Everyone from Eric Green at the NHGRI to David Dooling at Washington University’s Genome Center to Kevin Davies at Bio-IT World rightly acknowledges that the $1,000 genome is a misnomer if you decide to include the cost of computing, analyzing, storing, and querying the data. And if you don’t – well, you’ll be a touch over budget.

Nevertheless, recent innovations have changed the playing field. Just two years ago the debate was whether MAQ, the fastest algorithm at the time for mapping reads, was accurate enough based on its gapless strategy. Today, there are some electrifying algorithms out there based on the Burrows Wheeler transform that move 10 times the number of reads through the same compute pipelines. And where Burrows Wheeler doesn’t work – long reads like the kinds we’re seeing more and more – there are fantastic new approaches like GASSST (which we use to get a 7x increase in mapping speeds with arbitrarily long reads and arbitrarily large gaps).

I gave a talk at ABRF this year entitled, “The Bioinformatics Bottleneck,” where I challenged an audience full of bioinformaticians that if we keep getting stuck arguing about the fastest algorithm to map reads and whether to store image files, the industry would simply move on without us. (No, Steve Lincoln, I didn’t out-Steve you.) Illumina, Life Tech, Roche, and now PacBio are going to keep making machines. Beijing Genomics is going to keep buying them. In the heat of the moment I think I said something about not only throwing out the image files, but also throwing out the reads themselves – after all it’s the representation of variation, not the reads, that researchers and clinicians will ultimately care about. As late as March 2010 this was met with shock and even disdain; but today great companies like Complete Genomics and important projects like the Personal Genome Project are doing just that – providing us with the variant calls only; not the reads. Unless of course you want them – your choice, by the way, and may your disk drives spin forever.

Now that’s not to say that we don’t need to keep mapping. And so we do. GenomeQuest is on track to have mapped 100 billion reads in 2010 at year end – about 11 million an hour. Of course if our systems were 100% utilized – an impossible dream for any data center – the number would be far higher. And next year we expect it will be five times that at least. Mapping goes on, powered by innovations in software, and innovations in hardware as well. Earlier this year we announced a partnership with SGI that enabled us to build a purpose-designed architecture designed specifically for mapping. By ensuring reference data availability on every compute node, we minimize network traffic; through significant redundancy in both compute and head nodes we can ensure quality of service. By the end of 2011 we expect to be able to map 300 whole human genomes at 30x coverage per month, or 6,000 exomes per month.

The idea that so many genomes can move through a uniform, industry tested workflow is enchanting. And it sets our sights on the next realm of opportunity: analysis of thousands of genomes at the same time. Projects such as the 1,000 Genome Project (actually planning more like 2,000 – and do you really think Durbin and Altschuler will stop there?) are troves of undiscovered knowledge on the basis of disease. Being able to overlay that on top of your own 100 exome project provides critical information on background. So using the same technology, GenomeQuest has an exciting new suite of products for multi-genome analysis that are currently available for early access. As the year rolls on into 2011, these workflows for computing allelic frequency, performing large-scale tumor/normal studies, and asking population-scale questions across thousands of genomes will be further enhanced in close collaboration with our users.

The end game for healthcare is genomic medicine, enabled by the sequencing of individual patients and the large-scale comparison against populations – from phenotype to clinical presentation to genotype to treatment to response. That’s why we’ve partnered with Beth Israel Deaconess Medical Center – to work closely on the development of whole genome reports that can actually be usable by pathologists, like any other lab test. The idea is to map clinical actions to specific variations and present them in a way that a trained genomics physician can ultimately guide the course of treatment. There is a long road ahead and plenty of stakeholders involved, but being in the clinic in 2010 drives our thinking about the research and clinical development applications that exist today in pharma and academia.

Genomics is still a bit of the wild west – in a single day I can have a meeting with a pharma executive, a seminar on an FDA position-paper, a discussion with agbio researchers on the genomics of polyploid organisms, and a deep dive with a team of bioinformatics talking about word-based hashing algorithms. When genomics is in the clinic this’ll have to stop.

But in the meantime…

However, on the other hand, without broad use of new software to analyze this massive volume of data, this group reported an “information bottleneck” for researchers — exacerbated by a culture of free, fragmented software.

So what to do?

I, for one, am quite optimistic on the topic.

I think that NIH and market forces will resoundingly address the challenge.  I heard from Eric Green, director of NHGRI, that his team and the NIH see the bottleneck as a critical issue and they are taking steps to adjust their priorities accordingly.

And, while researchers in the past, may have made significant discoveries with free and varied software, I contend that the following market forces are encouraging the best of them and their organizations to substantially and positively raise their software aspirations:

• the data volumes in whole-genome projects simply demands a new generation of analysis methods, tools, and platforms — and providers are responding.
• more and more research happens in partnership, requiring collaboration software to share data, intermediate findings, and discoveries — again, solutions exist.
• more and more organizations will want to inject public studies directly into their research, remove silos, and create a “continuous/growing/queryable database of variant information” — available today.
• more and more research will be aimed at personalized medicine, diagnostic testing, and clinical applications – areas that call for strict and documented information exchange.
• bioinformatics teams and companies will utilize emerging sequence database systems to arm researchers with powerful new discovery applications. (In a conversation yesterday with George Church, he commented: “how could it be any other way” when I asked if he envisioned the industry moving to such systems.)

