Speaking at GigaOM’s Structure:Data conference Wednesday, Harvard Associate Professor Karim Lakhani said crowdsourcing the genomics project did several things. First and foremost it generated dozens of different approaches to tackling the same problem.

Before the TopCoder contest was created, researchers were considering two different paths of investigation. The contest revealed 89 differing approaches to the problem, 20 of which were extreme values — possibilities Harvard and its National Institutes of Health counterparts had never considered.

Second, the contest was able to create motivation to solve problems that you wouldn’t necessarily find in a group of researchers. An institution like Harvard may have brainpower in spades, Lakhani said, but throwing a bunch of geniuses at a problem doesn’t necessarily lead to result if they’re unmotivated to solve it.

“When you go into a self-selection model you don’t have to worry about motivation,” Lakhani said. There are monetary rewards for winning a TopCoder contest, of course, but taking home a prize is not guaranteed. Everyone has their own motivation for participating, whether it’s cash, experience, scoring reputation points or even the free T-shirts given to each participant. “Because there are large numbers of people participating, there’s a greater chance you’ll find the right skills and the right motivation,” Lakhani said.

Ultimately crowdsourcing your science isn’t a replacement for having smart people of your own, Lakhani said, but it certainly helps, something Harvard’s counterparts at NASA have discovered.

CHeck out the rest of our Structure Data 2013 coverage here, and a video embed of the session follows below:

A transcription of the video follows on the next page

Go to page 2 (of 2) on GigaOM .

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The Series A round was led by venture firm DFJ and brings the startup’s total amount raised to $3.7 million. With the new funding, Adina Mangubat, Spiral Genetics co-founder and CEO, said her eight-person team plans to expand product development, as well as sales and marketing.

Mangubat said that when she and one of her co-founders, Becky Drees, first looked at the field of genomics, their plan was to launch a consumer-focused genetic testing service like 23andme. But as that company started launching its services, they decided to switch tacks.

“We were looking at the industry and we wanted to do something really impactful that involved genomics and computing,” she said. When they realized the speed and volume with which raw sequence data was being generated, she said, they spotted an opportunity in offering high-performance bioinformatics tools for analyzing it.

Companies like DNANexus also offer sequence analysis, and others might conduct the analysis in-house, but Mangubat said they envisioned a service that could shrink the turnaround time for researchers and others in industry deluged by data. Last month, Redwood City, Calif.-based Bina Technologies announced the commercial launch of its own genomic analysis platform and similarly touts a faster-than-ever service.

Mangubat and Drees teamed up with their third co-founder Jeremy Bruestle and started building a computing platform specifically intended to solve this kind of big data problem. Now, the company says, it can analyze a whole human genome in 3 hours, which is about 40 times faster than what it might take others.

Spiral Genetics’ customers run the gamut from academic researchers to corporations, Mangubat said. For example, while some clients may use their bioinformatics tool to tackle childhood cancer, others in agrigenomics could use it to sequence different strains of corn.

Along with the new funding, Spiral Genetics announced a new partnership with Omicia, an Emeryville, Calif.-based provider of clinical genome sequence interpretation tools.

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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.

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But to understand how Syapse works, you have to understand the problem it’s trying to solve. A condensed version of the situation is this: Sequencing genomes, proteomes, biomes and other microscopic, but very important, biological players generates a lot of data. However, we’re not just talking about the terabytes of data that a fully sequenced genome (or perhaps the tens of thousands sequenced gut bacteria, which can change composition hourly) will produce, but also patient data (e.g., name, date of birth, smoker or non-smoker, etc.) and process data (i.e., everything that happens from the time a lab gets a sample to the time a doctor gets a report on his desk).

The complexity and perpetually changing nature of both the field of “omics” as it’s called, and the data itself, further complicates things. According to Syapse Co-founder and President Jonathan Hirsch, diagnostics labs and workers are always using new and different processes trying to optimally extract, tag and analyze samples. Furthermore, expert knowledge of what any particular genetic or other signature means is always changing (for example, Hirsch said, we only really understand about 1 percent of the human genome), as are the ontologies that lab workers, researchers and physician specialists use as their particular fields evolve.

“There is basically a wholes set of measurements that go beyond just sequencing the genome,” he explained. Analyzing genomes, proteomes and anything else is “like a very, very complicated recipe” that involves much more than swabbing someone’s cheek and getting back a comprehensive, understandable report. Syapse doesn’t actually do any of the sequencing work (like a DNAnexus or Bina Technologies does,) but just captures the metadata from those lab processes and connects to those hefty sequenced data via an API so the platform has access to everything it needs.

