You’ve got your samples ready, your experiment lined up, and funding secured. Everything looks set, so what could possibly go wrong? Well, plenty, if you haven’t involved your bioinformatics team when designing your experiment. I get it—bioinformaticians usually get called in at the final step. But here’s the thing: we’re your secret weapon for avoiding costly mistakes, saving valuable samples, and making sure your data is actually meaningful.
Getting us involved early can avoid the dreaded “garbage in, garbage out” scenario, where we’re handed poorly designed datasets and asked to “find something” from unclear or unreliable data. Involving us early in the design process can be a total game-changer for your experimental outcomes.
We’ve seen all sorts of avoidable data horror stories. Like the time a client collected all their treated samples from female animals and their controls from males—making it impossible to tell whether the differences were due to treatment or biological sex. Or the client who placed all their negative controls in the same corner of every plate (spoiler: plate edges are tricky, and their entire dataset was skewed because of it).
One of the most common issues we encounter is improper control setup. For example, multiple time points collected for treatment but only one for control. That’s a problem. Or adding unnecessary controls, wasting precious resources. We also frequently run into underpowered experiments, where there aren’t enough replicates to produce meaningful results. And then there’s the classic “throw everything at the wall and see what sticks” approach. Testing five variables at once? Without enough controls or replicates? That’s not an experiment—it’s a fishing expedition.
Another frequent issue is technical batching—processing treatments, control samples, or time points differently, which confounds the results. For example, using separate labs or technicians for control and treated samples can introduce bias that’s hard to fix later. Then there’s the chaos of mixed-up metadata. By the time the data reaches us, we’re untangling mistakes that take weeks to troubleshoot and that could’ve been flagged with one early meeting. Or, my favorite, when a client’s sequencing facility ran single-end sequencing for treated samples and paired-end for controls, and then told our client that it wouldn’t matter! But guess what? It absolutely does.
So, how can these pitfalls be avoided? Simple. Bring your bioinformatics team into the conversation early. The clients who involve us from the beginning avoid the big headaches. We’ll ask the right questions to refine your approach: What’s your scientific goal? What samples are you working with? What technology do you plan to use? From there, we’ll help set up the proper controls, enough replicates, and a solid experimental design that fits your budget. A little upfront planning can save you weeks—or even months—of troubleshooting later. And more importantly, it can help you avoid wasting irreplaceable resources like patient samples.
To keep your experiment from derailing and becoming a bioinformatics horror story, follow these few basic guidelines:
- Test fewer variables: Focus on one or two key questions.
- Randomize your setup: This helps reduce bias.
- Replicates matter: Aim for at least three replicates in animal studies, and more for human studies.
- Choose the right tech: Not every experiment requires the latest shiny tool—pick what best fits your research question.
Why gamble with your results when you can get it right the first time? Trust us, it’s a conversation worth having.