As prices for diagnostic and sequencing applied sciences have plummeted lately, researchers have collected an unprecedented quantity of knowledge 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 expertise. The cloud-based platform gives workflow templates and a customizable interface to assist customers discover and share knowledge of all kinds, together with whole-genome sequencing, transcriptomics, proteomics, metabolomics, high-content imaging, protein folding, and extra.
“Scientists need to be taught in regards to the software program and knowledge science components of the sphere, however they don’t need to turn out 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 must.”
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 situation 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 knowledge 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 shortly acquired excited by the chances 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 Laptop Science (EECS). Wang additionally interned at a biology lab at MIT, the place he was shocked how sluggish 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. “Whilst a single individual writing code, you have got 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 crew employed researchers with PhD backgrounds in areas like math and physics to develop new buying and selling methods, however they shortly 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 may run domestically on their machines. To place these approaches into manufacturing, they wanted engineers to make them work in a high-throughput means on a computing cluster. However the engineers didn’t perceive the character of the analysis, so there was loads of backwards and forwards. It meant concepts you thought might have been carried out in a day took weeks.”
To unravel the issue, Wang’s crew developed a software program layer that made constructing production-ready fashions as straightforward as constructing prototypes on a laptop computer. Then, just a few years after graduating MIT, Wang seen applied sciences like DNA sequencing had turn out to be low cost and ubiquitous.
“The bottleneck wasn’t sequencing anymore, so folks stated, ‘Let’s sequence all the pieces,’” Wang recollects. “The limiting issue grew to become 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 scenario appeared acquainted to Wang.
“It was precisely like what we noticed in finance, the place researchers have 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 discovered the biologists are hungry to run these experiments, however there may be such a giant hole they felt they needed to turn out 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 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. Creating personalised remedies or deciding on affected person populations for a medical research also can require big datasets, and there are new methods to research knowledge being printed in scientific journals on a regular basis.
Right this 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 knowledge varieties to speed up their work. Standard AI-based instruments like AlphaFold and Geneformer are additionally accessible, 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 means you deploy one thing and you then simply run it at scale perpetually. Analysis isn’t like that. Analysis is about arising with an thought, testing it, and utilizing the end 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 most recent advances in biology and accelerating scientific discovery within the course of.
“In the event you might help scientists unlock insights not somewhat bit sooner, however 10 or 20 instances sooner, it could actually 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 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.”
