Description Target Audience Prerequisites Outline

Course Description

High-throughput sequencing of RNA libraries (RNA-seq) has become increasingly common and largely supplanted gene microarrays for transcriptome profiling. When processed appropriately, RNA-seq data has the potential to provide a considerably more detailed view of the transcriptome. The CBW has developed a 3-day course providing an introduction to RNA-seq data analysis followed by integrated tutorials demonstrating the use of popular RNA-seq analysis packages. The tutorials are designed as self-contained units that include example data (Illumina paired-end RNA-seq data) and detailed instructions for installation of all required bioinformatics tools (HISAT, StringTie, etc.).

Course Objectives

Participants will gain practical experience and skills to be able to:

Perform command-line Linux based analysis on the cloud
Assess quality of RNA-seq data
Align RNA-seq data to a reference genome
Estimate known gene and transcript expression
Perform differential expression analysis
Discover novel isoforms
Visualize and summarize the output of RNA-seq analyses in R
Assemble transcripts from RNA-Seq data.

Target Audience

Graduates, postgraduates, and PIs working or about to embark on an analysis of RNA-seq data. Attendees may be familiar with some aspect of RNA-seq analysis (e.g. gene expression analysis) or have no direct experience.

Prerequisites

Basic familiarity with Linux environment and S, R, or Matlab.

You will also require your own laptop computer. Minimum requirements: 1024×768 screen resolution, 1.5GHz CPU, 2GB RAM, 10GB free disk space, recent versions of Windows, Mac OS X or Linux (Most computers purchased in the past 3-4 years likely meet these requirements). If you do not have access to your own computer, please contact support@bioinformatics.ca for other possible options.

This workshop requires participants to complete pre-workshop tasks and readings.

Course Outline

Module 1: Introduction to Cloud Computing

Introduction to cloud computing concepts

Module 2: Introduction to RNA sequencing and analysis

Basic introduction to biology of RNA-seq
Experimental design and analysis considerations
Commonly asked questions

Lab Practical

Introduction to the test data
Examine and understand the format of raw FastQ files
Obtain reference genomes (fasta) and gene annotation resources (GTF/GFF)
Perform pre-alignment QC

Module 3: RNA-Seq alignment and visualization

RNA-seq alignment challenges and common questions
Alignment strategies
Introduction to HISAT2
Introduction to the BAM and BED formats
Basic manipulation of BAMs with samtools, Picard, etc.
Visualization of RNA-seq alignments – IGV
Alignment QC Assessment
BAM read counting and determination of variant allele expression status

Lab Practical

Run HISAT2 with parameters suitable for gene expression analysis
Use samtools to explore and manipulate the features of the SAM/BAM files
Use IGV to visualize HISAT2 alignments, view a variant position, load exon junctions files, etc.
Determine BAM-read counts at a variant position
Use samtools flagstat, samstat, FastQC to assess quality of alignments

Module 4: Expression and differential expression

Expression estimation for known genes and transcripts
FPKM/TPM expression estimates vs. raw counts
Differential expression methods
Downstream interpretation of expression and differential expression estimates

Lab Practical

Generate gene/transcript expression estimates with StringTie
Perform differential expression analysis with Ballgown
Summarize and visualize differential expression results

Module 5: Reference free alignment

Explore the use of Kallisto to get abundance estimates without first aligning to a reference

Module 6: Isoform discovery and alternative expression

Explore use of StringTie in reference annotation based transcript (RABT) assembly mode and de novo assembly mode. Both modes require a reference genome sequence.