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

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 using Salmon, the workshop covers best practice guidelines for RNA-seq experimental design and data organization/management.

Note for Trainers: Please note that the schedule linked below assumes that learners will spend between 3-4 hours on reading through, and completing exercises from selected lessons between classes. The online component of the workshop focuses on more exercises and discussion/Q & A.

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 analyzing the resulting data.

Lessons

• Workshop schedule (trainer-led learning)
• Self-learning

Installation Requirements

All:

• FileZilla Client (make sure you get ‘FileZilla Client')

Mac users:

• Plain text editor like Sublime text or similar

Windows users:

• GitBash
• Plain text editor like Notepad++ or similar

Citation

To cite material from this course in your publications, please use:

Mary E. Piper, Meeta Mistry, Jihe Liu, William J. Gammerdinger, & Radhika S. Khetani. (2022, January 10). hbctraining/Intro-to-rnaseq-hpc-salmon-flipped: Introduction to RNA-seq using Salmon Lessons from HCBC (first release). Zenodo. https://doi.org/10.5281/zenodo.5833880. RRID:SCR_025373.

A lot of time and effort went into the preparation of these materials. Citations help us understand the needs of the community, gain recognition for our work, and attract further funding to support our teaching activities. Thank you for citing this material if it helped you in your data analysis.

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).