Organizing complex data requires a graph

Using semantic-analysis and graph-processing techniques, Syapse thinks it can bring the world of “omics” under control. Although it’s currently working with research centers that analyze the data in order to better hone their processes, Hirsch expects the company will eventually make most of its money from doctors and hospitals using Syapse to help better diagnose their patients. “[We're] trying to fill the gap and be the company that cracks the physician side of this,” he said.

This is where the Graph Search comparison comes into play. The Syapse platform is continuously updated with the latest ontologies from various fields and the changing meanings of the metadata associated with the various lab processes. All this information is stored based on its relationship to other pieces, and semantic analysis means the Syapse software knows that Term X in one field might actually mean Term Y in another.

Syapse has essentially created a “huge knowledge graph” of clinical, diagnosis and omics data, Hirsch explained, and doctors and researchers can mine it using whatever terms they use in their daily lives. They can easily search, for example, by patients they’ve treated for breast cancer whose genes showed certain specific markers and were processed using particular techniques in the lab in order to find connections among them.

Syapse Co-founder and CEO Glenn Winokur — an admitted “IT guy” compared with his biotech-focused partners — likes to put the platform’s promise in the terms of business software. ”Think of this entire workflow as similar to a sales or marketing workflow,” he said, adding that Syapse is trying to make mining omics data as simple for its users as Salesforce.com makes CRM for its users.

That’s probably a good analogy for selling the software to hospital administrators who might be more concerned with budgets than with big data technology. As we’ll discuss in more detail at our Structure: Data conference on March 20-21, business people are increasingly concerned with using data to make better decisions, but they need applications that make it easier and faster to find stuff out than is possible with many open source packages targeting engineers and statisticians. If Syapse can deliver on this promise for making sense of our complex biological systems, it could make a big difference.

Feature image courtesy of Shutterstock user kentoh; lab image courtesy of Shutterstock user VILevi.

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On Monday, Google announced on its Research Blog the six projects which it granted 100 million core-computing hours apiece as part of the Google Exacycle for Visiting Faculty program last year. The projects, most of which are led by university researchers (and one by a Google researcher), tackle a variety of pharmaceutical and biological challenges, as well as the problem of analyzing petabytes of data generated by a forthcoming astronomical survey project. The latter hopes to “[reduce] the time required to simulate one night of [Large Synoptic Survey Telescope] observing, roughly 5 million images, from 3 months down to a few days.”

Although these projects require a lot of horsepower to tackle enormous datasets and tough compute problems, Google’s Exacycle program is hardly the first attempt to use to cloud for good. In 2010, for example, Microsoft launched a version of the NCBI BLAST data-analysis tool on its Windows Azure cloud and gave away free access to researchers. Amazon Web Services also hosts its own research grant program that awards access to its cloud computing infrastructure. Both companies also host large genomic and other datasets that are available to anyone using their services.

Free or not, though, cloud computing has proven a boon for researchers hungry for computing power but not keen on waiting in line for supercomputer resources or sending massive data across the network. Google pimped its Compute Engine cloud as a scientist’s best friend when launching the service in June, and AWS recently highlighted how one customer built an 8,000-core system that churned out 115 years of work in just one week.

Feature image courtesy of Shutterstock user Sashkin.

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Here are some of the interesting use cases we’ve written about over the past year or so, and a few others I’ve just come across recently. If you have a cool one — or a suggestion for a new use of big data within the healthcare space — share it in the comments:

• Genomics. This is the epitomic case for big data and health care. Genome sequencing is getting cheaper by the day and produces mountains of data. Companies such as DNAnexus, Bina Technologies, Appistry and NextBio want to make analyzing that data to discover cures for diseases faster, easier and cheaper than ever using lots cutting-edge algorithms and lots of cloud computing cores. Dell is providing computing power for two research centers to try and treat a particular form of pediatric cancer based on each child’s specific genetic profile.

• BI for doctors. Doctors and staff at Seattle Children’s Hospital are using Tableau to analyze and visualize terabytes of data dispersed across the institution’s servers and databases. Not only does visualizing the data help reduce medical errors and help the hospital plan trials but, as of this time last year, its focus on data had saved the hospital $3 million on supply chain costs.

• Semantic search. Imagine you’re a doctor trying to learn about a new patient or figure out who among your patients might benenfit from a new technique. But patient records have been scattered throughout departments, vary in format and, perhaps worst of all, all use the ontologies of the department that created the record. A startup called Apixio is trying to fix this by centralizing records in the cloud and applying semantic analysis to uncover everything doctors need, regardless who wrote it.

