Informatics for RNA-seq Analysis

Course Objectives
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.).
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: 1024x768 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 course_info@bioinformatics.ca for other possible options.
This workshop requires participants to complete pre-workshop tasks and readings.
Course Material
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Module 1: Introduction to Cloud Computing
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Module 2: Introduction to RNA sequencing and analysis
Content:
- Basic introduction to biology of RNA-seq
- Experimental design and analysis considerations
- Commonly asked questions
Presentation file(s):
Lab Practical
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Module 3: RNA-Seq alignment and visualization
Content:
- 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
Presentation file(s):
Lab Practical
Content:
- 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
Presentation file(s):
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Module 4: Expression and differential expression
Content:
- 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
Presentation file(s):
Lab Practical
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Module 5: Reference free alignment
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Module 6: Isoform discovery and alternative expression
Content:
- Explore use of StringTie in reference annotation based transcript (RABT) assembly mode and de novo assembly mode. Both modes require a reference genome sequence.
Presentation file(s):
Lab Practical
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Module 7: Genome-Free De Novo Transcript Assembly
Content:
- Reconstructing transcripts using Trinity
- Genome-free transcript quantification and differential expression analysis
Presentation file(s):
Lab Practical
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Module 8: Functional Annotation and Analysis of Transcripts
Lab Practical