In bioinformatics, there’s never a calm day. Pipelines are overloaded, requests are endless, and priorities shift overnight. Under this kind of pressure, abstraction (or modular, reusable design) can feel like a luxury.

The Problem: Death by a Thousand Small Requests

Every bioinformatics team has been there.

You deliver a result and, the next week, a collaborator comes back with:

• “Can you run it again, but with STAR instead of Bowtie?”
• “Can we test this with different filters?”
• “Can we apply this workflow to our new dataset?”

Each of these seems like a small tweak or request. But without thoughtful abstraction, these requests balloon into logistical nightmares. You’re copying scripts, hand-editing configs, and hoping you remember exactly what you did last time. This over time starts to manifest itself as slow turnaround times, inconsistent outputs, and stressed-out teams trapped in a cycle of rework.

The real danger isn’t a single ad hoc workflow. It’s the culture that grows around it. When every new project starts from scratch, two things happen:

1. Knowledge Fragmentation: Methods live in individual people’s heads or random folders, making onboarding and handoffs painful.
2. Scaling Collapse: As demand grows, the team can’t keep up. What worked for three requests a month fails miserably when there are thirty.

Abstraction as a Force Multiplier

Abstraction flips this dynamic. Instead of hardcoding solutions, you build modular, reusable components.

With abstraction, those same frustrating requests transform:

• Without abstraction: A request for a different aligner means editing scripts, updating configs, and manually re-running everything.
  
  
• With abstraction: It’s a dropdown menu. Swap Bowtie for STAR and rerun with a single click.

At Via Scientific, we’ve baked this philosophy into how we design every workflow and integration:

• Reusable, swappable pieces: Every pipeline is composed of modules that can be rearranged without rewriting code.
• Future-ready integrations: We design with the assumption that today’s tools will be obsolete tomorrow. Adding a new one shouldn’t require a full rebuild.

The beauty of this approach is that small, smart steps today compound into massive wins tomorrow.

Case Study: The Power of a Single Abstraction Layer

I managed a core facility managing RNA-seq analysis for dozens of labs and initially each lab had slightly different needs:

• Lab A preferred Bowtie.
• Lab B needed STAR with custom filtering.
• Lab C wanted experimental pipelines for a new dataset.

So my team initially built one-off workflows to satisfy each request. Within six months, maintenance consumed about half of their time. Adding a new dataset or method started feeling like a project in its own right, instead of a preparation step.

After introducing a modular abstraction layer:

• Switching aligners dropped from days of work to minutes.
• Adding a new dataset became as simple as selecting a source.
• The team’s throughput doubled without adding headcount.

Most importantly, morale improved. Analysts could focus on meaningful science instead of fighting with brittle scripts.

Why This Matters for the Future of Bioinformatics

The volume and complexity of bioinformatics data are growing steadily. New sequencing technologies, experimental designs, and analytical methods appear every year. And at the same time, teams are still expected to support well-established and legacy methods and techniques. 

Without abstraction, teams will be buried under their own technical debt. With abstraction, they can adapt quickly.

Practical First Steps

If you’re convinced abstraction matters but unsure where to start, here’s a simple playbook:

1. Identify the Repeat Offenders:
   Look for workflows you’ve rebuilt more than twice.
   These are prime candidates for modularization.
   
   
2. Standardize Naming and Metadata:
   Inconsistent inputs create hidden complexity.
   Fixing this is often the highest-leverage change you can make.
   
   
3. Separate Logic from Configuration:
   Hardcoded parameters are workflow killers.
   Move settings into config files or parameter stores.
   
   
4. Build for the Tool After This One:
   When adding a new method or dataset, ask:
   “How will we swap this out next year?”
   
   
5. Document as You Go:
   Even basic docs save hours of future pain, especially during handoffs or onboarding.

In bioinformatics, small, smart steps don’t just add up – they compound. One day, you’ll look back and realize you’ve transformed an unscalable mess into a smooth, responsive system.

‍