Let's separate our concerns!

Separation of Concerns - it’s one of the most important concepts in software engineering, and somehow it never made it into my bioinformatics courses 🤔 .

I learned this the hard way when trying to scale prototype analyses into maintainable, production-ready tools.

THE PROTOTYPE TRAP: Your initial analysis script works perfectly. It reads data, cleans it, runs analysis, generates plots, and saves results. All in one beautiful 500-line Python script.

Then stakeholders ask: “Can you run this on different data?” “Can we change the visualization?” “What if we use a different algorithm?”

Suddenly, your elegant prototype becomes a maintenance nightmare 😵‍💫 .

WHAT IS SEPARATION OF CONCERNS? Simply put: each part of your code should have one job and do it well.

Instead of one script that does everything, you separate:

➡️ Data ingestion (reading files, databases)

➡️ Data processing (cleaning, transformation, QC)

➡️ Analysis logic (algorithms, statistics)

➡️ Visualization (plotting, reporting)

➡️ Output handling (saving results)

WHY THIS MATTERS: Need to swap your RNA-seq aligner? Easy—you only touch the analysis module. Your data cleaning logic works for multiple projects. You can test each component independently.

NEXTFLOW: SEPARATION OF CONCERNS IN ACTION Many bioinformaticians already use this principle! NextFlow/Snakemake/CWL/etc workflows are a great example:

➡️ Each process handles one specific task

➡️ Swap your aligner? Only modify that process—the rest stays unchanged

THE EDUCATION GAP: Most bioinformatics courses focus on algorithms and statistics (crucial!) but don’t explicitly teach these software engineering principles.

We learn sequence alignment but not why organizing code into modular, single-purpose components makes everything more maintainable.

FOR PRACTITIONERS: If your analysis scripts are becoming unmaintainable monsters, it might be time to refactor with separation of concerns in mind.

What software engineering principles do you wish you’d learned earlier in your bioinformatics career?