The bioinformatics challenge
For a bio-researcher, the path from exploration to discovery and onto practical applications continues to be exciting and arduous. While modern instruments are often capable of delivering high throughput and high content data, the associated complexity and workflow are profound. The contemporary researcher is habitually deluged with data that are intricate, diverse, massive and complex. The analyses of these datasets require highly specialized levels of computational and statistical expertise, skills that an overwhelming majority of life science researchers do not possess.
Existing bioinformatics solutions are convoluted and often inadequate
The complexity of modern instruments is so vast that a life science researcher has to seek esoteric software to merely compute an experimental output. As an example, a researcher may have to use open source tools with each tool supposedly optimized for a discrete subset of the experimental workflow. The open source piecemealed approach is fraught with challenges including likelihood that these tools could be outdated, untried, and may not have the validity required for many applications. Another option for the life science researcher is to buy dedicated third party software tools. While these tools have a commercial viewpoint, they are often constrained to specific niches, and have limited utility. As such a researcher is usually seeking an assortment of software tools for just carrying out routine applications. To make matters more problematic, the utilization of many bioinformatics tools is not straightforward at all. Rather, most of these software tools require a fair bit of understanding, demand extensive user inputs that include defining several specifications, selections, settings, and parameters along various steps of data analysis. Researchers often spend a lot of time to get familiar with this aspect of a software, only to realize that it was not optimal or relevant to their applications.
Another approach is to transfer or provide all data to a statistician or outsource it for analytics. While this approach absolves the researcher from playing the role of a statistician, it adds many layers of complexities. For instance, the statistician may be deploying a-priori tools to parse, sort and classify data, and this action may not be ideal for the data set in question. To place the dataset in a biological context, the statistician or bioinformatician would need to fully understand the intricate details of the experiment or a hypothesis, and be able to grasp the ultimate objective of a study or the project. In a nutshell, the statistician would need to understand esoteric biology, and / or the biologist would need to cognize cryptic computing – these happenings are highly improbable, extremely time consuming, and could be very expensive. As such, most bioinformatics approaches are plagued with roadblocks – research comes to a crawl, becomes inefficient, erratic, and often borders on becoming irrelevant.