RNA-seq Data Aanalysis with R
Bioinformatics
R
Transcriptomics
Course Description
The RNA-seq Data Analysis with R course is designed to provide participants with the knowledge and skills needed to effectively analyze and interpret RNA sequencing (RNA-seq) data using the R programming language. Participants will learn the entire workflow, from raw data preprocessing to differential gene expression analysis and visualization. This hands-on course is suitable for researchers, biologists, and bioinformaticians interested in unlocking insights from gene expression data.
Learning Objectives
- Understand the principles of RNA-seq technology and its applications in gene expression analysis.
- Recognize the steps involved in the RNA-seq workflow, from sample preparation to data analysis.
- Perform quality control assessments on raw RNA-seq data.
- Trim, filter, and preprocess raw reads to ensure data quality.
- Assess the impact of sequencing quality on downstream analyses.
- Perform differential expression analysis to identify genes with significant expression changes between conditions.
- Apply statistical tests (e.g., DESeq2, edgeR) to assess differential expression.
- Interpret the results in terms of fold changes and adjusted p-values.