yejg2017 / rna-seq-teaching-o2

This repository has teaching materials for a 2 and 3-day Introduction to RNA-sequencing data analysis workshop using the O2 Cluster

Introduction to RNA-seq using high-performance computing (HPC)

Audience	Computational Skills	Prerequisites	Duration
Biologists	Beginner/Intermediate	None	2-day workshop (~13 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

1. Understand the necessity for, and use of, the command line interface (bash) and HPC for analyzing high-throughput sequencing data.
2. Understand best practices for designing an RNA-seq experiment and analysis the resulting data.

Workshop Schedule

The schedule for using the materials in a trainer-led workshop can be found here

Contents

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

Dataset

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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).