The explosion of quicker and cheaper DNA sequencing tools hasn’t just made it easier to map a person’s genome. It’s also created several potential niches for fledgling companies coming up with answers for what to do with all the data those tools generate. SolveBio, a new startup based out of New York, is the latest example.
SolveBio is announcing today that it has raised $2 million from VC firm Andreessen Horowitz; Max Levchin (the co-founder of PayPal); SV Angel (a San Francisco-based angel firm); Nat Turner and Zach Weinberg (co-founders of New York-based Flatiron Health, a startup that just raised $130 million last week); Quora founder Charlie Cheever; and others. The cash will help SolveBio develop and market a platform that’s designed to speed up the process of finding reference data against which researchers and doctors can compare patients’ DNA sequences.
“Genetic testing is becoming relevant; it’s becoming mainstream—and it’s entirely dependent on reference data,” says co-founder and CEO Mark Kaganovich. “We see this as being a platform on which a very large percentage of genetic tests and genetic research applications are run.”
Startups are finding places all along the genomics food chain to fit into. Edico Genome, for instance, is developing a processor that can be installed into next-generation sequencers to cut the time it takes to map DNA segments. Other companies, like DNAnexus and Curoverse, have come up with ways to efficiently store large amounts of genetic information. Still others, like Knome and Cypher Genomics, are devoted to interpreting sequencing reports and finding out if particular genetic variants are associated with a certain disease.
Kaganovich (pictured above, left, with chief technology officer David Caplan) says that such companies share one common feature that creates a big need: all of them compare individuals’ DNA sequence and health information to reference data. Such data are stored in a host of different databases built up over years by public and private entities. What’s more, says Kaganovich, they have “inconsistent formats and quality, and [are] not programmatically accessible.” This can present a huge headache for programmers trying to build applications that draw on genomic and health reference information from the disparate sources.
“It takes a team of internal programmers to build the infrastructure to collect the data, parse it, normalize it, connect it, and serve it up to clinicians,” Kaganovich says.
This is where SolveBio is supposed to come in. SolveBio has come up with a proprietary application programming interface, or API—a tool that helps software programs interact with one another—that would act as the technological middleman for programmers building, say, a diagnostic application that taps into the various databases. The company is also partnering with other organizations to collect the relevant reference data and distribute it to customers.
“Instead of having to download all of that data into a database themselves, they [would] just write a few lines of code, and then that application calls our application, and our application gives them the right kind of data they need,” Kaganovich says. “Generally what we noticed is that there were genomic interpretation companies out there, but there wasn’t anyone that was doing something for programmers.”
SolveBio is primarily targeting people in hospitals and companies who are building diagnostic and research applications. The company will probably charge either a monthly or yearly subscription, and customers would pay a rate tied to the amount of work they’d need done. Kaganovich compares the concept to Amazon Web Services-like model, where “the more business your website does, the more you pay Amazon.” Customers would also pay SolveBio to manage licenses for datasets they’d have to pay to get access to, he says.
Kaganovich says the platform is in a “private beta phase” right now. While it’s still building and tweaking the system, it does have a few customers, including a “major hospital” that is using the system to build a diagnostic application for exome sequencing. The company has five employees right now, though it intends to roughly double its size by adding more programmers to its staff with the cash before possibly raising more funding in “about a year,” Kaganovich says.
Of course, Kaganovich acknowledges that “there are a lot of unknowns” here for SolveBio, including the size of its potential market opportunity and its growth trajectory. Various factors are converging to open up the market for SolveBio—cheaper DNA sequencing, more startups and potential customers in the field, more genetic testing, and increasing scientific understanding of important genetic mutations—but it’s anyone’s guess as to how fast that would turn into tangible opportunities for the company. Kaganovich says that going forward, SolveBio is also going to add more to its pitch than cost savings—namely, getting its hands on new sets of data that others don’t have—to stand out.
Still, Kaganovich is hoping SolveBio’s platform can help push the market in part by becoming a key tool for genetic testing startups.
“We think we can move some of this forward,” he says. “People want to find out more precise information about the diseases they have or might have in the future, and adding this computer science element to analyzing biological data makes this whole process more precise.”