Just like people used to be afraid of browser cookies and their implications for privacy and now accept them as standard, Shim, Placed’s founder and CEO, said he believes our attitudes toward our location data will undergo the same transition once people understand the inherent value of what they’re getting back in exchange for what they’re giving up.
“Right now everyone’s afraid of [sharing] location … as people start to understand the benefit of sharing this kind of data it will start to become more open,” said Shim. “People won’t mind sharing data if you get something back in return. That perception will chagne over time.”
Jain, founder and CEO of Inome agreed: while some mothers may object in theory to location sharing for privacy reasons, if they were given the ability to know their young child’s exact location and whether they had arrived after walking to school, they may feel differently. Similarly, he noted that people will be willing to offer up even more personal information like DNA or medical histories if they know that the result is (someday) personalized medicine for people with specific genome types.

Chahine, who is SVP and General Manager of Ancestry.com’s DNA service, said he’s seen some of that with DNA. “People aren’t concerned about giving us their DNA,” he said. “They give us their DNA, we give them their result — and you won’t be surprised to learn — we don’t give them a generic result.” In other words, individuals get something out of it, the value of which is highest to them: who else they’re related to. That “value exchange” is crucial and what makes Ancestry.com customers comfortable handing over such personal information. And, he said, they’ll continue to do so “as long as we strike the right balance between privacy and personalization.”

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

A transcription of the video follows on the next page

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From my perspective, what was amazing about the outcome of the ENCODE project wasn’t just the results, but the infrastructure that supported it. According to the press release discussing the findings, ENCODE generated more than 15 terabytes of raw data, and the data analysis consumed the equivalent of more than 300 years of compute time. For those living on the edge of the hyper scale world, these numbers may not be all that impressive — after all Facebook says it takes in 500 terabytes of data a day. But the ENCODE data is shared and accessed by scientists around the world.

And that’s what’s worth thinking about, as we try to build economies and organizations around big data. The ENCODE project didn’t just come up with some new truths about our genetic material: It was a global collaboration that required 32 labs to gather and perform more than 1,600 experiments on more than 147 tissue samples to generate data that would then be further used to make more discoveries.

Jim Kent, director of the UCSC Genome Browser project and head of the ENCODE Data Coordination Center laid out some of the challenges associated with making sure experiments were independent, worthwhile and still generating accurate data. From a release announcing the recent findings from ENCODE:

“For Kent and his data coordination team at UCSC’s Center for Biomolecular Science and Engineering, the scale of the project presented many challenges. To start with, they had to coordinate a small army of researchers who were producing data in labs around the world. “We had five data wranglers who traveled around to the labs, probably four conference calls a week at the height of it, plus large group meetings twice a year, and countless emails and Skype calls,” Kent said.”

“The management of data and the processes / QA around it are the biggest problems out there and are tough to follow. Most people struggle massively just with managing all the data, let along keeping it up to date, etc.,” said Sultan M. Meghji, a VP with Appistry, a company that manages genetic data.

The project doesn’t just offer ways to think about and organize the hunt for and use of gigantic data sets. Researchers also developed software tools to analyze their results. These include new databases for tracking specifics associating with genetic analysis such as HaploReg or RegulomeDB. There’s also a pre-configured virtual machine available designed to host and analyze data generated by the ENCODE project. And of course, the data is open for researchers and the project’s participants are actively encouraging interested parties to learn how to use the data and contribute via a portal.

So while this is a big data story, a cloud computing story and a science story, it’s also a peek at the future of collaboration and management challenges driven by our connected world. And if that’s not enough to get you excited about the project, let’s return to the science and discoveries about the secrets locked inside the genome.

“The quality and scientific depth of the data is the bigger import. As we move primarily into clinical operations, this kind of data, if it is of high quality and repeatable from a process perspective is massively useful,” Meghji said.

This article on the genetic link that could help doctors predict what antidepressants are most likely to help cure a person with depression — doing away with the trial and error approach in place today — shows what could become the future of medicine. And that’s awesome.

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Kosuri and geneticist George Church, both researchers at Harvard’s Wyss Institute, have successfully encoded Church’s forthcoming book Regenesis: How Synthetic Biology will Reinvent Nature on Ourselves into DNA. They may not sound like a big deal until you realize they have replicated that tome 70 billion times on a single gram of biological material . That amounts to 700 terabytes of data and 100 times the number of books ever printed.

Using life’s building blocks as a storage medium isn’t new, but according to ExtremeTech, Church and Kosuri managed to cram 1000 times more information into their double helices than had previously been achieved. Basically they used the four the nucleobases that define genetic code – adenine (A), cytosine (C), guanine (G) and thymine (T) – as binary markers. A and C become 0, and G and T become 1.