• Hadoop for everything. Cloudera is partnering with the Mount Sinai School of Medicine to help it develop new methods and systems for analyzing biological data. But that’s just the latest of Cloudera medical efforts, which also include working with the Food and Drug Administration to detect unsuspected adverse side effects from multi-drug combinations, and Emory University on helping pathologists more accurately analyze medical images. One of Cloudera’s customers, Explorys, built a business around aggregating and analyzing medical records, and Intel and NextBio are teaming to tune Hadoop for processing genomic datasets.

• Watson. IBM has dozens of irons in the healthcare fire, but its coolest might well be a partnership with WellPoint to put the Jeopardy! champion question-answering system in doctors’ offices. Watson could help doctors answer questions posed in natural language by analyzing them against mountains of medical research data that no individual doctor could possibly read and digest.

• Getting ahead of disease. It’s always good if you figure out how to diagnose diseases early without expensive tests, and that’s just what Seton Healthcare was able to do thanks to its big data efforts. Trying to find better ways to detect congestive heart failure early in order to save the exorbitant costs of treatment as the disease progresses, a team found that a distended jugular vein — something that can be spotted during any routine physical exam — is a particularly high risk factor.

• Data scientist in residence. Here’s a new title for a healthcare organization — chief data scientist. Yet, that’s exactly the position Alliance Health Networks just added in May. The company, which provides social networks focused on specific medical conditions, acquired medical research database Medify and decided it needed someone to lead the effort of analyzing all that data and providing valuable feedback to network users.

• Crowdsourced science. In a field where controlled experiments can be expensive and sometimes ineffective, it’s turning out there might be no substitute like the real-world data. Probably the most widely known company in this space is PatientsLikeMe, a social network designed to let individuals share their medical conditions so they can learn from others like themselves what treatments might work best in their particular circumstances. As a side effect, the company is able to conduct observational trials based on data users willingly volunteer.

Feature image courtesy of Shutterstock user lenetstan; Tableau graph courtesy of Perceptual Edge; exam image courtesy of Shutterstock user Blaj Gabriel

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Starting at about 15:50 in the video below, Hammerbacher tells a room at the HISUM2012 conference about some of the work Cloudera has already done in the healthcare space. Among those efforts was a project along with the Food and Drug Administration to detect unsuspected adverse side effects from multi-drug combinations, and an application developed with Emory University to help pathologists more accurately analyze medical images.

Feature image courtesy of Shutterstock user 18percentgrey.

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In order to understand digitalDNA, though, you first must be familiar with ADNAS’s core technology. Its flagship product, called SigNature DNA, takes specially created, double-stranded DNA signatures derived from plant DNA and combines them in solution made out of ink or some other material. That solution can be applied directly to a product — anything from textiles to microchips to documents — or applied to an invisible bar code that can be read by scanners capable of detecting the DNA strand. Marks can also be swabbed and sent to ADNAS for verification.

Companies using SigNature can verify the authenticity of shipments by scanning the products they receive. If the products aren’t legit, businesses don’t accept them and, presumably, an investigation ensures. Presently, Miller said, this process is unreproducible, meaning would-be counterfeiters can’t one-up ADNAS customers by replicating their authentication method as well as the product itself. In January, Wired published an article about how the U.S. Department of Defense is using SigNature to detect bogus microchips in military equipment.

Aside from simply stopping counterfeiting activity, though, SigNature is also used to prosecute criminals because the DNA markers are all-but irrefutable evidence (the false positive rate is 1 in a trillion) that someone is in possession of stolen goods. In the United Kingdom, Miller told me, more than a quarter of all cash in banks is marked with using SigNature in order to catch criminals who steal it from transporters such as ADNAS customer Loomis. ADNAS also sells products that pre-mark certain items in order to transfer DNA to thieves, or that spray fleeing intruders with DNA.

Another company, called DNA Technologies, claims to use a similar method for anti-counterfeiting and actually tagged the footballs to be used in Super Bowl XLVI. Unlike RFID tags, DNA marks can be placed even on small individual objects, or incorporated into them in the case of clothing, for example, and cannot be easily removed.

Turning DNA into QR codes

The new digitalDNA product takes SigNature to the next level by tying it to cloud computing, big data and mobile phones. The unique DNA signature still exists on the physical QR code applied to packages, but it has also been digitally encrypted onto to a 2-dimensional QR code in a way ADNAS claims is not copyable. As packages move along the supply chain, employees equipped with iPhones and the ADNAS app can scan products to chart their progress and verify authenticity. But that’s just the beginning.

