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 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 be taught in regards to the software program and knowledge science components of the sector, 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 info 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 situation was proper in my wheelhouse: It’s a troublesome technical drawback. It’s additionally a significant drawback as a result of these individuals are working to deal with illnesses. 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 tens of millions 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 gradual and labor-intensive experiments have 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. “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 workforce 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 have been transferring slowly as a result of the researchers have been used to constructing prototypes,” Wang says. “These have 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 plenty of forwards and backwards. It meant concepts you thought may have been applied in a day took weeks.”
To unravel the issue, Wang’s workforce 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 folks stated, ‘Let’s sequence the whole lot,’” Wang remembers. “The limiting issue turned computation. Individuals didn’t know what to do with all the information being generated. Biologists have been ready for knowledge 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 have been attempting to work with engineers, however the engineers by no means absolutely understood, and also you had all this inefficiency with folks ready on the engineers,” Wang says. “In the meantime, I discovered 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 in regards to the rising complexity of biology analysis. Unlocking new insights more and more includes analyzing knowledge from complete genomes, inhabitants research, RNA sequencing, mass spectrometry, and extra. Growing personalised remedies or choosing affected person populations for a medical examine may also require big 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. Fashionable 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 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 means you deploy one thing and you then 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 give you one other thought. The quicker you’ll be able to design, implement, and execute experiments, the quicker you’ll be able to transfer on to the subsequent one.”
Accelerating biology
Wang believes Watershed helps biologists sustain with the most recent advances in biology and accelerating scientific discovery within the course of.
“In case you may help scientists unlock insights not a little bit bit quicker, however 10 or 20 occasions quicker, it could actually 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 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 is predicated. That is the place a lot of the cutting-edge progress is occurring. We’re attempting to do our half to allow the way forward for biology.”
