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  • RNA-Seq Analysis

    • WORKSHOP NAME logo
  • I Introduction
  • Workshop Info
    • Class Photo
    • Schedule
    • Pre-work
  • Meet Your Faculty
  • II Modules
  • Module 0: Introductions and Environment Setup
    • Lecture
    • Environment Setup
    • Tool Installation
      • Note
      • SAMtools
      • bam-readcount
      • HISAT2
      • StringTie
      • gffcompare
      • htseq-count
      • TopHat
      • kallisto
      • FastQC
      • Fastp
      • MultiQC
      • Picard
      • RegTools
      • RSeQC
      • bedops
      • gtfToGenePred
      • genePredToBed
      • how_are_we_stranded_here
      • Install Cell Ranger
      • Install R
      • R Libraries
      • Bioconductor
      • Sleuth
      • PRACTICAL EXERCISE 1 - Software Installation
      • Add locally installed tools to your PATH [OPTIONAL]
      • Installing tools from official ubuntu packages [OPTIONAL]
      • Installing tools by Docker image
      • Installing tools by Docker image (using Singularity)
  • Module 1
    • Lecture
    • Labs
      • Module 1 - Key concepts
      • Module 1 - Learning objectives
      • Lecture
      • FASTA/FASTQ/GTF mini lecture
      • Obtain a reference genome from Ensembl, iGenomes, NCBI or UCSC.
      • Note on complex commands and scripting in Unix
      • PRACTICAL EXERCISE 2 (ADVANCED)
      • FASTA/FASTQ/GTF mini lecture
      • Obtain Known Gene/Transcript Annotations
      • Definitions:
      • The Purpose of Gene Annotations (.gtf file)
      • Sources for obtaining gene annotation files formatted for HISAT2/StringTie/Ballgown
      • Important notes:
      • Indexing mini lecture
      • Create a HISAT2 index
      • Obtain RNA-seq test data.
      • Determining the strandedness of RNA-seq data (Optional)
      • PRACTICAL EXERCISE 3
  • Module 2
    • Lecture
    • Labs
      • Module 2 - Key concepts
      • Module 2 - Learning objectives
      • Lectures
      • Read trimming with Fastp
      • Use FastQC and multiqc to compare the impact of trimming
      • Clean up
      • PRACTICAL EXERCISE 5
      • Alignment mini lecture
      • HISAT2 alignment
      • Merge HISAT2 BAM files
      • PRACTICAL EXERCISE 6
      • Introduction
      • Visualization Part 1: Getting familiar with IGV
      • Visualization Part 2: Inspecting SNPs, SNVs, and SVs
      • Visualization Part 3: Automating Tasks in IGV
      • Indexing BAM files with samtools
      • Visualize alignments with IGV
      • PRACTICAL EXERCISE 7
      • BAM Read Counting
      • PRACTICAL EXERCISE 7
      • Alignment QC mini lecture
      • Use samtools and FastQC to evaluate the alignments
      • Create versions of our BAM files with only the chromosome 22 alignments
      • Using FastQC
      • Using Picard
      • RSeQC [optional]
      • MultiQC
      • View a pre-generated MultiQC report for full bam files
      • Part I - Obtaining the dataset & reference files
      • Part II - Data Preprocessing (QC & Trimming)
      • Part III - Alignment
      • Part IV - Post-alignment QC & IGV Visualization
      • Presenting Your Results
  • Module 3
    • Labs
      • Module 3 - Key concepts
      • Module 3 - Learning objectives
      • Lectures
      • Expression mini lecture
      • Use Stringtie to generate expression estimates from the SAM/BAM files generated by HISAT2 in the previous module
      • PRACTICAL EXERCISE 8
      • Differential Expression mini lecture
      • Ballgown DE Analysis
      • PRACTICAL EXERCISE 9
      • ERCC DE Analysis
      • Differential Expression mini lecture
      • edgeR DE Analysis
      • Differential Expression mini lecture
      • DESeq2 DE Analysis
      • Setup
      • Format htseq counts data to work with DESeq2
      • Filter raw counts
      • Specifying the experimental design
      • Construct the DESeq2 object piecing all the data together
      • Running DESeq2
      • Log-fold change shrinkage
      • Annotate gene symbols onto the DE results
      • Data manipulation
      • Save results to files
      • Briefly examine the top over-expressed genes
      • Perform some preliminary exploration of DE genes using webtools
      • Visualize overlap with a venn diagram. This can be done with simple web tools like:
      • Ballgown DE Visualization
      • Differential Expression Visualization
      • Viewing pairwise sample clustering
      • Part I - Expression Estimation
      • Part II - Differential Expression
      • Presenting Your Results
      • PART 0 : Obtaining Data and References
      • Part 1 : Data preprocessing
      • Part 2: Data alignment
      • Part 3: Expression Estimation
      • Part 4: Differential Expression Analysis
      • Part 5: Differential Expression Analysis Visualization

  • Sponsors

    Canadian Institutes of Health Research logo. Genome Canada logo. Ontario Genomics logo.

    Ontario Institute for Cancer Research logo. HPC4Health logo.

RNA-seq Analysis 2025

RNA-seq Analysis 2025

Faculty: Malachi Griffith and Obi Griffith

July 7-9, 2025

Workshop Info

Lecture slides are available here
Mini-lecture slides are available here
Lab instructions are available here

Class Photo

Schedule

Pre-work

You can find your pre-work here.