Even with my relatively limited experience in the wet lab, it became clear to me very early on that every experimental series really should start with some time in front of a computer. More specifically, with a bit of thoughtful bioinformatic analysis.
It might sound obvious to some, but it’s a step that’s still often underestimated: starting with a solid bioinformatic analysis before heading into the wet lab. Doing this can save a significant amount of time, money, and effort by helping refine experimental design from the start. It’s not just about cutting costs; it’s about clarity. Bioinformatics can reveal patterns, connections, or even entire directions that aren’t immediately obvious otherwise. In many cases, the most valuable insights only emerge when you look at the data first, not after something goes wrong at the bench.
What I find especially important to point out is that this approach isn’t reserved for big institutes with massive budgets. Even small labs can run surprisingly sophisticated analyses without spending much. From someone who’s always had an interest in computers and follows hardware trends closely, I can confidently say that the specs needed for most routine bioinformatic tasks are well within reach today. You don’t need a server farm just a reasonable amount of RAM, a few decent CPU cores, and a solid SSD. Sure, less powerful hardware might mean longer computation times, but that’s rarely a real limitation. Most analyses can run overnight, or even over a weekend, while we sleep or work on other things. In many cases, you'd probably hit the limits of your creativity or data quality before you run into serious hardware bottlenecks.
Free and open-source tools like Galaxy, BLAST, R, or even just a handful of Python or Bash scripts can take you surprisingly far. The internet is full of tutorials, forums, and ready-to-use pipelines that make it easier than ever to get started. You don’t need a formal background in programming, just a bit of curiosity and the willingness to use those quiet moments in the lab, like during washes or centrifuge spins, to explore and learn.
Another reason I value this approach is the large amount of biological data that’s already out there, freely accessible. Especially if you're working with a model organism, you can find just about everything: annotated genomes, gene expression data, protein interactions and so on. Even when working on non-model organisms, there's still a surprising amount you can do. Take Pomacea canaliculata, for example, a species I’ve worked with. It’s not a classic model, but there is a sequenced genome, and hundreds of transcriptomic datasets are available. That was more than enough for me to explore gene functions, run alignments, look at expression profiles, and start sketching the outline of an experiment long before prepping any reagents.
Of course, the quality of data for non-models varies a lot. Sometimes you only get partial annotations or a draft genome. But even then, you can usually find enough to evaluate whether your idea is worth pursuing, or which version of your idea makes more sense just by poking around and fiddling with the data until something useful starts to emerge.
For me, bioinformatics isn’t just a tool, it’s the part of the process where I get to explore, play with hypotheses, and let the data challenge what I think I know. It doesn’t replace the wet lab at all but it strengthens it.
Boot Linux, fire up your terminal, and dive deep into those datasets!