| Audience | Computational Skills | Prerequisites | Duration |
|---|---|---|---|
| Biologists | Beginner/Intermediate | None | 2-day workshop (~13 hours of trainer-led time) |
This repository has teaching materials for a 2-day Introduction to RNA-sequencing data analysis workshop. This workshop focuses on teaching basic computational skills to enable the effective use of an high-performance computing environment to implement an RNA-seq data analysis workflow. It includes an introduction to shell (bash) and shell scripting. In addition to running the RNA-seq workflow from FASTQ files to count data, the workshop covers best practice guidlelines for RNA-seq experimental design and data organization/management.
These materials were developed for a trainer-led workshop, but are also amenable to self-guided learning.
- Understand the necessity for, and use of, the command line interface (bash) and HPC for analyzing high-throughput sequencing data.
- Understand best practices for designing an RNA-seq experiment and analysis the resulting data.
The schedule for using the materials in a trainer-led workshop can be found here
| Lessons | Estimated Duration |
|---|---|
| Introduction to the shell | 70 min |
| Searching and redirection in shell | 45 min |
| Introduction to the Vim text editor | 30 min |
Shell scripts and for loops |
75 min |
| Permissions and environment variables | 50 min |
| Project and data organization | 40 min |
| Introduction to High-Performance Computing for HMS-RC's O2 cluster | 45 min |
| Introduction to RNA-seq and Library Prep | 50 min |
| NGS Workflows and Data Standards | 35 min |
| RNA-seq data QC with FastQC | 55 min |
| RNA-seq Alignment with STAR | 75 min |
| Assessing Alignment Quality | 60 min |
| Generating a Count Matrix | 75 min |
| Documenting Steps in the Workflow with MultiQC | 30 min |
| Automating the RNA-seq workflow | 60 min |
| Alternative workflows for analyzing RNA-seq data | 15 min |
| Quantifying expression using alignment-free methods (Salmon) | 75 min |
These materials have been developed by members of the teaching team at the Harvard Chan Bioinformatics Core (HBC). These are open access materials distributed under the terms of the Creative Commons Attribution license (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- Some materials used in these lessons were derived from work that is Copyright © Data Carpentry (http://datacarpentry.org/). All Data Carpentry instructional material is made available under the Creative Commons Attribution license (CC BY 4.0).