Frequently asked questions

Setup and computers

I have Windows. Can I take part? Yes, through WSL2 (a real Linux inside Windows) or GitHub Codespaces. Three tools of the pipeline (bwa, samtools, bcftools) have no Windows builds, so native Windows only works for the report and notebook lessons. See setup.

My laptop is old or locked down. Use a Codespace (setup, step 7). The free quota (about 60 hours a month on a 2-core machine) is enough for the course if you stop codespaces when you are done.

How much disk space do I need? About 10 GB free: roughly 4 GB of software environments for the example project, 0.5 GB of data and results, and Pixi’s package cache. Take-down frees it afterwards.

I have never used a terminal. Is that a problem? No. The lessons give every command, but they go faster if cd, ls and pipes are familiar. Spend an hour on Bash 101 before lesson 02; it ends with longer shell tutorials, from Data Carpentry and Software Carpentry, if you want more.

Do I need administrator rights? Not for Pixi or anything it installs. Only WSL2 (Windows) and Docker need them, once.

Tools

Why Pixi and not Conda? Pixi uses the same packages (conda-forge, bioconda), but keeps the environment in the project, always writes a lock file for every platform, and runs tasks. That makes “rebuild exactly what I used” one command. Conda is still covered: 05b Pixi and Conda compared.

Why Python and not R? To keep one language across Quarto, Jupyter and the helper scripts. Everything in the course works the same with R: Quarto runs R, and Pixi installs R packages from conda-forge.

Why both Snakemake and Nextflow? They are the two main workflow managers in bioinformatics; you will meet both. Snakemake is taught in full; Nextflow gets an introduction and a home tutorial on the same pipeline, so you can compare them, and their results are identical.

Do I need to understand the genomics? No. The variant-calling steps come from the Data Carpentry lesson and are treated as a given pipeline. The course is about making any analysis reproducible.

The course material

Can I use this material in my own teaching? Yes: text and slides are CC BY 4.0 and code is MIT. Credit the workshop and the sources it adapts (Sources and credits), and say what you changed. Teach these lessons shows how to run it as your own course or workshop.

I found a mistake. Please open an issue or a pull request on the GitHub repository.

How are the tutorials tested? Every shell command in a tutorial is marked in the source and run by pixi run test-tutorials in a fresh folder, lesson after lesson, as a learner would. GitHub Actions runs them on Linux and macOS after every change and every Monday, including the Git and Docker steps (Linux); the latest runs show whether they pass today. Steps that need your own accounts (GitHub web pages, Zenodo) or a cluster are not tested automatically.