a lot of interest. “There’s been a lot of demand for 1,000 genomes pilot data in Amazon Web Services,” Singh says.
Showing the scientific value is one part of the equation, but the business proposition is just as important. Amazon’s case is a pretty simple one. A lab can make a big capital expenditure upfront, but they usually have peaks and valleys of computing power needs. That means the lab isn’t really fully utilizing its server capacity all the time. Plus, the new breed of sequencing instruments are getting so cheap and fast that there’s no way a lab can really anticipate its server capacity needs in the future. Instead of buying expensive equipment and risk getting it maxed out in a year or two, the argument goes, why not lean on Amazon and its basically limitless capacity and flexible pay-as-you-go pricing model?
That may make sense for a lot of labs, but Amazon has found it needs to tailor its product a bit more for life sciences customers. The feedback prompted Amazon to make one curious old-economy concession to help with cloud computing. For some customers who don’t have the bandwidth to efficiently communicate with Amazon’s cloud, the company has set up what it calls an “import/export” service, which allows the lab to save their data on disk and physically ship it to Amazon via FedEx. This helps with some customers who want to know they can get their data to and from Amazon in a reliable way, on a predictable time schedule, without hogging up too much bandwidth on campus.
As much as academic labs might be interested in what Amazon is offering, they can only do as much as their funding agencies really allow. And there are rumblings that the U.S. National Institutes of Health, the world’s primary funding agency for biomedical research, might be facing budget cuts in not so distant future.
Budget cuts at NIH could actually benefit Amazon, Singh says. It could put pressure on labs to be more careful with their capital spending, think a little harder about whether to build their own server clusters, and look more closely at alternatives like cloud computing. Even though the cloud is supposed to be cheaper, Amazon has felt the need to offer customers discounts on price. The company has started offering a cloud infrastructure service with less backup capability for the data, or “redundancy.” That offering is still a more durable backup option than a lab can build on its own, Singh says, and it comes at a lower price than Amazon’s regular cloud offering. The “Reduced Redundancy” service makes sense, he says, for a dataset that can be quickly reproduced (like another sequencing run on a blood sample, if the data is lost, for example).
Most of the interest in Amazon’s offering is from academic labs, rather than with biotech companies and Big Pharma, Singh says. If Amazon can gain a toehold first in academia, it will almost certainly look to continue the momentum in Big Pharma, which spent an estimated $45.8 billion on R&D last year. Big Pharma spends all that money, and is still living with an abysmal success rate, in which only one out of 10 drugs that enters testing ever becomes an approved product.
Sequencing of individual human genomes, and analysis of how they differ, is one of the ways researchers are hoping to someday lower the cost and increase the odds of success in developing new medicines. It’s a long-term trend that Amazon wants to be in position to reap.
“Over the past 12 months, there’s been significant interest. All you have to do is look at conference agendas,” Singh says. “The nature of the conversation has shifted from ‘What should we do?’ to ‘What are we doing, and how should we do it?'”
