A real analysis is rarely one command. It is a chain of steps, from quality control to alignment to counting or variant calling, run on many samples. Running them by hand is slow and easy to get wrong, and it makes your results hard to repeat. Reproducibility means that you, or someone else, can run the same analysis again and get the same answer.
You'll start with why workflow managers matter: they record each step, track what depends on what, and re-run only what has changed. Then you'll meet Snakemake, a workflow system built on Python, and write your first Snakefile made of simple rules.
Finally, you'll see how to make a pipeline easier to share by pairing it with software containers, so it runs the same way on another computer. This brings together the command line, scripting, and analysis skills from the whole course. Once you've finished, move on to the Projects to put them into practice.
Why workflow managers help you get better scientific reproducibility, from getting your data to handling the outputs.
An introduction to a tutorial series on bioinformatics engineering and data analysis using Snakemake.
A brief introduction to Snakemake, for people who already know some Python and Bash.
A quick demonstration of using Snakemake with Singularity so your pipeline can be reproduced by others and by yourself.