With every scan, information is also sent to a cloud-based database where it’s stored and analyzed a set of algorithms specially designed to identify patterns associated with counterfeiting or theft. If something pops up, companies can be proactive in trying to determine the problem or take measures to prevent a crime. And even if there isn’t nefarious activity taking place, digitalDNA users can still use the geospatial data they’re generating to get a better handle on their supply chain dynamics.

Looking to the future, Miller said ADNAS is also experimenting with methods for using the ubiquity of iPhones to bring consumers and retail outlets into the fold. That could mean anything from scanning the DNA-based QR code to ensure the freshness of a product to helping stores identify sales trends. Admittedly, though, those uses are a while out and would require cooperation from ADNAS’s customers, which are the ones dealing directly with resellers and consumers. Presumably, the DNA-based QR codes could provide more granular data because they’re tied to individual units of products.

However digitalDNA usage evolves, even if it never really takes off, the high-level concept behind the product is sound. As we’ll discuss in numerous sessions at our Structure conference next month in San Francisco, there’s an undeniable connections between cloud computing, big data and mobile technologies as it relates to capturing, storing and processing entirely new types of data. When literally anybody with a mobile phone and the right app can scan a code and send rich data up to the cloud, it opens up entirely new possibilities around both analytics and application architectures.

All images courtesy of Applied DNA Sciences.

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Domain expertise, statistics and HPC, unite!

The latest is Bina Technologies, which just emerged from stealth mode last week and is bringing an Apple-like business model to genomics. The company, which grew out of a research project at Stanford University, relies on its Bina Box appliance to make genome analysis faster than typically possible without the benefit of having a supercomputer and a research network on hand.

According to Bina CEO Narges Bani Asadi, who co-founded the company while completing her Ph.D. at Stanford, the appliance came about as part of a mission to solve a disconnect among the stakeholders in cancer research. Improving the analysis of cancer data required input from medical researchers, statisticians and high-performance computing experts, “but people are not speaking even the same language,” she said. While they’re all headed in the same direction, their paths rarely converge to harness peak velocity.

Bani Asadi and her team solved that problem by developing a system that merged the three areas into one. With Bina, researchers can develop analysis pipelines that are optimized at both the algorithmic and silicon levels to run optimally across a mix of CPUs, GPUs and FPGAs, all of which are present within the purpose-built box. Applications are getting what they need in order to perform their best, and Bina says results can be processed 10 to 100 times faster (hours instead of days) than running jobs on the Amazon Web Services cloud, which has proven very popular for genomics workloads thanks to its supercomputer-like performance.

That being said, a chart Bina uses to illustrate the performance difference compares the Bina Box to a single eight-core AWS instance rather than a cluster of those high-performance instances.

The Apple analogy

Bani Asadi answers the inevitable question of whether research centers will want to special appliances instead of using the cloud or generic servers by pointing to Apple. That company’s devices and computers can be a little more expensive and more difficult to tinker with than alternatives, but they’re also designed specifically with Apple’s operating system and applications in mind. It’s an analogy other companies, such as cloud computing startup Nebula, also use to justify their appliance-based businesses.

Not that Bina dismisses the cloud. If Bina’s software is the Mac OS to the Bina Box’s iMac, the Bina Cloud is the company’s iCloud. Once the box processes the raw sequencing data and compresses it into a smaller volume (up to 1,000 times smaller), the data is shipped to the Bina Cloud where it’s stored and can be easily accessed and shared. Actually, Bani Asadi said that’s where the most-innovative research likely will take place. While Bina’s appliance handles the necessary first steps of  genome analysis (e.g., determining how it’s unique), it’s the resulting data sets that are accessible by doctors, specialists and others to really make sense of it all.

Presumably, Bina is referring to companies such as DNAnexus when it compares its solution to entirely cloud-based approaches. DNAnexus is another Silicon Valley startup trying to democratize genome analysis, relying on the processing power and centralized nature of the cloud to serve as a platform for analyzing and collaborating on DNA data. Another startup, St. Louis-based Appistry, has taken a somewhat different approach, building its own high-powered cloud service and developing its own algorithms specially designed for genome analysis.

In the end, it’s all about the data

Regardless of which approach a researcher takes to solving the problem of sequenced genome data (they all have unique benefits), the underlying trend driving innovation is the deluge of genome data itself. Bani Asadi said the biggest difference now compared with past efforts to analyze health data is that we have so much available. There are 30,000 fully sequenced genomes available right now, and some predict there will be 10 million in five years.