So why use DNA to store digital data? ExtremeTech sums it up best:

It’s incredibly dense (you can store one bit per base, and a base is only a few atoms large); it’s volumetric (beaker) rather than planar (hard disk); and it’s incredibly stable — where other bleeding-edge storage mediums need to be kept in sub-zero vacuums, DNA can survive for hundreds of thousands of years in a box in your garage.

It is only with recent advances in microfluidics and labs-on-a-chip that synthesizing and sequencing DNA has become an everyday task, though. While it took years for the original Human Genome Project to analyze a single human genome (some 3 billion DNA base pairs), modern lab equipment with microfluidic chips can do it in hours. Now this isn’t to say that Church and Kosuri’s DNA storage is fast — but it’s fast enough for very-long-term archival.

Image courtesy of Flickr user dullhunk

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It you’ve used Ancestry.com recently, you’re probably thankful for its efforts. According to Head of Engineering Scott Sorenson, Ancestry.com has more than 10 billion records that are part of a 4-petabyte (or 4-million gigabyte) data store. If you’re searching for “John Smith,” he explained, it probably has about 60 million for “Smith” and about 4 million for “John Smith,” but you’re only interested in the relative handful that are relevant to your John Smith.

Making models smarter

That’s why Ancestry.com is using machine learning to make sorting through those records a lot less like finding a needle in a haystack and a lot more like having that needle — and any others made from the same batch of steel — delivered right to your door. Here’s how the process works, in a nutshell:

1. Crawl digital records (e.g., newspapers, birth records, death records, census data, ship manifests, etc.) online and extract relevant data
2. (Or 1(a)) Scan, upload and index physical records (via a partner in China)
3. Stitch together new records with user data to add more context
4. And this is key, constantly analyze user behavior in order to make its algorithms smarter

As users make judgments about the records they’re presented, Sorenson said, Ancestry.com’s algorithms get better at performing their particular tasks. So, a system for extracting data from newspaper pages might be able to better recognize the various sections of the page (so as to ignore the ads, for example) and then be able to adjust for mistakes in the section it is analyzing. And as with Google’s search algorithms, the more that users interact with records, the better Ancestry.com’s sorting algorithms are able to determine those records relevance to any given user.

Spit in a tube, pay $99, learn your past

Oh, but Ancestry.com has decided that merely storing and analyzing historical records is just the beginning with regard to providing accurate genealogy information. It also will sequence your DNA, focusing on 700,000 markers important to determining one’s race, lineage and other factors. That service, which simply requires users to swab their cheek or spit in a tube and send it to the lab, costs only $99 (a full genome sequence would cost at least 10 times that, by the way), but could revolutionize the accuracy of Ancestry.com’s models.

Right now, Sorenson said, the DNA service can tell users their race and what country they’re from, and also connect them with other relatives who share a DNA profile. (If your privacy red flag has gone up reading this, Sorenson did note the following: all communications with relatives are optional and initially anonymous; all DNA information is disassociated from personal information; and users get their sequence results via an encrypted key “that we treat with a higher level of security than we’d store your credit card information.”)

Connecting with distant relatives can be valuable, though. A third cousin, for example, might have ancestral information that you don’t, which will help make your family tree that much more accurate. But Sorenson said when it really gets interesting is when Ancestry.com can combine DNA data with record data in family trees. Someone’s DNA might indicate he’s from France, Sorenson explained, but cross-checking that against that person’s family data will let the service discover he’s actually from the Normandy region.

Going forward, Sorenson said Ancestry.com expects its DNA service to take off like a rocket. The company is investing between $10 million and $15 million into that service over the next couple years, and has bioinformatic scientists on staff trying to scale algorithms designed to handle hundreds of samples to work with hundreds of thousands or even millions of samples. In that regard, though, Ancestry.com isn’t alone — the steady drop in the price of genome sequencing has everyone in the sector anticipating skyrocketing data volumes.

What’s next: Telling stories and making genealogy real-time

OK, so it has billions of records and our DNA, what more can Ancestry.com possibly want or need to provide us information on our ancestors? Nothing, actually.

It just needs to make better use of what it does have and the new technologies available for working with that information. Genealogy has traditionally been “dusty,” Sorenson explained, but Ancestry.com is trying to tell the stories behind those dusty records. If you’ve seen the NBC program “Who Do You Think You Are?”, on which Ancestry.com traces celebrities’ ancestral roots, you have an idea of what Sorenson is talking about.

