Cutting-Edge Bioinformatics in a High-Performance Computing Environment

HCEMM - Scientific Computing ACF
Instructors: Joao Sequeira, Maria Kavoosi, Istvan Szepesi-Nagy

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Course Introduction

This course introduces advanced bioinformatics techniques within high-performance computing (HPC) environments. Participants will learn to efficiently manage computational workflows, leverage HPC resources, and apply bioinformatics tools to analyze biological datasets. By the end of the course, students will gain hands-on experience in combining computational power with cutting-edge bioinformatics approaches.

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General bioinformatics pipeline

Source: HBCTraining - Introduction to RNA-Seq using high-performance computing

Content

1. Data download (fasterq-dump)
2. Quality Control (fastqc)
3. Trimming (trimmomatic)
4. Alignment (STAR)
5. Counting (featureCounts)
6. Pseudo-alignment (Salmon)
7. Differential expression analysis (limma in R)
8. SLURM (Task submissions on HPC systems)

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Conda Enviroment Setup

Enviroment setup

Load Miniconda:

module load miniconda3
OR
ml miniconda3

Check:

conda --version
    conda 25.11.1

Create our environment:

conda create -f bioinfo-hpc.yml

Activate environment:

conda activate bioinfo-hpc

Problems

If conda environment is not activated, try:

/opt/miniconda3/bin/conda init bash
source ~/.bashrc

then try activating the environment again!

References

• Course content is based on HBCTraining website (Harvard Chan Bioinformatics Core) - doi.org/10.5281/zenodo.5833880
• Differential expression analysis pipelines are influenced by Marta Perez Alcantra's content on bulk RNA-seq analysis.
• Publicly available data is accessed through the Seqeunce Read Archive (SRA) based on Himes et al., 2014. - (SRP033351)