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Description:

This course is an intensive, hands-on introduction to RNA-Seq data analysis, designed for beginners and those looking to strengthen their bioinformatics skills. Participants will learn the end-to-end process of RNA-Seq data analysis, from preprocessing and quality control to downstream analysis and visualization. Alongside learning the principles of RNA-Seq, participants will gain essential Bash scripting skills to streamline their data analysis workflows. The course culminates in a collaborative group project, allowing participants to apply their knowledge to a real-world dataset.

 

Eligible Audience:

  • Graduate students, researchers, and professionals interested in bioinformatics or RNA-Seq analysis.
  • Individuals new to bioinformatics or with limited programming experience.
  • Scientists from molecular biology, genomics, or related fields seeking practical data analysis skills.

 

Course Outline

Session 1: Introduction to RNA-Seq & Bash

  • Objective: Understand RNA-Seq basics and its applications.
  • Basics of RNA-Seq technology and its use in research.
  • Overview of Bash scripting for bioinformatics.

Session 2: Introduction to Bash

  • Objective: Master basic Bash commands and scripting techniques.
  • Command-line navigation and basic commands.
  • Writing and running simple scripts with loops and conditionals.

Session 3: Preprocessing RNA-Seq Data

  • Objective: Ensure data quality and prepare for analysis.
  • Tools and methods for quality control and trimming (FastQC and Trimmomatic).
  • Hands-on: Analyze sample datasets for quality assurance.

Session 4: Alignment and PCA

  • Objective: Perform pseudoalignment and exploratory data analysis.
  • Introduction to Kallisto for efficient pseudoalignment.
  • Hands-on: Use Kallisto to align sample datasets and visualize principal component analysis (PCA).

Session 5: Downstream Analysis and Normalization

  • Objective: Identify meaningful biological insights from RNA-Seq data.
  • Differential expression analysis using DESeq2.
  • Normalization techniques and their significance.
  • Visualization tools: Create volcano plots and heatmaps for data presentation.

Session 6: Group Project

  • Objective: Apply knowledge to a real dataset in a collaborative environment.
  • Analyze RNA-Seq data from preprocessing to differential expression.
  • Prepare and deliver a presentation summarizing key findings and visualizations.

 

Course Outcomes:

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

  1. Understand the principles and applications of RNA-Seq.
  2. Use Bash commands and scripts to automate bioinformatics workflows.
  3. Perform RNA-Seq data preprocessing, quality control, and alignment.
  4. Conduct differential expression analysis and normalization.
  5. Visualize and interpret RNA-Seq data using plots and heatmaps.
  6. Collaborate on a data analysis project and present insights effectively.

 

Instructor:

Dr. Mohamed Emam

  • PhD student at the Evolutionary genomics, Bioinformatics group at Faculty of Science, University of 2022 Porto, Portugal
  • MSc in computer science, bioinformatics major, Nile University, Egypt 2021
  • Graduated from Biotechnology Department, Cairo University, Giza(Egypt) 2019