Relationships for Innovation

This week we announced new agreements with two premier healthcare institutions: Geisinger Health Systems (GHS) and the University of California, San Francisco (UCSF). We also announced, with Complete Genomics, our participation in its Genomics Discovery Partners program.

Each of these relationships opens exciting new opportunities. NGS technology generates terabytes of data requiring enormous storage capacities and supercomputing processing power to extract meaningful information. Academic research centers, university hospitals, and commercial organizations risk being overwhelmed by this rapidly growing amount of data. As researchers and clinicians seek to integrate these datasets into their work, industry leaders are increasingly investing to meet this data management analysis challenge.

We are excited to enable these industry leaders to innovate solutions with us on our cloud-based platform. GHS is integrating its genomic data assets with clinical applications, including parent-child trio studies for disease characterization and prevention. UCSF is uploading, managing, and analyzing sequencing data for large-scale genome sequencing research applications. Complete Genomics is offering our data management and visualization services to customers of their human genome sequencing service.

These relationships exemplify ways we help organizations to capitalize on opportunities created by the growing ubiquity of low-cost genomics data. Together we are applying these experiences to create new services and capabilities that support their customers, researchers, and clinicians.

Check back often for updates on these and other collaborations in the works.

Load Up on Caffeine … AGBT Is Almost Here

View from the Marcos Island Marriott, the AGBT venue

We’re gearing up for the Super Bowl of the next-gen sequencing field – the Advances in Genome Biology and Technology (AGBT) meeting held annually in Marco Island, Fla. In a typical year, there would be major announcements from the established sequencing vendors at this event, but given that Life Technologies and Illumina already went public with their big news at JP Morgan, and the Roche bid for Illumina will likely still be playing out, the big stories from this year’s meeting will probably revolve around major research findings, technology applications, and what’s going on with the sequencing upstarts. (Oxford Nanopore, for example, will be announcing plans to commercialize its instrument later this year and providing attendees a sneak peek. GnuBio will also be presenting on its desktop sequencer, the iGnuIT 1000.)

As usual, this year’s agenda is chock full of thought-provoking presentations, including a talk by DNAnexus co-founder Arend Sidow, who will be presenting on the use of deep whole-genome sequencing to monitor breast cancer progression (Thursday, Feb. 16, at 4:35pm).

We’ll be there to meet with colleagues, customers, and potential collaborators. We’ll also be presenting two posters on current DNAnexus projects. If you’ll be there, we encourage you to stop by — find out more about us, get a demo, have some wine and cheese, you name it. Here’s a quick preview of what we’ll be showcasing:

  • Candidate Gene Variants in “Micronesian” Autosomal Recessive Aplastic Anemia – Brigitte Ganter, Majed Dasouki, S. Abhyankar, M. Furness, R. Calado
    This work was done with collaborators at the University of Kansas Medical Center and National Heart, Lung, and Blood Institute (NHLBI). In the project, researchers performed exome sequence and nucleotide-level variation analyses for two siblings with aplastic anemia, a condition where bone marrow does not produce sufficient new cells to replenish blood cells. The results led to the identification of 12 candidate homozygous variants in 9 different genes. In this poster, we’ll discuss how DNAnexus was used to identify these variants and characterize their potential role in aplastic anemia.
  • Expanding and Enhancing Access to the Sequence Read Archive (SRA) Through a Complementary New Web-Based Mirror – Brigitte Ganter, Evan Worley, Bing Xia, Andreas Sundquist
    As we announced last October, we teamed up with Google to develop a complementary hosted mirror of NCBI’s Sequence Read Archive (SRA). Through a typical user scenario, we will discuss the underlying data processing pipeline, key features of the new web-based interface and how researchers can use it to quickly identify and browse datasets of interest, link-out to PubMed references, and integrate data into follow-on analysis workflows.

Sequence Data: The View from JP Morgan

Last week, Andrew Lee, Vice President of Strategic Operations, and I attended the JP Morgan Healthcare Conference, an annual investor conference here in San Francisco that brings some 25,000 people to the city. The big news this year came from Life Technologies and Illumina, which both announced platforms that will be capable of sequencing an entire human genome in a day. Life Tech in particular noted that its same-day genome sequence will cost $1,000 in reagents — effectively putting an end to a race that began 10 years ago, when scientists first started seriously competing to achieve the $1,000 genome.

With this price point achieved, we expect people to sequence genomes at a much faster clip than ever before. Indeed, a survey from GenomeWeb and Mizuho Securities found that scientists anticipate that sequencing data will increase 32 percent this year over 2011, and increase another 38 percent next year compared to this year. That’s exciting on the data analysis front: As the volume of DNA data grows exponentially, it’s even more important to have a scalable platform to manage, store, and analyze that data securely and efficiently. The GenomeWeb/Mizuho survey also found that people expect to spend more on informatics in 2012 than they did in 2011.

After all, mainstream genome sequencing won’t be possible until the analysis costs come down by orders of magnitude. Even with so much attention on cutting the price tag for sequencing technologies, that didn’t translate to matching improvements for data costs. The race to the $1,000 genome may be over, but as it turns out, that was just the first leg of a relay. Now the baton has been passed to the data management and analysis folks, and it’s our turn to run as fast as we can.