Summary:

New hardware from Bina Technologies gives analysts another example of a use case in which the public cloud isn’t always the most appropriate solution.

Bina Box
photo: Bina Technologies

Bina Technologies is launching its Bina Box for on-premise genome processing, enabling researchers to quickly and cheaply analyze genomes and give doctors data-driven suggestions for custom treatments.

Use a genome sequencer to see one person’s DNA profile, and you’ll get 6 billion unique characters, or half a terabyte of data, said Bina co-founder and CEO Narges Bani Asadi. Start processing it to find mutations and variations, and you’ll find yourself with more than one terabyte. It’s not small data. As the price of sequencing a genome keeps dropping, scientists will want to do this more and more. It’s a big data problem, Bani Asadi said. The company wants to solve the problem on premises, with hardware and software.

The Bina Box will run on “high-end Intel processors and very high-bandwidth memory,” Bani Asadi said, and can scale out with additional Bina Boxes as customers processing needs change. Price depends on how much processing customers have in mind. If a customer wants to process 100 samples a month, for instance, it would cost $12,500 per month, or $125 per sample, said Mark Sutherland, Bina’s senior vice president of business development.

A Bina Cloud to tie in with the Bina Box will come later this year. The Bina Cloud will host just the needle of genomic data isolated from among the haystack of the entire genome, and it will enable scientists to aggregate many genomes, run data visualizations and collaborate to derive big-picture insights. Early customers are already using a pilot version of the cloud.

The box offering contributes more proof of the notion that, for certain uses, public clouds might not make sense, not yet anyway. (It remains a largely popular perspective in financial services, as my colleague Barb Darrow reported a couple of months ago.) The Bina Box, for its part, “provides security that on-premise solutions have, versus cloud solutions, (which) sometimes people in this industry are not completely ready to move into,” Bani Asadi said. Big pharmaceutical companies are a perfect example, as a breach could hamper product development using genomes. Aside from security, there’s the matter of performance. “It’s impossible to send (half a terabyte of raw data from a sequencer) to the cloud easily,” Bani Asadi said.

Meanwhile, other genomics-focused startups, including DNAnexus and Appistry, are eschewing hardware and relying exclusively on cloud resources.

Whether hardware is involved or not, as my colleague Derrick Harris mentioned when he wrote about Bina last year, it’s clear that the rise of big genomics inherently equates to a rise in data.

The practice of merging life sciences and other industries with big data will come up in conversation when Ayasdi CEO Gurjeet Singh hits the stage at GigaOM’s Structure:Data conference on March 20 in New York.

Comments have been disabled for this post