All told, motivated by these opportunities, I believe that we are entering an “Information Renaissance” — powered by  sequencing + software technology!

Keep in mind that the answers lie in the sequence data — and researchers will not stop until they get them.  And if high-quality, fair-priced software is required to get at them, so be it. Increasingly, great software is becoming a “must have” with “must have” rewards.

I’ve seen this happen in other professions as they turned to computers. Electrical engineers started with free or home-grown software to mostly capture schematic drawings.  Then chip manufacturing technology exploded and a $5B electronic design automation (EDA) software industry was born.  Soon software was synthesizing whole circuitry from simple specs, placing and routing millions of transistors on chips, simulating whole systems before manufacturing, even telling you how to test the chip after manufacturing – all “must have’s” that have co-powered Moore’s Law for over 20 years.

So, as exciting as sequencing performance has made Life Science research in the past decade, I suggest that a co-powered “Information Renaissance” will take us to new levels of discovery.  And we all very much look forward to it!

Moving data to the cloud remains the biggest obstacle to cloud adoption. The article makes the case for moving the computation to the data instead of vice versa. Presupposing however that people will be willing to move their data at least 1 time. Otherwise, “moving the computation to the data” is an argument for building and maintaining a local compute cluster, nearby the sequencing instrument.

At GQ we realize there is no “one size fits all”. We support a hosted, cloud based solution for customers with limited IT expertise or inclination. Or, for lab operations running multiple sequencing instruments, we can install the cloud locally so you benefit from the bandwidth of the Local Area Network connecting the instrument to computing. Either way, data moves only 1 time and GQ solves for scalability with its algorithms and data model.

In contrast, as the New York Times article Consumers Slow to Embrace the Age of Genomics discusses, consumer genomic information is not actionable.

Early on during our evolution I was influenced by an interview with Meg Whitman, then CEO of eBay who said there were 5 things people want to do on the Internet: Find, Buy, Pay, Share, and be Entertained. For now, Consumer Genomics is an entertainment business.

When will it become more than entertainment? When researchers have created the association databases with enough statistical quality to make the associations meaningful.

Right now, we see a big market for providing Sequence Data Management for discovery and clinical research applications, where the results of searches is actionable, and therefore more valued by our customers. These many hundreds of thousands of researchers will lead us to millions of doctors and in the future, billions of consumers.

Additional blog posts on the NY Time article:

Gene Expression: Personal genomics is dead, long live personal genomics

Genomics Law Report: The New York Times versus Personal Genomics: Much Ado About Not Very Much

In a couple of words, I think people have a right to information about their own genomes. But, privacy is critical and the society has to be careful not to let the “payors” use this information against us. I haven’t delved into the specifics of GINA, but I’m pretty sure this law was enacted to to protect us from just this kind of information misuse.

My good friend, colleague, trained geneticist, amateur photographer and head of scientific applications at GQ Henk Heus (check out his photo site here!) wrote me a note adding these ideas to the the discussion:

Henk says:

“Besides the medical insurance policies, and incomplete data, there are a number of other issues that I can think about. Mostly that it’s difficult to let people understand what a 19% increase of risk really means for them. It’s not black & white, there are always tricky interactions between multiple genes and environment involved. I think we can wait for the day that someone undergoes some radical medical treatment (like removing breasts because of possible increase risk of breast cancer), only to find out that it was not needed (knowledge incomplete, risk factor inaccurate). People will get sued for a lot of money, and regulation may follow.”
“The other thing is: ignorance is bliss. If there is some late-onset disease for which there is no cure, do you really want to know about it? Especially if it runs in the family and you’ve experienced its effects close by. There should be a right not to know, and that can be difficult with your genome sequence out in the open.”

Thank you Henk for these thoughtful remarks.

50 years of computer evolution and molecular biology are on a convergence course to change the course of society. Exciting times we live in, don’t you think?

And another by Boonsri Dickinson at SmartPlanet The amazing race for the cheapest and fastest DNA machine.

Its helpful to think of the cloud as a layered architecture. The VC Bessemer provides a nice definition of this layering.

• Infrastructure-as-a-Service: Web-services applications framework for provisioning computers, networks, and storage resources without regard to the actual hardware topology. Examples of IaaS are Amazon EC2 and SGI

• Platform-as-a-Service:  Web-services application framework for provisioning data, algorithm, and analysis services and developing new applications. Leveraging all the benefits of the IaaS layer, the GenomeQuest Developer API Framework provides services for managing, comparing, and mining sequence databases without regard to the underlying computers and storage.

• Software-as-a-Service: A finished Web-appliction end-to-end solution for a given end-user application. Examples of this in the Life Science space include the Ingenuity IPA product and the GenomeQuest 6.3 SDM platform.

To my knowledge, we have the industry’s only cloud-based “PaaS”. With a few commands from a remote desktop, developers can upload data, apply algorithms on hundreds of cores while accessing terabytes of well managed sequence data. In the future, we intend to leverage the extraordinary economies of scale offered by IaaS providers such as Amazon.

If you’re curious about the API’s, take a look here.

The title of the article on the new $50,000 ION Torrent machine by Kevin Davies at Bio-IT World says it all: “Watson meets Moore” as Ion Torrent Introduces Semiconductor Sequencing