May 11, 2011
Next generation DNA sequencing has brought a wealth of opportunities in research, pharmaceutical and clinical contexts, but for those who are in the high performance computing space, this particular market is bursting with a different array of opportunities. From specialty clusters dedicated exclusively to crunching the overwhelming amounts of data coming of sequencers (not to mention the storage might to keep it all in check) the biosciences industry is a prime target for vendors of all stripes.
Interestingly, with the rise of cloud computing and on-demand resources, investment in hardware for many companies isn’t always the first option. According to Tom Coull, Senior Vice President at Penguin Computing, a large number of DNA-driven companies are finding on-demand HPC a perfect fit, especially since their demands for high throughput computing are large but generally sporadic.
Providers of on-demand high performance computing that have an eye on this particular industry (Penguin Computing, Cycle Computing, and SGI in particular) have little elbow room in this tight market to garner valuable life sciences business. In addition to competing with public cloud resources like Amazon EC2, not to mention competition from traditional modes of computing (buying your own cluster) such services have to run a tight ship to keep their own hardware investments churning at peak capacity.
This issue of peak capacity is critical for both users of on-demand HPC and for the providers themselves. Naturally a provider like Penguin wants to make sure their investment is being fully utilized and they’re retuning a profit on the core hours spent. On the flip side, however, life sciences companies want to make sure that they’re balancing time-to-market concerns with core competency arguments.
To be more specific about this balance of issues, we spoke to Abe Lietz who heads IT for a major life sciences firm, Life Technologies. This global company provides a range of solutions for customers in the industry, from biological products for research to the instrumentation to back next-generation DNA sequencing efforts. In short, as Abe told us, “our core competency is about keeping pace with a rapidly changing industry; things change quickly and it’s not part of our goal to put the extreme time and resources into running our own IT the right way.”
Life Technologies is using Penguin Computing’s HPC on-demand (POD) offering to back a web interface into one of its most popular software packages for gene sequence analysis, Bioscope. While on the surface this might sound like a simple enough offering, the complexity of Bioscope and the fact that it is residing on collocated servers in Salt Lake City goes deeper than one might imagine.
Users log in through solidbioscope.com and are able to use the pay as you go model to analyze genomic data, using Penguin’s storage and resources exclusively. Penguin’s Coull noted that the pricing is roughly equivalent to what you might get with a similar cloud provider but unlike with a public cloud, users are able to know exactly where their data is at any given moment—an important issue for the HIPPA compliance-aware.
Coull also noted that for genomics researchers considering this from a purely cost-driven basis, if you’ve built and maintained a cluster based on peak requirements and you’re not using it at 35 percent on a full-time basis, you’re better off using an on-demand resource provider. During our phone interview he was watching POD activity from his screen and noted that of the applications that were running at any given moment, a good estimation is that 50% of users have replaced their in-house systems electing to use POD exclusively while the other half were the sporadic users who make up a nice portion of the life sciences on-demand market due to the spotty need for big computation.
On a side note, Coull says that Penguin expects 4-fold growth over the next year for their POD service with the build-out of two AMD and Intel partnerships for new POD centers. Although he didn’t comment what percent of the business was life sciences driven, he noted this market was “significant” and that they’d seen a surprising uptick from academic institutions that needed extra resources.
Coull noted as well that their software stack has been tweaked by users to be able to bridge over to other cloud computing options, including Amazon’s S3, due to the fact that it seems to be one of the most popular storage options for this type of user.
It’s worth noting, by the way, that this was not Life Technology’s first interaction with Penguin Computing. The company had been providing hardware services to support Life Technologies’ proprietary software since 2007.
According to Penguin, this is a side effect of having a solid reputation with customers who are software-driven—if their in-house systems perform well and they like the service and support, it’s a natural fit for users to consider using their remote resources if they fit the bill.
Coull noted that some users are getting creative about using the POD service. For instance, during his occasional glances at the real-time reports from the POD interface, there were Life Technologies training sessions going on in real time, which gave users the chance to work in a hands-on fashion with the software.
VP of Life Technologies, Jeff Cafferty also weighed on this, noting that beyond sheer training, potential customers interested in evaluating analytics options (since there are many—and many are non-proprietary) could hop on the POD-driven solidbioscope.com resource and compare results, including mappability and other specific factors.
In addition to extolling the benefits of the cloud beyond just analytics, Cafferty told us, “We are in the post-human genome sequencing project phase of life sciences” what’s happened in this last decade is that companies like ours have been developing evermore high throughput technologies for sequencing DNA and furthermore the cost of sequencing has gone down tremendously. What this means is that there’s been a huge explosion in the amount of sequence information available for life sciences researchers.
This is a fact that is driving the next big buzzphrase after cloud computing—“big data”—into every marketing message, particularly on the storage end, for obvious reasons. While the massive data end of the equation is a major factor that is causing genomics researchers to consider looking beyond physical hardware, the computational requirements are nothing to sneeze at either.
Caffrey put this in context, noting that to sequence a human genome researchers are dealing with something that is 3 billion base pairs long. Their instrumentation for next generation sequencing creates what are called “short reads” of DNA and in one genome, this creates billions such reads that then need to be mapped back to a reference genome.
He also elaborated on a topic that is growing nearer and dearer to storage, compute, software and cloud vendors alike: “Life sciences researchers have traditionally functioned on an experimental model that involved a great deal of time generating data (biological samples can be rare or hard to extra information from) and relatively small amounts of time analyzing it, in part because there just wasn’t very much of it. In sequencing in particular this paradigm has been flipped—we’re now generating a tremendous amount of data in a very short period of time and thus the length now is because of the mining, management, comparing and analysis of all that data.”
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