Beijing Genomics Institute alone has purchased 128 of these instruments. The Broad has 51. And based on Illumina’s 2010 Q1 10-Q filing, they’ve got a backlog that represents maybe another 200 machines. So by 2011, there may be some 500 of these machines running. Not to mention the GA-IIs, the SOLiD machines, the 454 machines, Helicos, Pac Bio, Ion Torrent, Complete Genomics, and all of the next-next generation single  molecule sequencing companies making big promises.

The Fact Is…

…it’s easy to lose track of what this means. It’s easy to get stuck in today’s problems.

In 2010, we may have something like 1,000 publicly available human genomes at a wide variety of coverage. That’s giving us as a society the benefit of the doubt.

In 2011, the worldwide capacity for whole human genome sequencing will easily reach 50,000 – real data based on orders that have already been placed.

Do we believe this is going to slow down? What incentives does the industry have to dial this down? None that I can think of.

If it’s 50,000 genomes in 2011 (50x increase from 2010), it’s totally reasonable to believe that capacity will grow to 250,000 genomes by 2012 – that’s only a 5x increase from the previous year. Call 2013 a 4x increase over 2012 – that’s a capacity to sequence 1 million genomes, just three years from now.

The only thing in the way of this explosive growth is our ability to absorb the new capacity – and that gets directly to tools that can analyze the data. As the number of genomes increases exponentially, the types of questions we’ll ask of this data will change dramatically. We’re in the middle of an incredible revolution that will move more quickly than many of us appreciate. Let me propose one vision.

2001-2009: A Human Genome

The 10 or so years after the Human Genome Project, through say 2009, were characterized by large-scale research operations to understand the basic biology behind genomics. Gene and target discovery, pathway modeling, disease models, GWAS, expression analysis. Consumers of the Human Genome Project have been academic, pharmaceutical, and biotech researchers. The genome was sequenced, and sequencing was thought to be yesterday’s job.

2010: 1,000 Genomes – Learning the Ropes

In 2010 with the nascent adoption of NGS (if you think it’s widespread today, just wait), new applications have exploded on to the scene: larger-scale resequencing of exomes and whole genomes, RNA sequencing, CHiP-seq, metagenomic sequencing, and a renaissance in the agricultural sciences who can finally run their own versions of the Human Genome Project. The consumers of this early-stage adoption of NGS remain the academic researchers, pharma and biotech researchers, and ag companies. We’re finding new variation across different ethnicities, identifying novel transcripts in previously well-understood genes, and developing exciting new insights in epigenetics. But it’s still basic research. And the bioinformatics community is still arguing about basic approaches to alignments, calling variants, and normalizing across experiments.

2011: 50,000 Genomes – Clinical Flirtation

How do things change when we have the capacity to sequence and analyze 50,000 genomes? Catalogues of human variation will become large-scale for the first time. We’ll build strong correlations between phenotype, genotype, and treatments. Early-stage sequence-based diagnostics will find their way into the leading-edge labs and hospitals. Pharma will take real steps towards the design and optimization of genotype-centric clinical trials. The FDA will provide better guidances towards developing drugs and diagnostics that employ sequencing. We’ll start talking about “Genomicists” in the same way we currently describe Pathologists or Radiologists although there will be very few of them. (Indeed, some Pathologists already believe that genomics will fall in their house.)

2012: 250,000 Genomes – Clinical Early Adoption

With 250,000 genomes, the clinical adoption of sequence data will begin in earnest. Genomics-based diagnostics will be a real business: comments from a recent J.P. Morgan report indicate that lab managers believe that this switch will occur in the next 5 years, particularly in cancer detection and classification. The FDA will support pharmacogenomics-based clinical trials at large. Population studies will continue to drive massive insights into human variation. Leading-edge hospitals will store whole genome data for patients as a part of their medical records. The consumers of NGS are changing from academic and commercial researchers to Pathologists, Genomicists, VPs of Clinical Development in pharma, and young doctors everywhere.

