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 information 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 supplies workflow templates and a customizable interface to assist customers discover and share information of every type, together with whole-genome sequencing, transcriptomics, proteomics, metabolomics, high-content imaging, protein folding, and extra.
“Scientists need to be taught concerning the software program and information science elements of the sector, however they don’t need to grow to be software program engineers writing code simply to grasp their information,” co-founder and CEO Jonathan Wang ’13, SM ’15 says. “With Watershed, they don’t need to.”
Watershed is being utilized by giant and small analysis groups throughout business and academia to drive discovery and decision-making. When new superior analytic methods 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 information are solely getting higher and cheaper,” Wang says. “Coming from MIT, this concern was proper in my wheelhouse: It’s a tricky technical drawback. It’s additionally a significant drawback as a result of these individuals are working to deal with illnesses. They know all this information has worth, however they battle to make use of it. We need to assist them unlock extra insights sooner.”
No code discovery
Wang anticipated to main in biology at MIT, however he rapidly acquired excited by the probabilities of constructing options that scaled to thousands and thousands of individuals with pc 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 sluggish and labor-intensive experiments had been.
“I noticed the distinction between biology and pc science, the place you had these dynamic environments [in computer science] that allow you to get suggestions instantly,” Wang says. “Whilst 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 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 shifting 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 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 group developed a software program layer that made constructing production-ready fashions as straightforward as constructing prototypes on a laptop computer. Then, a number of years after graduating MIT, Wang seen applied sciences like DNA sequencing had grow to be low cost and ubiquitous.
“The bottleneck wasn’t sequencing anymore, so folks stated, ‘Let’s sequence every little thing,’” Wang remembers. “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 information scientists and bioinformaticians to assist them, however these folks didn’t all the time 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 making an attempt to work with engineers, however the engineers by no means totally understood, and also you had all this inefficiency with folks ready on the engineers,” Wang says. “In the meantime, I realized the biologists are hungry to run these experiments, however there’s such an enormous hole they felt they needed to grow to be 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 not concerned in day-to-day operations of the corporate.
Wang has since heard from biotech and pharmaceutical executives concerning the rising complexity of biology analysis. Unlocking new insights more and more entails analyzing information from complete genomes, inhabitants research, RNA sequencing, mass spectrometry, and extra. Creating customized remedies or deciding on affected person populations for a scientific research also can require large datasets, and there are new methods to investigate information being printed in scientific journals on a regular basis.
At the moment, 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 commonest information varieties to speed up their work. Fashionable AI-based instruments like AlphaFold and Geneformer are additionally obtainable, and Watershed’s platform makes sharing workflows and digging deeper into outcomes straightforward.
“The platform hits a candy spot of usability and customizability for folks of all backgrounds,” Wang says. “No science is ever actually the identical. I keep away from the phrase product as a result of that suggests you deploy one thing and then you definately simply run it at scale eternally. Analysis isn’t like that. Analysis is about arising with an thought, testing it, and utilizing the result to provide you with one other thought. The sooner you possibly can design, implement, and execute experiments, the sooner you possibly can transfer on to the subsequent 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 might help scientists unlock insights not a bit bit sooner, however 10 or 20 instances sooner, it could 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 selections about new experiments and drug candidates.
“We’ve seen success in all these areas, and the widespread thread is folks understanding analysis however not being an skilled in pc 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 relies. 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.”
