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Bulk RNA-seq Data Analysis using High-Performance Computing (bulk RNA-seq Part I – FASTQ to counts)

Learning Objectives



Mac users:

Windows users:


Instructions for Harvard researchers with access to HMS-RC’s O2 cluster

To run through the code in the lessons below, you will need to be logged into O2 and working on a compute node (i.e. your command prompt should have the word compute in it).

  1. Log in using ssh and enter your password.
  2. Once you are on the login node, use srun --pty -p interactive -t 0-2:30 --mem 1G /bin/bash to get on a compute node or as specified in the lesson.
  3. Proceed only once your command prompt has the word compute in it.
  4. If you log out between lessons (using the exit command twice), please follow points 1. and 2. above to log back in and get on a compute node when you restart with the self learning.


Part 1

  1. Introduction to RNA-seq
  2. Shell basics review
  3. Working in an HPC environment - Review
  4. Best Practices in Research Data Management (RDM)
  5. Project Organization (using Data Management best practices)

Part II

  1. Quality Control of Sequence Data: Running FASTQC
  2. Experimental design considerations
  3. Quality Control of Sequence Data: Running FASTQC on multiple samples
  4. Quality Control of Sequence Data: Evaluating FASTQC reports

Part III

  1. Sequence Alignment Theory
  2. Quantifying expression using alignment-free methods (Salmon on multiple samples)

Part IV

  1. QC with Alignment Data
  2. Documenting Steps in the Workflow with MultiQC
  3. Troubleshooting RNA-seq Data Analysis

Part V

  1. Automating the RNA-seq workflow

Answer Keys

Building on this workshop


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.