The advancements in diagnostic and sequencing technologies in recent years have been nothing short of revolutionary. These developments have paved the way for researchers to amass a treasure trove of biological data. However, translating this data into actionable insights or medical breakthroughs often requires something more than simple domain knowledge — it necessitates wieldy computational skills. This is where the groundbreaking work of Watershed Bio comes into play.
Watershed Bio’s innovative, cloud-based platform empowers scientists and bioinformaticians to run complex experiments and analyze massive datasets without the need for coding. This is made possible through customizable interfaces and workflow templates that can handle a diverse range of data types such as whole-genome sequencing, transcriptomics, proteomics, metabolomics, high-content imaging, and protein folding among others.
“Scientists have an eagerness to learn about the software and data science aspects of their field, yet they don’t aspire to become software engineers just to decode their data,” says Jonathan Wang ’13, SM ’15, co-founder and CEO of Watershed Bio. “We’ve designed Watershed in a way that they won’t have to.”
Wang’s journey kickstarted at MIT, where he intended to major in biology, but soon found himself drawn to the scalability and immediate feedback loops of computer science. He graduated with both bachelor’s and master’s degrees from the Department of Electrical Engineering and Computer Science (EECS). Wang’s experiences interning in a biology lab, impressed upon him the glaring contrast between the dynamic feedback environments in computer science and the slow, manual experiments in biology.
Prior to launching Watershed, Wang co-founded a high-frequency trading firm with a team of MIT classmates. During this tenure, he identified a recurring problem: researchers excelled at building prototypes but grappled with converting these into production-ready models — a task that fell on engineers, who often lacked a comprehensive understanding of the research process, stalling innovation.
To combat this, they developed a software layer that simplified the deployment of production-ready models, mirroring the ease of building prototypes. Years down the line, Wang saw an analogical bottleneck taking shape in biology. The cost of sequencing had dropped significantly, leading to a deluge of biological data. However, the computational tools to process this data were lagging, placing biologists at the mercy of engineers or data scientists who didn’t always grasp the biological context.
Spotting these similarities, Wang co-founded Watershed in 2019 with physician and fellow MIT alum Mark Kalinich ’13. The company has made a concerted effort to bridge this gap and has since grown, catering to academic labs and biotech firms of various sizes.
Watershed’s pioneering platform is designed to make sophisticated tools like AlphaFold and Geneformer accessible to its users without the need for setting up servers or writing code. It offers pre-built templates that support the most common data types, the capability to run large-scale analyses in the cloud, and the convenience of sharing workflows and results with collaborators effortlessly.
Wang is of the belief that expediting the research process even by a factor of 10 or 20 could be a massive gamechanger in propelling scientific progress forward. Watershed’s platform is already being utilized by pharmaceutical companies for making strategic decisions about experiments and drug candidates, as well as by academic researchers who wish to delve into complex datasets with relative ease.
Seeing the platform’s success across these different arenas, Wang observes that the common thread is the understanding of research, albeit without proficiency in computer science or software engineering, and the desire to accelerate discovery. “It’s exciting to see this industry evolve. For me, it’s great being from MIT and now to be back in Kendall Square where Watershed is based. This is where so much of the cutting-edge progress is happening. We’re trying to do our part to enable the future of biology.”
For the original article, refer to MIT News.
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