Despite the whole “knowledge is power” argument – it could help with early diagnosis and prevention or lead a doctor to better treatment options for an existing condition – sequencing skeptics raise valid concerns and questions when it comes to gene sequencing for healthy people. How precise is it? How well will consumers be able to interpret the results? Will it just lead to needless hand-wringing about conditions that people won’t be able to do to much to address or that won’t surface until much later in life?

For now, those are questions for people with only the deepest pockets. But it won’t be long before the conversation becomes more relevant for more of us and, in the Wall Street Journal this week, two doctors weigh in with the pros and cons of the debate over whether healthy people should have their genomes sequenced.

Dr. Atul J. Butte, division chief and associate professor at the Stanford University School of Medicine and director of the Center for Pediatric Bioinformatics at Lucile Packard Children’s Hospital in Palo Alto, Calif., takes the pro position. And Dr. Robert Green, a medical geneticist at Brigham and Women’s Hospital and Harvard Medical School in Boston, argues against it.

Even though Dr. Butte acknowledges that gene sequencing isn’t perfect, he believes the positives outweigh the negatives. He says:

• Identifying DNA variants that are early indicators of disease can lead to early diagnoses and preventative strategies.
• Couples planning families can learn whether they carry genetic risks for serious disorders.
• Doctors can better figure out the most effective drugs for a patient or what to avoid
• It can help in the diagnosis of illnesses that haven’t yet been identified.

On the flip side, Dr. Green believes that while affordable genomic analysis opens the door to personalized disease prevention and treatment options, there are still roadblocks. For example:

• Medically dangerous gene mutations are rare in healthy individuals but it would still be very expensive to find them – less than 2 percent of healthy people have a dangerous DNA mutation that would spur a doctor to monitoring or treatment. Assuming sequencing costs $5,000 now, it could cost $250,000 to find one person with a mutation.
• Known mutations may or may not carry the same risk without a family history, so sequencing alone can’t always lead to action.
• Geneticists don’t always agree on whether gene mutations are dangerous.

When it comes to health, I tend to fall on the side of information – the more of it we have, the better off we are. And the rise of consumer-ready medical technology that gives us clearer windows into our bodies – from Fitbits (see disclosure) to the AliveCor iPhone-compatible heart monitor – is setting the stage for an era in which people are armed with even more data about their health. 23andme doesn’t do full gene sequencing but its genotyping services already let people explore their DNA for just $99.

But as we move into this new bioinformation-filled future, it’s important to keep the skeptic’s voices in mind because gene sequencing doesn’t just have personal implications but public health consequences. One of Dr. Green’s most haunting concerns is the rise of “patients in waiting” who spend their lives in anxiety, undergoing unnecessary tests and potentially doing themselves more harm than good. But as others have noted, sequencing could take its toll on the health care system with unessential screenings and procedure, tax the patient-doctor relationship and lead to other biotethical questions.

Disclosure: True Ventures is an investor in Fitbit and the parent company of this blog, Giga Omni Media. Om Malik, founder of Giga Omni Media, is also a venture partner at True.

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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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VC investments decreased substantially in the areas of clean technology and life sciences, which was balanced by significant increases in the software industry. The investments trended away from seed investments and toward early-stage (as in, the period in a startup’s lifespan beyond seed stage.)

The information is provided from the MoneyTree Report by PricewaterhouseCoopers LLP and the National Venture Capital Association (NVCA), based on data from Thomson Reuters. Here are some notable facts from the report, released Friday:

• In Q4, VCs invested $6.4 billion into 968 companies, a 3 percent decrease in dollars but 5 percent increase in number of deals over Q3.
  
• 38 percent fewer seed stage companies received VC dollars in 2012 than 2011.
• Software made up the largest investment sector of the year, seeing a 10 percent increase in dollars and 8 percent increase in deals over 2011.
• First-time financings in biotechnology and medical devices saw some of the biggest decreases in funding, with the lowest number of deals since 1995.
• Clean tech saw serious drops as well, with a 28 percent decrease in dollars and a 23 percent decrease in deals. The sector saw only $3.3 billion in funding, as compared to $4.6 billion in 2011.
• Internet companies saw a 5 percent decrease in dollars and deals this year, but still had the second-best year for internet deals since 2001. They made up about a quarter of all VC deals this year.

