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 usually 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 abilities. The cloud-based platform gives 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 be taught in regards to the software program and knowledge science elements of the sphere, however they don’t wish to grow to be software program engineers writing code simply to grasp 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 subject 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 wrestle 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 obtained 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 individual writing code, you may 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 group 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 might run regionally on their machines. To place these approaches into manufacturing, they wanted engineers to make them work in a high-throughput approach on a computing cluster. However the engineers didn’t perceive the character of the analysis, so there was a variety of forwards and backwards. It meant concepts you thought might have been carried out in a day took weeks.”
To unravel the issue, Wang’s group developed a software program layer that made constructing production-ready fashions as simple as constructing prototypes on a laptop computer. Then, a couple of years after graduating MIT, Wang observed applied sciences like DNA sequencing had grow to be low cost and ubiquitous.
“The bottleneck wasn’t sequencing anymore, so individuals stated, ‘Let’s sequence all the things,’” Wang remembers. “The limiting issue turned 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 all the time perceive the biology at a deep sufficient degree.”
The state of affairs appeared acquainted to Wang.
“It was precisely like what we noticed in finance, the place researchers had been making an attempt 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 discovered the biologists are hungry to run these experiments, however there may be such an enormous hole they felt they needed to grow to be a software program engineer or simply concentrate on 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 includes analyzing knowledge from whole genomes, inhabitants research, RNA sequencing, mass spectrometry, and extra. Growing personalised therapies or choosing affected person populations for a scientific examine may also require large datasets, and there are new methods to investigate knowledge being printed in scientific journals on a regular basis.
In the present day, corporations 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 most typical knowledge sorts to speed up their work. Widespread AI-based instruments like AlphaFold and Geneformer are additionally obtainable, 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 really the identical. I keep away from the phrase product as a result of that suggests you deploy one thing and you then simply run it at scale ceaselessly. Analysis isn’t like that. Analysis is about developing with an concept, testing it, and utilizing the end result to provide you with one other concept. The quicker you’ll be able to design, implement, and execute experiments, the quicker you’ll be able to 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.
“In case you will help scientists unlock insights not a little bit bit quicker, however 10 or 20 occasions quicker, it may actually make a distinction,” Wang says.
Watershed is being utilized by researchers in academia and in corporations of all sizes. Executives at biotech and pharmaceutical corporations additionally use Watershed to make choices 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 making an attempt to do our half to allow the way forward for biology.”
