At present biological data is stored and formatted in a highly individualised manner. The discrete nature of data storage between biological fields, subfields, and even within work groups presents a significant barrier to making meaningful connections between a personal dataset and broader biological context. Revealing these connections involves complex in-silico analyses that are inaccessible to the typical wet lab biologist.
We are developing a lab notebook based on graph database technology and cutting edge bioinformatics solutions. We aim to offer users the broadest possible perspective on their data coupled with a level of computational analysis usually unattainable to most biologists.
Computational methods have never been more prevalent and powerful in biological sciences than today. Anecdotally, the typical wet lab biologist in both academia and industry who has limited knowledge and skills in bioinformatics and scattered, inconsistent data storage. Biotechnology solutions have never been more societally and economically valuable than they are today. The biotechnology market is experiencing unprecedented growth, expected to reach USD$2.44 trillion by 2028 (GVR, 2021).