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Have access, will innovate

Wired published an interesting profile Tuesday morning that exemplifies what’s possible when smart people have access to good data. The piece showcases the work of a man named Fred Trotter who has accessed reams of buried Medicare data via a Freedom of Information Act request and is uncovering some potentially valuable information. Already, the article explains, he has built a “Doctor Social Graph” by analyzing some “60 million relationships between doctors, and how often they refer patients to one another.” His next mission is to build a doctor rating system based on data he’s uncovered about credentials, nursing home inspections and other relevant info.

Elsewhere, companies such as Palo Alto, Calif., startup Apixio are trying to make hospitals more efficient by using semantic analysis to connect the dots between patient charts, electronic medical records, billing data and whatever other sources of information that hospitals generate. (We covered Apixio in early 2011, although the company has significantly expanded its services since then.) In health care, everyone seems to have their own way of doing things, as Apixio natural-language-processing scientist Vishnu Vyas told me recently, so “the variety of the data becomes as important as the volume of the data.”

Linda Drumright, GM of the Clinical Trial Optimization Solutions group at IMS Health, agreed. She explained that her company is able to do its job because it has access to mountains of data from pharmacies, insurance claims, medical records, partners and other sources. All told, it houses 17 petabytes of data spread across 5,000 databases. Her division’s clients, which generally include pharmaceutical and biotech companies running patient trials, need all this data in order to ensure their trials will actually be successful.

One recent customer wasn’t able to recruit test subjects fast enough, she noted, and IMS helped it comb through its criteria about who to or not to include in the trial only to find “that the patient population they were looking for didn’t exist.” As IMS went back and began eliminating criteria and iterating design, it realized that trial never should have begun in the first place.

There are a million ways to think about how to use this data, Drumright said, and as more customers begin to fully understand what they can do with it, her goal is to “make this information accessible in a way where it’s easy at the point where it’s needed, and consumable where it’s needed.”

The key to curing cancer might be more data

But whatever Trotter, Apixio, IMS and others accomplish will have been made possible because they have access to some valuable datasets, albeit not always with great ease. Many individuals who’d like to improve the health care system — if not our health, generally — aren’t so lucky. Take, for example, the world’s genetic researchers. It’s very possible the data they need to discover the medical Holy Grail of a cure for cancer is locked in gene sequence data that only very few people will ever see.

David Haussler

According to University of California, Santa Cruz researcher David Haussler, the limited access that many geneticists and computer scientists like himself have to valuable genetic data is “a crime.”

“We are on the brink of a real new understanding of cancer by being able to sequence cancer genomes,” he told me during a recent interview, but big data will be the key to unlocking it.

There are 1.6 million cases of cancer in the United States every year, Haussler explained, and most of the information from those tumors is being ignored. This is partially because of privacy restrictions about who can access personal medical data and for what purposes, and partially because there isn’t yet a concerted effort to collect the necessary genetic samples. As genome sequencing gets faster and cheaper, he says researchers need access to healthy and cancerous samples from the same person — and as many of these samples as possible — in order to analyze the “astounding” number of molecular changes that occur in every type and variation of cancer.

“We can’t completely understand what we’ll find, but we know we the only way we’ll pull out signal from the noise is to [analyze all these genes],” Haussler said.

Haussler understands the need for privacy regulations, but thinks there’s an opportunity to at least ease some current restrictions on how researchers access data. Even when there are relatively large (if not ideal) datasets available such as with the Cancer Genome Atlas project, researchers must apply to the National Institutes of Health for access, and the data must always remain behind an organizational firewall. Every cancer patient in the country could agree to having their data available to researchers, he said, but as long as that data isn’t accessible over the internet it’s only of limited utility.

