Bioinformatic Platforms: Bridging the Gap Between Biologists and Bioinformaticians
Written by Axel Martinelli
Last updated on December 13, 2024
Published on August 26, 2021
⏱ 5 min read
Introduction
The analysis of omics data involves the collaboration of biologists and bioinformaticians. They need each other to generate the data and analyse it.
Biologists have a deep understanding of the biological nature of their data and the questions that they are trying to address. Meanwhile, bioinformaticians possess the required skills to perform the analysis of such data. They also understand the intricacies of complex statistical considerations involved in such analysis. These include aspects such as data batch correction and normalisation that can often escape biologists. They also, crucially, have the programming skills needed to implement and speed up such analysis.
The collaborative challenge
Unfortunately, communication between the two groups can be difficult due to their different backgrounds and approaches to data interpretation. Biologists are not always able to grasp what bioinformatic analysis can deliver and the time-frame required. They also tend to take a more evidence-driven approach when interpreting results.
Meanwhile, bioinformaticians have a more data-driven mentality and risk missing the biological context of the data and simply follow standard analysis protocols that may not be appropriate.
These challenges are further compounded when bioinformatic analysis mistakes and missteps in omics experimental design are not properly addressed, leading to issues in both data interpretation and experimental setup.
Biologists and bioinformaticians can actually master both skill sets. However, such renaissance-type figures are not common, especially in an era of deep specialisation in one area.
The current modus operandi
A more common scenario is that of biologists generating data based on meticulously designed experiments and then going to bioinformaticians for the analysis.
The former know the questions they want to answer and the biological properties (and issues) of the data. However, they lacks the required skills to perform the bioinformatic analysis.
The latter have the required bioinformatic skills, but have little knowledge about the biological nature of the data. They will thus approach the data with standard pipelines and parameters. The biologists will then interpret the results and request a further round of analysis. Not because they do not trust them, but because they may notice patterns that need further exploration. The process can continue through several rounds of analysis and interpretations until the biologists are satisfied with the output.
This modus operandi is the product of the different backgrounds and approaches adopted by biologists and bioinformaticians towards data analysis and interpretation. It can be time-consuming and sometimes even frustrating.
Platforms as a solution
To address this problem, several bioinformatic platforms have been developed. These platforms make the analysis more accessible, with the implication that biologists can analyze the data by themselves with no external support.
Several platforms encourage such an interpretation, which is rather counterproductive. Some knowledge of the statistical methods behind such platforms is required and often experience dealing with such datasets plays an important role too.
Biologists often spend most of their time in the lab and do not devote their time exclusively devising and generating omics data. In fact, omics data analysis often represents only one of their numerous tasks.
It is thus challenging for them to develop the level of expertise that bioinformaticians possess. As such, input from experienced bioinformaticians is often still required to optimise and finalise the analysis of omics datasets.
Platforms should thus not only provide easier and faster access to analysis methods. They should also promote communication and collaboration between biologists and bioinformaticians through an interactive interface, rather than static reports.
Our philosphy
This is why at Bigomics we do not view platforms simply as a tool to empower biologists. Nor do we believe their purpose is just to free up bioinformaticians for more creative tasks. Instead of this dichotomy, we believe that platforms should also be a communication and collaboration tool between the two groups.
They should allow the sharing of knowledge and views more intuitively as the omics analysis is taking place.
Bioinformaticians can produce an output that biologists can view in real-time. The latter gain insights from it and provide immediate feedback to the bioinformaticians to refine the analysis (again in real-time), thus improving communication and analysis efficiency.
Bioinformaticians do not need to produce several static reports and biologists do not need to go through them each time.
This philosophy is reflected in the nature of our platform, Omics Playground, which allows academic, biotech and pharma teams to drive collaboration and insight by interacting with and altering outputs as they perform their RNA-Seq, proteomics or metabolomics data analysis.
About the Author
Axel Martinelli’s academic background is in molecular biology and parasitology. He earned a Ph.D. on the genetics of strain-specific immunity against malaria infections and a master’s degree in bioinformatics with specialization in the analysis of omics data. During his postdoctoral career, he worked on genomics and transcriptomics studies and is currently the head of biology at Bigomics Analytics.