2013: 1 Million Genomes – Consumer Awareness

When the planet has the capacity to sequence 1 million genomes per year, many 1st-world health-care consumers will have enough knowledge to seek out health-care providers who provide these services. Savvy patients, already practiced in researching their own conditions on the Internet prior to a doctor’s visit, will begin to push back on doctors’ recommendations, saying, “before we make a decision on that cancer treatment, I want my genome sequenced to see whether it’ll be effective.” Health and life insurance companies will get into the game, and barring significant ethical battles, will use genomic information to guide treatments, suggest specialists, and even set prices for premiums. Diagnostics for personalized care will double from the previous year. The personal genome will be within reach to many individuals, and the FDA will struggle to keep up with regulation to restrict the use of personal genomes from unapproved diagnostics. It is not at all clear to this author whether the FDA is sufficiently staffed to keep pace with the innovation that will explode from this level of availability of sequencing capacity.

2014: 5 Million Genomes – Consumer Reality

Many cancers in the 1st-world will be sequenced as a regular component of a biopsy. Patterns of drug efficacy will be published and made available against different genotypes. Oncologists will work with statisticians to develop treatment programs. Hospitals will offer whole genome sequencing services to newborns. Chronic pain will be managed on a genotype-by-genotype basis. Medical schools will redesign their curricula to produce physicians and researchers to lead medicine into the Genomics Age and to provide advanced training for the Genomicist specialty.

2015-2020: 25 Million Genomes And Beyond – A Brave New World

The ability to sequence 25 million genomes just five years from now seems well within the industry’s grasp, barring significant issues of uptake and absorption of the data. And applying just a doubling of capacity each year between 2015 and 2020, we would have the capacity to sequence just under 1 billion genomes a year by 2020. This will have drastic impacts on society.

While the health-care industry will continue to adopt sequencing for broader and broader applications, the insurers will do everything in their power to get access to this information both for the microeconomic management of individuals as well as for the macroeconomic indicators of ethnic and regional health that will surely increase their profit margins.

Consumer applications for genomics will flower: want to see whether you are genetically compatible with your new girlfriend? There’s an app for that. DNA sequencing on your iPhone? Believe it. Personalized genomic massage, anyone? This is already happening today – see labs testing for allele 334 of the AVPR1a gene to see whether your new mate has the “cheating gene.” Then imagine the market for consumer applications and gimmicks when your entire genome is already on a USB drive.

Genetic discrimination may need to be addressed in the highest regulatory bodies: do you really want to elect a President whose genome suggests cardiomyopathy? Think this won’t happen? Just imagine the first candidate to release his healthy genome just like his last two years of tax returns, challenging his opponents to do the same. What will the world’s reaction be?

Will LinkedIn and Facebook suggest people you may be related to? Sure, they’ll probably not have your genome, but your genome will be somewhere in a de-identified way, sitting right next to other de-identified genomes. It’s easy to envision software to mine this data that will find your relatives and common ancestors. It may start as a medical application but it won’t be able to stay that way. Just let that software platform tell you that they’ve found a genome of someone who looks like a third cousin and provide a way to reach out to them anonymously. Welcome to ChromosomallyLinkedIn.

Back to Reality

I’m no futurist – most weeks I can barely tell you what my schedule is the following week. So while it’s fun to dream up the next decade, there are too many variables to get it all right and this thought experiment may be off a few years in any direction. We’re squarely in 2010, the year of the 1,000 genomes. The deeper we allow ourselves to look into the future, the less clear it becomes.

But one thing is certain – sequencing capacity world-wide will continue to grow exponentially for at least the next 10 years. This is going to happen. That means sample preparation will get vastly easier, throughput will continue to increase at a dizzying rate, sequencing costs will plummet, and the applications of sequencing will become more mass-market.