He — along with others in the field — thinks cloud computing could be the solution because it gives genetic researchers a central location where they can access and perform computations on the data. Haussler and his team that house the Cancer Genome Atlas and a couple other projects currently have more than 400 terabytes of data and expect to have around 5 petabytes of data eventually. Downloading that is infeasible save for access to high-speed research networks, so “we need a place where people can experiment with these big data problems,” Haussler said.

In the meantime, Haussler and his peers will keep on collecting and accessing genome data however they can. And they’ll keep building software packages and algorithms that analyze that data better and faster than ever before. However, he lamented, “If we had the big data out there in an unrestricted setting, then all the best minds in the world would already be crunching on it.”

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Innovation blowing past Moore’s Law

In a recent interview, DNAnexus Co-Founder and CEO Andreas Sundquist explained the opportunity he sees for his company’s services, which include cloud-based storage and processing of DNA-sequencing data. The problem and the opportunity genomics researchers face is that innovations in the field are “outpacing Moore’s Law,” he said, which has resulted in the cost of a DNA profile being pretty much on par with that of any other standard medical test. Soon, everyone will have DNA profiles as part of their medical records.

This “will change the way medicine is done” and could grow into a hundred-billion-dollar market, he explained, but it also will result in lots of data generation: hundreds of gigabytes per person. Whereas the high-end research facilities might have access to high-performance computing and storage necessary to perform DNA sequencing, the hospitals that will now be doing those analyses on a regular basis certainly will not.

And that’s where Sundquist thinks a service such as DNAnexus becomes indispensable. Leveraging the storage capacity and processing power of Amazon Web Services and Google Cloud Storage, his company provides sequencing facilities and researchers with the infrastructure and the software to run the analyses and display the results. Because of its investment relationship with Google and the sheer scale of its operations on AWS, Sundquist said DNAnexus actually works very closely with both companies.

However, unlike some areas where analytics are primarily focused on algorithms because access to storage is just a matter of buying more commodity gear, capacity is still a big issue in genomics. By using the cloud, Sundquist said DNAnexus lets customers share and collaborate on data without actually transferring hundreds to thousands of gigabytes.

It also helps with DNAnexus’ latest undertaking: hosting the Short/Sequence Read Archive. The comprehensive set of sequencing data was hosted by the National Center for Biotechnology Information but was slated for sunsetting because of budget cuts.

That database is currently at about 400TB, but Sundquist says that’s just the tip of the iceberg. He said DNAnexus has actually “blown past” analyzing data at the Hadoop/MapReduce scale and is now focusing on parallelizing computation across 100,000 nodes and scaling its storage infrastructure into the exabyte range.

Genomics aren’t just for humans

Say what you will about Monsanto’s business in genetically engineering food, but it does involve high science on par with DNA sequencing in humans and presents many of the same data problems. That’s why Monsanto deployed Cloudant’s BigCouch database as the focal point of its massively distributed genomics system.

According to Mike Miller, co-founder and chief scientist of Boston-based Cloudant, NoSQL offerings such as his company’s CouchDB-based product are actually ideal for the genomics space because they allow for cheap, horizontal scalability and high throughput. Cloudant is particularly well-suited, he explained, because of the incremental MapReduce engine built into BigCouch.

Miller compares it to Google Percolator, the data-processing framework Google recently deployed to replace its legacy MapReduce system. Whereas traditional MapReduce implementations such as that found in Hadoop are designed for batch processing, Percolator and Cloudant’s MapReduce implementation enable near-real-time analysis because they let users process data as it enters the system and update the dataset accordingly.

This is important for Monsanto because BigCouch isn’t just an analytics system, but an operational database serving a wide variety of users. Some users who aren’t data scientists, but consumers of the data, need up-to-date information and must rely on the system to provide it.

Ultimately, Miller paints a picture of DNA sequencing very similar to what DNAnexus’ Sundquist does. Innovation is rampant, but data growth is outpacing the ability to analyze it, making faster, cheaper and more scalable data systems integral to leveling the playing field. If they can help bridge the gap between the data and the algorithms to analyze it, Miller says, “We’re going to see things in the space beyond our wildest dreams.”

Feature image courtesy of Flickr user micahb37.

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