Informatics for RNA-seq Analysis

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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. Must be able to complete and understand the following simple Linux and R tutorials (up to and including “Descriptive Statistics”) before attending:

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, you may loan one from the CBW. Please contact course_info@bioinformatics.ca for more information.

Pre-work and pre-readings can be found at https://bioinformaticsdotca.github.io/rnaseq_2018.

Course Material