And most of all, it means that the software that we use to analyze sequence will need to become a lot simpler to use, and more purpose-built for specific applications. General bioinformatics frameworks are dinosaurs awaiting the impact of the meteor. In the (near) future, no one will be arguing about gapped vs. ungapped alignments. No one will be talking about Phred-like quality scores. No one will be talking about reads, even – they’ll seem like antiquated tiny puzzle pieces from a past when sequencing technology was like a nuclear bomb rather than a precision scalpel.

As I look ahead to develop the long-term vision for the product roadmap for GenomeQuest it’s obvious to me that our immediate-term focus must be on simple, easy-to-use, whole and multi-genome analysis. With the coming of 50,000 genomes next year, our immediate problem is supporting the absorption of this new knowledge. That means continuing to enable the processing of data as quickly as it comes off the sequencers and presenting it to end users in a way they can understand, interact with, and discover. What are all of the proteins affected by this individuals variants and what are the types of modifications we see? How does that impact disease pathways? How is this individual similar to others for whom we have treatment/outcome data?

Today’s consumer of genome sequencing is the researcher or clinician doing basic discovery with thousands or hundreds of thousands of genomes. But the longer-term audience is the clinic itself.

And I for one don’t think we have that long to wait.

Calling all clinicians.

Rants welcomed.
-Richard

Some excerpts:

… the science of biology is being transformed.

… the genomic revolution depends on two technological changes. One, in computing power, is generic—though computer-makers are slavering at the amount of data that biology 2.0 will need to process, and the amount of kit that will be needed to do the processing. This torrent of data, however, is the result of the second technological change that is driving genomics, in the power of DNA sequencing.

… the goal of the new biology is to tie these things together reliably and to understand how the phenotype emerges from the genotype.

… that will lead to better medical diagnosis and treatment. It will result in the ability to manipulate animals, plants, fungi and bacteria to human ends.

According to Bob, it was the 2nd largest crowd ever for his seminars and the largest ever for a software topic — an indicator that reseachers are indeed planning for NGS and eager for answers to their “information bottleneck”.

Over 80 principal investigators, Post Docs, and MDs attended from Harvard hospitals including Beth Israel Deaconess, Children’s Hospital, Dana Farber, Brigham and Womens, Mass General, as well as Harvard Medical School.

Based on the questions, most researchers were interested in the RNA-Seq and Variant Detection applications. Richard Resnick stressed GQ’s cloud-based storage/analysis of results, the GQ Browser for whole-genome analysis, as well as cloud-sharing of results.

Harvard bioinformaticians and computational biologists are welcome to a follow-up seminar or training on the GQ API.

I think the problem is one of expectations. Understanding the genome won’t cure any disease, but it can help determine dosing levels for prescription drugs. Big payer companies like Medco are on record that pharmacogenomics reduces hospitalization rates.

I think people also underestimate the complexity of the genome and the technology needed, on top of the regulations and the need for training more doctors. I love the passage in Thomas Goetz book The Decision Tree on how to apply the genome to enable personalized medicine. There he points out that (I’m paraphrasing ) “only the comparison of variation of 10, 100, 1000 and more genomes holds the promise of personalized medicine—hypothesize the association of a set of variations to a disease, and let the statistics tell the story. The more genomes, the better the statistics.”

Doesn’t it follow that before you can attack this problem, you need many genomes? How could we get where we are without the investments in Genomics? Now the 1000 Genomes data are being published and we are working on it! As the data flows forth, we should be able to deliver personalized medicine research leading to (hopefully) more efficient healthcare delivery.

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.

As with the Internet, it was many people who “invented” it, but, in the case of genomics, it’s clearly been the NIH that’s provided inspirational, institutional, and financial stewardship.

Which leaves us in life science with a fundamental responsibility – to leverage these investments throughout the industry.

An opportunity here is the 1000 Genomes Project — the first project to sequence the genomes of a large number of people and provide a comprehensive, free resource on human genetic variation. It’s an amazing, emerging resource and, in my opinion, the life science industry should enable every research and clinical health organization to fully leverage this $50M investment. It’s also a leading indicator of more studies already underway and surely more to come.

