integration for various bioinformatics applications.

Course Level: beginner, Intermediate.

Eligible Audience:

• Bioinformaticians looking to expand their programming skills with R.
• Data Scientists working in bioinformatics or with biological data.
• Researchers in the fields of genomics, proteomics, and multi-omics who seek to integrate data and develop analytical pipelines.
• Graduate Students and professionals interested in leveraging R for bioinformatics analysis.

Content:

Session 1: Getting Started with R

• Introduction to R and its role in bioinformatics.
• Installation of R and RStudio.
• Basic R syntax and understanding R data types.
• Setting up R for bioinformatics applications.

Session 2: Data Manipulation with R

• Working with data frames: importing, cleaning, and modifying data.
• Using conditional statements and loops for dynamic data manipulation.
• Introduction to ggplot2 for data visualization.
• Creating impactful visualizations for bioinformatics data.

Session 3: Writing Custom Functions

• Writing custom functions to streamline repetitive tasks.
• Overview of function syntax and error handling.
• Project 1: Complementary Sequence — Apply custom functions to solve a real-world bioinformatics problem, like finding complementary sequences in DNA.

Session 4: Multi-Omics Integration with R

• Introduction to multi-omics data and its importance in modern biological research.
• Challenges in integrating diverse omics data types (genomics, proteomics, transcriptomics).
• Simulating a multi-omics dataset and understanding its integration.

Session 5: Hands-on MOFA

• Introduction to the MOFA (Multi-Omics Factor Analysis) R package.
• Practical session applying MOFA to integrate multi-omics datasets.
• Real case study application, reinforcing theoretical concepts and analytical techniques.

Course Outcomes:

By the end of this course, participants will be able to:

• Set up and navigate R and RStudio for bioinformatics.
• Manipulate and clean biological data efficiently using data frames, conditional statements, and loops.
• Create visualizations of bioinformatics data using ggplot2 for insightful representation.
• Write custom functions to automate bioinformatics workflows.
• Integrate multi-omics datasets using R tools like MOFA.

Prerequisites:

• Basic knowledge of bioinformatics concepts (e.g., genomics, proteomics).
• A computer with at least 8GB of RAM is recommended, though 16GB or more would be ideal for heavy data processing..
• Willingness to engage in hands-on coding and projects.

Instructor:

Dr.Mohamed Eman

• PhD student in the Evolutionary Genomics and Bioinformatics group at the Faculty of Science, University of Porto, Portugal, 2022
• MSc in Computer Science, Bioinformatics major, Nile University, Egypt, 2021
• Graduated from the Biotechnology Department, Cairo University, Giza, Egypt, 2019″