For example, by improving its image-processing capabilities, Ancestry.com could extract more information than just name, data and location from old records that it already knows how to process. It could tell someone that his grandfather was the only person on the block to own a radio, or whether he owned his home. Combined with socioeconomic and other external data, Sorenson said, Ancestry.com could “create a really vivid picture” of what it was like to live during a specific time.

By using location data from cell phones, Sorenson said Ancestry.com could deliver a mobile experience that’s far more than a translation of the web on a smaller screen by making genealogy a geospatial pursuit. For example, Sorenson, explained, if a user takes a picture of a gravestone, Ancestry.com would like to provide him with relevant historical data related to that place, and maybe even some nearby points of interest.

Some might think Ancestry.com’s practices and plans toe the privacy line, but if someone has to toe that line, this might be the company to do it. In a fast-paced world it’s easy to get tied up in the moment and in our own little worlds — especially with big data being used elsewhere on the web to keep our attention firmly on one site or another. Using personal data to let users dig into decades into their family histories ends up looking very refreshing.

Feature image courtesy of Shutterstock user tovovan.

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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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Two weeks ago, a small green box showed up in my mail. Inside was a “spit kit” my wife had ordered me from DNA sequencing startup 23andme. Within a few minutes, I’d completed and returned the sample. In a few weeks, I’ll be able to analyze my DNA online. What if I find something I don’t like?

Thanks to technology, such diagnostics are now within the reach of consumers. As more people test themselves, doctors and insurers may face the additional burden of just-in-case surgery and a “previvor” mentality. So, will technology cure health care, or kill it?

Normally, doctors prescribe tests when patients report symptoms. Occasionally, the U.S. Preventative Services Task Force decides to recommend blanket testing, particularly for diseases that are hard to detect until they’re fairly advanced. Sometimes the task force actually recommends against testing, partly because treating the condition is unlikely to prolong life. But increasingly, we can ignore their advice and just test ourselves.

The cost of diagnostics is dropping fast, particularly for DNA. 23andme’s service recently fell from $999 to $399. “We always knew the technology cost would drop,” said Linda Avey, the company’s co-founder. “The decline in genotyping costs surpasses that of Moore’s Law.”

Testing can get as low as $60, as Familybuilder recently showed. Founded in 2007, the company received a $1.5M Series A funding from DN Capital in February 2008. While the company only analyzes enough DNA to trace genealogy, it stores the raw samples for two years, so CEO Ilya Nikolayev hasn’t ruled out the possibility of selling additional analysis to customers in future.

By slashing prices, DNA-testing companies hope to build big databases of customers’ DNA sequences. For Familybuilder, this means more chances to find its clients’ relatives. For 23andme, it means better research. “It’s about getting statistical power,” said Avey. “As we get enough people with a certain phenotype, we can get them to enter data on when they contracted the disease and what drugs they’re on, and we can do genome-wide studies.”

But will widespread diagnostics increase the burden on healthcare? Somewhere between 10 and 50 percent of autopsies reveal diseases other than the one that killed the patient. If consumers test themselves, then tell their doctors, the medical system could wind up treating 50 percent more diseases than it does today — even those that wouldn’t have killed the patient.

Avey believes some patients will want to get tested for everything, but says she hopes that their doctor will be there to talk to them about it. “The data isn’t that definitive. Your risk [of having a disease] might be 12 percent, someone else’s 8 percent,” she points out. Understanding these finer points of diagnostic analysis is something medical professionals are trained to do, and a skill most of us lack.

DNA testing has also raised concerns that insurers or employers might use a person’s knowledge of genetic conditions against them, but recent legislation makes this illegal. According to Avey, the Genetic Information Nondiscrimination Act, passed in May, says that if you know about your genetics, your insurer and employer can’t discriminate based on that knowledge. The FDA is watching consumer diagnostics closely: It recently sent a warning to Laboratory Corporation of America that one of its customers, Ovasure, is illegally marketing a DNA test for ovarian cancer without the administration’s approval.

Anne Wojcicki, co-founder of 23andme and wife of Google co-founder Sergey Brin, says she thinks widespread DNA research will help, not harm, the medical system by encouraging prevention. “17 percent of [U.S.] GDP goes to health care but we need a radical change,” she said. “We are a country that has really been focused on reactive care; now, we’re at an inflection point where we’re transitioning to a preventative model.” She points out that DNA research can also improve treatment by showing which medication will work best for a particular genotype.

Advances in technology may well strain the healthcare system. But in the end, they may also be our best hope for fixing it.

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