As prices for diagnostic and sequencing applied sciences have plummeted in recent times, researchers have collected an unprecedented quantity of information round illness and biology. Sadly, scientists hoping to go from knowledge to new cures typically require assist from somebody with expertise in software program engineering.
Now, Watershed Bio helps scientists and bioinformaticians run experiments and get insights with a platform that lets customers analyze complicated datasets no matter their computational expertise. The cloud-based platform supplies workflow templates and a customizable interface to assist customers discover and share knowledge of every kind, together with whole-genome sequencing, transcriptomics, proteomics, metabolomics, high-content imaging, protein folding, and extra.
“Scientists wish to study in regards to the software program and knowledge science elements of the sector, however they don’t wish to develop into software program engineers writing code simply to know their knowledge,” co-founder and CEO Jonathan Wang ’13, SM ’15 says. “With Watershed, they don’t should.”
Watershed is being utilized by giant and small analysis groups throughout business and academia to drive discovery and decision-making. When new superior analytic strategies are described in scientific journals, they are often added to Watershed’s platform instantly as templates, making cutting-edge instruments extra accessible and collaborative for researchers of all backgrounds.
“The information in biology is rising exponentially, and the sequencing applied sciences producing this knowledge are solely getting higher and cheaper,” Wang says. “Coming from MIT, this difficulty was proper in my wheelhouse: It’s a troublesome technical downside. It’s additionally a significant downside as a result of these persons are working to deal with ailments. They know all this knowledge has worth, however they battle to make use of it. We wish to assist them unlock extra insights quicker.”
No code discovery
Wang anticipated to main in biology at MIT, however he rapidly bought excited by the probabilities of constructing options that scaled to hundreds of thousands of individuals with laptop science. He ended up incomes each his bachelor’s and grasp’s levels from the Division of Electrical Engineering and Pc Science (EECS). Wang additionally interned at a biology lab at MIT, the place he was stunned how gradual and labor-intensive experiments had been.
“I noticed the distinction between biology and laptop science, the place you had these dynamic environments [in computer science] that allow you to get suggestions instantly,” Wang says. “At the same time as a single particular person writing code, you will have a lot at your fingertips to play with.”
Whereas engaged on machine studying and high-performance computing at MIT, Wang additionally co-founded a excessive frequency buying and selling agency with some classmates. His staff employed researchers with PhD backgrounds in areas like math and physics to develop new buying and selling methods, however they rapidly noticed a bottleneck of their course of.
“Issues had been transferring slowly as a result of the researchers had been used to constructing prototypes,” Wang says. “These had been small approximations of fashions they may run domestically on their machines. To place these approaches into manufacturing, they wanted engineers to make them work in a high-throughput method on a computing cluster. However the engineers didn’t perceive the character of the analysis, so there was a number of backwards and forwards. It meant concepts you thought may have been applied in a day took weeks.”
To resolve the issue, Wang’s staff developed a software program layer that made constructing production-ready fashions as simple as constructing prototypes on a laptop computer. Then, just a few years after graduating MIT, Wang observed applied sciences like DNA sequencing had develop into low cost and ubiquitous.
“The bottleneck wasn’t sequencing anymore, so individuals mentioned, ‘Let’s sequence all the things,’” Wang recollects. “The limiting issue grew to become computation. Folks didn’t know what to do with all the information being generated. Biologists had been ready for knowledge scientists and bioinformaticians to assist them, however these individuals didn’t at all times perceive the biology at a deep sufficient degree.”
The state of affairs seemed acquainted to Wang.
“It was precisely like what we noticed in finance, the place researchers had been attempting to work with engineers, however the engineers by no means totally understood, and also you had all this inefficiency with individuals ready on the engineers,” Wang says. “In the meantime, I realized the biologists are hungry to run these experiments, however there’s such a giant hole they felt they needed to develop into a software program engineer or simply deal with the science.”
Wang formally based Watershed in 2019 with doctor Mark Kalinich ’13, a former classmate at MIT who’s now not concerned in day-to-day operations of the corporate.
Wang has since heard from biotech and pharmaceutical executives in regards to the rising complexity of biology analysis. Unlocking new insights more and more entails analyzing knowledge from whole genomes, inhabitants research, RNA sequencing, mass spectrometry, and extra. Creating customized remedies or choosing affected person populations for a medical research also can require enormous datasets, and there are new methods to investigate knowledge being revealed in scientific journals on a regular basis.
In the present day, firms can run large-scale analyses on Watershed with out having to arrange their very own servers or cloud computing accounts. Researchers can use ready-made templates that work with all the commonest knowledge varieties to speed up their work. In style AI-based instruments like AlphaFold and Geneformer are additionally out there, and Watershed’s platform makes sharing workflows and digging deeper into outcomes simple.
“The platform hits a candy spot of usability and customizability for individuals of all backgrounds,” Wang says. “No science is ever actually the identical. I keep away from the phrase product as a result of that means you deploy one thing and then you definitely simply run it at scale perpetually. Analysis isn’t like that. Analysis is about developing with an concept, testing it, and utilizing the result to give you one other concept. The quicker you may design, implement, and execute experiments, the quicker you may transfer on to the following one.”
Accelerating biology
Wang believes Watershed helps biologists sustain with the newest advances in biology and accelerating scientific discovery within the course of.
“If you happen to will help scientists unlock insights not somewhat bit quicker, however 10 or 20 instances quicker, it may actually make a distinction,” Wang says.
Watershed is being utilized by researchers in academia and in firms of all sizes. Executives at biotech and pharmaceutical firms additionally use Watershed to make selections about new experiments and drug candidates.
“We’ve seen success in all these areas, and the frequent thread is individuals understanding analysis however not being an knowledgeable in laptop science or software program engineering,” Wang says. “It’s thrilling to see this business develop. For me, it’s nice being from MIT and now to be again in Kendall Sq. the place Watershed is predicated. That is the place a lot of the cutting-edge progress is going on. We’re attempting to do our half to allow the way forward for biology.”