That means researchers can study DNA at a much more-granular level the previously possible, Bani Asadi said, and they can analyze findings across huge data sets to identify previously undetectable patterns. Especially with cancer, she said, each case is relatively unique, shaped by many conditions and factors. If we’re going to make significant progress on treating it, we’ll need to know exactly what’s going on in any given case and how similar cases have played out. The more data, the more accurate the diagnosis and treatment.

Feature image courtesy of Keith Edkins.

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1TB times 1 million equals …

How much data is anybody’s guess, but the exponential increases in productivity suggest it will be in the exabyte range within a few years. A fully sequenced human genome results in about 100GB of raw data, although DNAnexus Founder and CEO Andreas Sundquist told me that volume increases to about 1TB by the time the genome has been analyzed. He also says we’re on pace to have 1 million genomes sequenced within the next two years. If that holds true, there will be approximately 1 million terabytes (or 1,000 petabytes, or 1 exabyte) of genome data floating around by 2014.

A few years ago, Complete Genomics publicly announced its plan to sequence a million genomes by 2014, but it has been woefully behind schedule to this point. It was hoping to do 50,000 genomes in 2011, but finished the year at only 3,000.

Life's Benchtop Ion Proton Sequencer

However, sequencing instruments are evolving in a manner similar to mainstream computers, which is to say they’re always getting faster and more affordable. Whereas sequencers used to cost more than half a million dollars and take up a room, Life’s genome-in-a-day instrument, the one that claims a $1,000-per-genome price point, sits on a desk and will cost only $149,000 when it’s available later this year. Upgrading to Illumina’s new instrument from the previous model costs only $50,000.

The fast rate of improvement comes from genomics’ own version of Moore’s Law, Sundquist said: data throughput and cost both improve by tenfold every 18 months. When Life rival Illumina set a world record in February 2008, it took “less than four weeks at a cost of about $100,000.” At this rate, we’ll have $100 genome sequencing by 2014.

Sundquist added that medical systems have tens of thousands of patients queued up for sequencing, many of which they might start doing now that it can be done so fast and at such a low cost.

Hidden costs: ‘The quest for the $1,000 genome interpretation’

Where things get hairy for IT vendors is figuring out how to make it affordable to store, process and analyze all that data — something Sundquist calls the quest for the $1,000 genome interpretation. It’s still not an inexpensive proposition to buy and maintain a system capable of storing and processing potentially petabytes of data. And if doctors or researchers want to collaborate with colleagues, their facilities bandwidth likely won’t cut it for sending even the raw data for a single genome. That’s why many research institutions are connecting to high-speed research networks designed solely to move massive scientific data sets.

As Forbes’ Matthew Herper opined early last year, even though research costs for genomes will soon cost only $1,000, it costs a lot more to employ people and pay for software capable of analyzing it. Because research genomes aren’t accurate enough for medical use, they often must be sequenced multiple times. Herper’s ultimate analysis:

I’d think if we’re talking about actual medical use, $10,000 is a more accurate number. Certainly, it is not going to drop below the $2,000 level for a magnetic resonance imaging scan. And once the technology is in use, I think it is possible that the costs will go back up.

So, even if genome sequencing itself becomes less expensive, hospitals and patients will both be paying well more than $1,000 for the procedure. Presently, $10,000 is about the going rate from Complete Genomics to sequence, analyze and deliver research results to an individual, although the costs certainly are subject to change if hospitals start performing sequencing workloads themselves.

Cloud computing to the rescue?

Sundquist thinks cloud computing is the answer. His company, DNAnexus, provides a cloud-based platform for storing and analyzing genomics data, something we’ve covered before. “A 100-megabit connection could more than keep up with about a dozen of these machines,” he said, and once the data is in DNAnexus’s cloud platform, institutions no longer have to worry about keeping up with exploding data volumes, sending terabytes of data across the Internet or paying software licenses. Access is centralized and everything takes place on DNAnexus’s virtual infrastructure.

Additionally, cloud computing is ideal for spiky use cases, as is generally the case with genome sequencing.  A general rule of “cloudonomics” is that the cloud costs more on a per-unit basis, but generally will cost less over time unless it’s being used for a steady workload flow better suited to an on-premise system.

Whether it’s DNAnexus or some other cloud service, Sundquist’s reasoning is sound. As prices for gene sequencing continue to fall, doctors should be increasingly likely to do it, but they’ll be limited by the infrastructure in place to support them. Unless the costs of doing this on-premise come down significantly, the cloud might be the only place where storing and analyzing potentially petabytes per hospital isn’t such a daunting undertaking.

Feature image courtesy of Flickr user Robert Gaal.

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