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.
Different approaches, different mentality
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. 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 a biologist generating data based on meticulously designed experiments and then going to a bioinformatician for the analysis. The former knows the questions he wants to answer and the biological properties (and issues) of the data. However, he lacks the required skills to perform the bioinformatic analysis. The latter has the required bioinformatic skills, but has little knowledge about the biological nature of the data. He will thus approach the data with standard pipeline and parameters. The biologist will then interpret the results and request a further round of analysis. Not because he does not trust them, but because he may notice patterns that need further exploration. The process can continue through several rounds of analysis and interpretations, until the biologist is 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 to “empower” biologists. 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 laboratory 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.
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 and as the 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 platforms which allow users to interact with and alter outputs as they perform the analysis.
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