THIS REPO IS ARCHIVED, PLEASE GO TO https://hbctraining.github.io/main FOR CURRENT LESSONS.
Introduction to RNA-seq using high-performance computing (HPC)
| Audience | Computational skills required | Duration |
|---|---|---|
| Biologists | None | 2- or 3-day workshop (~13 - 19.5 hours of trainer-led time) |
Description
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.
Learning Objectives
- 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.
Lessons
Below are links to the lessons and suggested schedules:
- 2 day schedule
- 3 day schedule
Installation Requirements
All:
- FileZilla (make sure you get ‘FileZilla Client’)
- Integrative Genomics Viewer (IGV) (scroll down on the page for Download options). If you have trouble opening IGV after installing it, you may need to install Java.
Mac users:
- Plain text editor like Sublime text or similar
Windows users:
Dataset
- Day 1 - Introduction to Shell: Dataset
- Days 2 and 3 - RNA-seq analysis (coming soon)
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).