Why should you consult a bioinformatician before commencing a genomics project
You are a researcher, scientist, or graduate student. Your curiosity and scientific inclinations led you to important research questions that require multidisciplinary approaches, bioinformatics being one of them. When is the best time to bring on board a bioinformatician?
Contrary to popular opinion, a bioinformatician does more than feed data into analysis tools to provide you with results. Utilizing a combination of biological, computational, and statistical skills, they optimize the likelihood of effectively addressing your research questions. Here’s why.
From the project’s conception to grant writing, it is standard practice to involve various experts to advise on various parts of the project, a bioinformatician being one of them. A bioinformatician can assist in determining the best experimental framework to tackle your research questions effectively. Moreover, they are critical in avoiding common pitfalls like inaccuracies in estimating sample sizes, experimental biases, and insufficient statistical power. They employ statistical techniques and power calculations to determine the necessary sample sizes for achieving statistically robust results. Additionally, they offer insights into sample randomization, blinding procedures, and control selection to minimize experimental bias and enhance the validity of the research.
There’s a line by a famous artist that says, ‘The good news is, you came a long way; the bad news is, you went the wrong way.’ Most scientists tend to involve bioinformaticians during the analysis steps, and that may be too late as it leaves bioinformaticians with the arduous task of working with the wrong datasets or, worse, insufficient, poor-quality, or wrongly sampled data.
What does ‘wrong datasets’ mean in this context? A scientist may have favoured one experimental approach over another to attain a desired outcome. However, a bioinformatician may have proposed a better, more robust approach during the study design phase.
I have had a personal encounter where I had to advise a research team that wanted to perform transcriptomic sequencing experiments but ended up ordering genomic library preparation kits, and they could not tell the difference. They were so furious that this ‘cutting-edge sequencing platform’ could not deliver the desired results! Despite sequencing successfully, the obtained data was unlikely to produce the desired outcome; the odds were akin to a snowball’s chance in hell. This mishap necessitated a complete revision of the research question, the acquisition of new library preparation kits, and the resequencing of samples. In more severe cases, it may even require obtaining new specimens altogether!
An incorrect experimental design means an even worse budget plan. You are likely to spend your so-often scarce monetary and time resources on the wrong gadgets, equipment, and experiments. A bioinformatician gets to advise you on your proposed budget to avoid such costly mistakes.
The tech adage “Garbage in, garbage out” perfectly applies to bioinformatics analysis. Experiment design dictates data generation approaches, and even a tiny misstep can result in irreparable chaos. To prevent this, consulting people with the right competencies is crucial. Waiting until data is generated and encountering issues is similar to seeking medical help when a condition is already terminal — it may be too late for effective intervention. There’s only so much they can do. To quote the great polymath, Ronald Fisher, “To consult the statistician after an experiment is finished is often merely to ask him to conduct a post-mortem examination. He can perhaps say what the experiment died of.”
Bioinformatics expertise is invaluable but often overlooked until the analysis stage for researchers, scientists, or graduate students embarking on multidisciplinary projects. Contrary to popular belief, bioinformaticians offer more than just data analysis; they are instrumental for most of the project’s life cycle. By involving them early, they get to advise on experimental design, help prevent pitfalls like sample size inaccuracies and experimental biases, and ensure statistical robustness, potentially preventing wasted resources and irreparable chaos. Consulting bioinformaticians from the outset is akin to proactive treatment, ensuring project success and efficient resource allocation.
At what point is it optimal to engage a bioinformatician in your project involving bioinformatics? If you are not a competent bioinformatician, the answer should be from the beginning.
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