Course Description

This introductory course covers practical aspects of the analysis of differential gene expression by RNAseq, from planning the gathering of sequence data to the generation of tables of differentially expressed gene lists and visualization of results. We we will also cover some of the initial steps of secondary analysis, such as functional enrichment of the obtained gene lists. Participants will first start learning the concepts using small example datasets, and then will apply the learned concepts in the training room using real world examples. Participants will be shown different alternatives for data analysis so they can achieve maximum autonomy after the course.

Target Audience

Life Scientists who want to be able to use NGS data (RNAseq) to infer genes differentially expressed between different conditions. Computational researchers that wish to get acquainted with the concepts and methodologies used in RNAseq are also welcome.

Course Documentation

Note - All the datasets used for this training course is available in the following button. You need to unzip this file and follow the instructions throughout the documentation.

Download ADER18F Datasets _{File Size: 167,9MB}

Day 1

1 - Plan your experiment using NGS technologies:

• 1.1 - List possibilities and limitations of NGS sequencing technologies

  What choices do you have when sending your samples to the sequencing facility
  

• 1.2 - Choose adequate sequencing for your biological question

  How do the sequencing choices influence the kind of questions you can answer
  

2 - List steps in the analysis of RNAseq differential expression experiments

    What are the steps in RNAseq data analysis

3 - Assess the general quality of the raw data from the sequencing facility

• 3.1 - Interpret what are fastq files and what is their content

  What information is in fastq files, and how is it organized
  

• 3.2 - Use software like FastQC to process fastq files and produce QC reports

  Detect low quality bases in the QC reports  
  Detect sequence bias and possible presence of adaptors and other contaminants
  

4 - Do simple processing operations in the raw data to improve its quality

• 4.1 - Use trimmomatic to remove low quality bases from your reads

  Use trimmomatic to filter/trim low quality bases from your reads
  

• 4.2 - Use trimmomatic to remove adaptors and other artefactual sequences from your reads

  Remove adaptors (such as illumina adaptors) from your reads
  Check results using FastQC on filtered data
  

5 - Generate alignments of processed reads against a reference genome

• 5.1 - What is a reference genome, versioning and where to obtain genomes

  Are genomes constant?
  Obtain genome fasta from Ensembl
  

• 5.2 - Alignment software: hisat2; salmon

  What are the requisites for using burrows-wheeler approaches?
  Prepare a reference genome to use with hisat2
  

• 5.3 - Run an alignment: the SAM/BAM alignment format

  Run hisat2 in an example dataset
  What is the SAM/BAM format;  what is the BAM format
  

Day 2

6 - Assess the general quality of the alignments and detect possible problems

• 6.1 - What is a reference gene annotation, versioning and where to obtain

  What is the GFF/GTF format
  Obtain genome GTF from Ensembl
  

• 6.2 - Visualizing alignments in IGV for single genes

• 6.3 - Use Qualimap to assess quality of alignments

  Interpret general alignment statistics such as percentage of aligned reads
  Check the reports to assess RNA integrity and diversity
  

7 - Generate tables of counts

• 7.1 - The process of generating gene counts from genome aligments

  What parameters we need to consider when counting
  

• 7.2 - Use featurecounts to generate table of gene counts

• 7.3 - Using Salmon to generate counts only with the transcriptome

Day 3

8 - Generate lists of differentially expressed genes, at least for a simple pairwise comparison

• 8.1 - Using the R package edgeR and DESeq2 to produce a pairwise differential expression analysis

  Use Galaxy to produce differentially expressed genes with edgeR and DESeq2
  Use edgeR and DESeq2 in R and RStudio
  

• 8.2 - Interpretation and visualization of results

  Produce PCA plots comparing all samples: outlier detection
  Visualize expression profiles of top differentially expressed genes
  Produce other plots such as vulcano plots
  

• 8.3 - Use more complex settings: Generalized Linear Models

  Account for confounders using Generalized Linear Models
  Performing ANOVA-like comparisons
  

• 8.4 - Gain control over your analysis using R and Rstudio

  Use R in Rstudio to make a pairwise comparison using DESeq2 and edgeR
  Use edgeR to perform more complex analysis such as ANOVA-like all versus all comparisons
  

Day 4

9 - Perform simple functional enrichment analysis and understand the concepts involved (Slides(pdf))

• 9.1 - How to extract meaning from a list of genes

  What are functional annotations, what types exist, and where to get them
  

• 9.2 - Understand the concept of functional enrichment analysis, and the statistics involved

  What is enrichment analysis and how is it performed
  How to define sample and population sets
  Why do we need multiple test corrections
  

• 9.3 - Interpret the results of functional enrichment analysis

  What can we get from enrichment analysis results
  Using functional enrichment analysis with real lists of genes
  

Learning Objectives and Course Pre-requisites

Instructors

The source for this course webpage is on github.

ADER18F by GTPB is licensed under a Creative Commons Attribution 4.0 International License.