Advaita Bioinformatics: Using Small Observations to Drive a Big Data Revolution

How Small Observations are Driving a Big Data Revolution in Life Science

BY ALISHA BROWN, DIRECTOR OF MARKETING AND COMMUNICATIONS, MICHBIO SOURCE MATERIALS AND EDITING PROVIDED BY ADVAITA BIOINFORMATICS

Advancements in experimental tools and laboratory equipment have shifted the challenge in biology from figuring out how to perform an experiment to how to understand the results.

Even 25 years ago, the study of biology looked a lot different than it does now. In the late 90’s and it was not uncommon for a life scientist to spend years studying a single gene across a few conditions. Today, technologies such as microarrays and RNASeq allow the scientists to measure the expression of all genes in the human genome – over 30,000 – in a single experiment.

Simultaneous to that change, the industry infrastructure has evolved with highly sophisticated labs appearing at major institutions and companies around the world, staffed by trained biologists and research assistants, all capable of completing high-throughput studies and amassing enormous amounts of data. So much data, in fact, that it is impossible to understand the results without the aid of computers. The problem, most biologists are not also expert computer programmers, so, undoubtedly, major discoveries sit, buried in data that is too difficult to read.

That is where Sorin Draghici, PhD and his team at Advaita Bioinformatics come in. “I spent eight years as the Director of the Bioinformatics Core at Karmanos Cancer Institute,” says Dr. Draghici. “In that role we were supposed to help researchers analyze data they acquired from their studies, but that was a challenge. You see, a highly qualified life scientists usually lacked the programming ability to analyze their data in a significant way.”

He went on, “Even if they were the most qualified person in the world to understand the experiment, they would have to turn that data over to a computer scientist who didn’t have a background in the domain of the experiment and didn’t know what to look for. The person who had the knowledge didn’t have the tools and the person who had the tools, didn’t have the knowledge.”

He continued, “So the data ended up going through a Procrustean bed consisting of a set of standard steps that rarely fitted the experiment being analyzed. What we really needed was an interactive, easy-to-use tool that allowed the scientist to ask questions of the data without having to know how to code.”

At the same time, Dr. Draghici was teaching computer science at Wayne State University and took up this unique challenge in his research projects. Draghici developed new methods for data analysis that were then made available to the entire research community from his laboratory website. For the next ten years, the software was used, maintained, and changed by the computer science and lab communities, until it became so complex that it was unsustainable as a university project. Thus, Advaita Bioinformatics was born.

With a leadership team including PhD-level geneticists, biomathematicians, and software engineers, Advaita took the basic ideas from the Wayne State projects and developed their iPathwayGuide, iVariantGuide, and iBioGuide software platforms that give researchers the ability to quickly identify significantly impacted pathways, understand mechanisms of disease and drug actions, identify important genetic variants, etc.

“After demonstrating the power of systems like these,” continued Draghici, “we got subscription orders from many top universities including the University of Michigan, Stanford University, University of Washington, Columbia University, and others. We also signed distribution agreements in New Zealand, Australia, South Africa, with Japan, Korea, and Taiwan soon to be finalized. That type of adoption means that more experiments will be truly understood, more relationships will be uncovered, and more major discoveries will be made.”

After demonstrating its capabilities in the research and academic markets, Advaita is now prepared to deploy their software and technology to shorten the drug discovery pipeline by eliminating candidates that are destined to fail and identifying drugs suitable for repurposing, shorten the duration of clinical trials by identifying patients who are more likely to respond, and enable a true personalized medicine approach by identifying the best drug for a given patient.

With advancements like these that merge the expertise of scientists with the power of computerized data management, the biosciences industry is in the midst of a big revolution to understand even the smallest of measurements.

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