Bridging the Analysis Gap
The Symbiotic Relationship between the Wet Lab and Bioinformatics.
I’ll admit it. I am from the generation that asked parents where babies come from or, if you were bolder, how they’re made. Things have changed, with the newer generations seeming to be born with their eyes glued onto screens, fingers touchpads, and feet on the pedals. Anyway, that aside, this is a short piece for those up-and-coming bioinformaticians and analysts who ‘wonder’ how the data they use ‘is made’ and why a synergetic partnership between bioinformaticians and wet lab scientists should be like a beautiful dance of Tango.
Wet Lab and Bioinformatics: A Powerful Partnership
With the ‘cool’ hat and attention seeming to land on the heads of bioinformaticians and computational biologists more recently, let us not overlook the crucial role the classical wet lab plays in a successful biological research project. Yes, the pipettes, test tubes, and microscopes are not yet obsolete.
The truth is that these two approaches are partners in a high-stakes scientific dance, each respecting and valuing the unique contributions of the other.
The wet lab, where the foundation is laid
Wet lab experiments are the workhorses of biology, where the rubber meets the road and the heavy lifting is done. Wet lab scientists have the exciting task of creating the raw data that bioinformaticians then get to play with. This data is the lifeblood of bioinformatics, fueling their computational engines with DNA/RNA sequences, proteins, metabolites, and more.
Bioinformatics unravels the biological maze.
Biological data can often be complex and messy. Think of it as produce directly sourced from the farm. Someone’s got to work on it before it’s freshly served on your plate. The same applies to biological data.
Bioinformatics tools help us organize, analyze, and interpret this data. They can identify and visualize biological patterns, predict functions, and generate new hypotheses. Imagine a scientist sifting through mountains of data by hand — cumbersome and time-consuming. Bioinformaticians harness software tools that automate this process, allowing investigators and scientists to focus on the big picture.
The feedback loop
Therefore, it is correct to say that bioinformatics analysis does not exist in a vacuum. Its findings often point researchers back to the wet lab for further investigation, sparking new questions and avenues of exploration. For example, bioinformatics might identify a gene of interest. The wet lab scientist would then design experiments to test and validate this finding to understand the underlying function of the gene better. This dynamic back-and-forth loop between wet lab scientists and bioinformaticians is not just a cornerstone but a catalyst for the progress of biological science.
The future is collaborative.
In the corridors of science, it’s not uncommon to notice the occasional cold shoulder between bioinformaticians and wet lab scientists. As a graduate student, I remember having to work mornings in the wet lab and making sense of the data I had generated later in the day. This hybrid role was a personal choice, stemming from my strong wet lab background during my undergraduate studies. Unlike many bioinformaticians who prefer to steer clear of wet lab tasks, I found fulfilment in bridging wet and dry lab activities — a sense of completeness in being involved in the entire research life cycle, from data collection to analysis and dissemination.
However, it came at a cost. Striking a balance often left me feeling drained and burned out. Looking back, collaborating with a passionate wet lab scientist could have been a more strategic approach. By focusing on bioinformatics work while my collaborator handled wet lab tasks, or vice versa, we could have synergized our efforts, maximizing our contributions to the project like interlocking cogs in a well-oiled machine.
The future of biology belongs not to wet lab scientists or bioinformaticians alone but to the seamless integration of their skill sets. As new technologies emerge, the need for this collaboration will only increase. In essence, the relationship is highly symbiotic. One generates the data; the other analyzes it, shaping the future of biological research.
So, who deserves the bigger share of the biological research pie? The pie is enough for all; grab your forks and dig in!
