With the advent of high-throughput ‘omics’ technologies, life scientists are starting to grapple with massive data sets. As Big Omics data continues to grow, biologists start encountering challenges with handling, processing and analyzing information that were once the domain of astronomers and high-energy physicists.
The Rise Of The DIY Computational Biologist
Bioinformatics is becoming more and more intertwined in the work of the biologist. Whether this is in the form of the analysis of their own data or to keep up with the new technologies, it’s an exciting time for researchers who can take advantage of Big Omics data at hand in order to improve.
One such trend is the rise of the DIY computational biologist, a biologist who learned to use available software to analyze their data themselves. While this person will not replace the expert bioinformatician, they will take on an ever-expanding role in the coming years.
The current state of Bioinformatics tools
Current bioinformatics tools consists of a myriad of free software packages and a jungle of websites that provide specific bioinformatic services. For anyone wanting to analyze omics data, wading through this bazaar of options requires expert knowledge of which software package to choose or what website to use.
Traditionally, bioinformaticians would cook up scripts and juggle files between the websites to analyze the data. For biologists who want to analyze their own data, they would then pass on these scripts. The problem is that most biologists would still require considerable time to learn running the scripts and let alone being able to modify them in case something needs to be changed.
After years of biologists being dependent on busy, understaffed IT teams for their data analysis needs, a shift is occurring. Tired of the inflexibility and slow turnaround, and also the willingness to understand their data better, more and more biologists are turning away from the traditional bioinformatics support to self-service analytics. Blending their intuition with self-service tools to analyze, they may better leverage the data and provide deeper insights.
The Need of new self-service bioinformatics platforms
Organizations that have relied heavily on in-house programming are realizing they need a better bioinformatics platform — one that doesn’t lead to a patchwork of scripts that is increasingly messy, unreliable and unsustainable. New self-service bioinformatics platforms are essential to organizations that seek to place Big Omics data at the core of their research efforts.
Not only does self-service bioinformatics free up bioinformaticians to focus on more strategic work than reporting, but it also enables biologists to access what data they want and when they need it. This democratization of omics data across an organization opens up new opportunities that simply wouldn’t be possible with traditional bioinformatics tools. The rise of self-service bioinformatics is changing the way research institutes look at hiring and perform their biological data analysis. As Big Omics data continues to grow and more bioinformatics is needed, self-serving computational biologists are set to exceed bioinformaticians in the future.
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