At GenomeQuest, we’ve put much thought and research into this and, last week, announced GQ-PMR (personalized medicine research). You can find out more about it here. For this post, I’ll just say that we hope to help activate this public data for day-to-day use in pharma research and accelerate their transition to personalized medicine. And all done so standing on the shoulders of the NIH.

NPR: Gene Ruling Could Have Wide Implications

60 Minutes: Gene Patents

The industry blog Genetic Future has sponsored an amazing commentary from the legal and scientific community: “Jaw-dropping” verdict against Myriad in BRCA patent case.

What’s the real issue? The Myriad patent contains broad claims for not just the  test for breast cancer/ovarian cancer, but all other potential tests associated with the BRCA gene on the grounds that the discovery and ability to express the gene in a diagnostic is a patentable invention. The effect is blocking other commercial testing labs from offering a competing (and possibly more cost effective) breast cancer screen. (Note: Myriad test costs $3,200 for one gene. An entire genome scan will soon be cheaper.)

Proponents of gene patenting argue that without patent protection investors will have no incentive to invest in new diagnostics research, and innovation will suffer. Opponents of gene patenting argue that the BRCA patent is blocking research into the role of BRCA in other diseases, and making breast cancer screens inaccessible to a broad market.

So, what is the future of gene patenting?

The days of broad claims on single genes are probably over, though by the time a court makes a final ruling most of them will have expired. Commercial enterprises will keep the status quo.  Freedom-to-operate and patent-ability checks will continue. No one know how the courts will rule.

I also expect that the use of engineered sequences for drugs (for example molecular antibodies), treatments, and testing for disease susceptibility using complexes of interacting genes will continue to thrive and be patentable research.

Notably, the BRCA sequences are readily available in GenBank. Out of curiosity, I wondered who had dared to create IP using the human BRCA genes. If your curious, see the report generated from GenomeQuest here.

Pretty simple, right?

Well, the main challenge is that personalized medicine (PM) is all about comparing you to the history of human results. That’s right, molecular biologists know much about the concepts of DNA/RNA/proteins but they are now in deep learning mode about what causes what at a molecular level. And they are learning mostly by observation — that is, what happens to a person of type X when we do Y.

So the world of health care is collectively building a PM knowledge base and we wish for doctors to act upon it.

And while today’s knowledge base is small, if people are willing to contribute, it could swell to something very meaningful in but a few years. It’s a massively exciting time for medicine (what with the cost of genome sequencing crashing by 5X every year and cloud computing enabling global sharing of all this).

So a major impediment to this nirvana is that folks today are reluctant to share: “but what if something worse is exposed”. With worries that “existing conditions” (EC) will result in a lifelong insurance ban, it’s a reasonable objection.

Well, with a nation-wide heath care plan with EC worries removed, people will be far more open to sharing their data — which will materially accelerate our inexorable move to PM and its considerable social and economic rewards.

I offer this perspective as a (unintended?) consequence of our national health care program and progress that we all can applaud.

A recent commentary in GenomeWeb “Considering a Cloud? Cost isn’t everything…” citing the paper “The Real Cost of a CPU Hour” illustrates the confusion. The paper benchmarks HPC on a dedicated cluster versus bare metal resources on Amazon EC2. The conclusion is foregone since commodity EC2 network can’t keep up with a tuned applications running on a compute cluster with a high-speed network.

A more informative title for the blogger and the article would be: “Considering Infrastructure-As-A-Service? Beware if your application requires Message Passing Interface and therefore High-Speed Network”.

Fortunately for large scale Sequence Data Management the predominant application mode is “embarrassingly parallel” and therefore HPC are not needed, except maybe on the Web Server where response time is critical. The statistics in the article show that  Embarrassingly Parallel applications run acceptably well on Amazon EC2, adding less than 5% overhead to the computations.