Abstract

In recent years, RNA sequencing (in short RNA-Seq) has become a very widely used technology to analyze the continuously changing cellular transcriptome, i.e. the set of all RNA molecules in one cell or a population of cells. One of the most common aims of RNA-Seq is the profiling of gene expression by identifying genes or molecular pathways that are differentially expressed (DE) between two or more biological conditions. This tutorial demonstrates a computational workflow for the detection of DE genes and pathways from RNA-Seq data by providing a complete analysis of an RNA-Seq experiment profiling Drosophila cells after the depletion of a regulatory gene.

About This Material

This is a Hands-on Tutorial from the GTN which is usable either for individual self-study, or as a teaching material in a classroom.

Questions this will address

• What are the steps to process RNA-Seq data?
• How to identify differentially expressed genes across multiple experimental conditions?
• What are the biological functions impacted by the differential expression of genes?

Learning Objectives

• Check a sequence quality report generated by FastQC for RNA-Seq data
• Explain the principle and specificity of mapping of RNA-Seq data to an eukaryotic reference genome
• Select and run a state of the art mapping tool for RNA-Seq data
• Evaluate the quality of mapping results
• Describe the process to estimate the library strandness
• Estimate the number of reads per genes
• Explain the count normalization to perform before sample comparison
• Construct and run a differential gene expression analysis
• Analyze the DESeq2 output to identify, annotate and visualize differentially expressed genes
• Perform a gene ontology enrichment analysis
• Perform and visualize an enrichment analysis for KEGG pathways