From Laptop to HPC: Scaling Computational Biology Workflows

Modern bioinformatics quickly outgrows a personal laptop. This series walks step-by-step through the transition from local analyses to high-performance computing (HPC) clusters used in genomics and microbiome research.

Across six short posts, we cover the practical foundations of computational biology infrastructure — from running your first command on a cluster to building scalable, reproducible workflows.

Series Overview

• Day 1: Day 1 — Laptop vs HPC: Why Bioinformatics Needs Both (Mar 12, 2026)
• Day 2: Day 2 — Software Installation: sudo vs module load vs conda (Mar 13, 2026)
• Day 3: Day 3 — Running Jobs: Terminal, Bash Loops, and SLURM (Mar 16, 2026)
• Day 4: Day 4 — Scaling Analysis: For Loops vs SLURM Job Arrays (Mar 17, 2026)
• Day 5: Day 5 — Reproducible Pipelines: Snakemake and Nextflow (Mar 18, 2026)
• Day 6: Day 6 — Moving Your Data: scp, rsync, SFTP, and Globus Connect (Mar 19, 2026)

What You'll Learn

• Why laptops struggle with large genomics datasets
• How HPC clusters distribute compute resources
• Software installation on shared systems
• Running jobs using SLURM
• Parallelizing analyses across many samples
• Building reproducible pipelines with workflow managers

The goal is to make HPC less intimidating and help researchers move from small local analyses to scalable bioinformatics workflows.