In the past decade, the cost of sequencing an entire human genome has dropped from $1 billion to $10,000. As companies race to crack the $1,000 genome, the contending DNA machines in the marketplace suggest an end is near.
Now, there are 18 next generation sequencing companies who want to read parts of the DNA code — and they want to do it faster. Everyone is performing at their own pace. Six of the companies have sequencing machines that are working and are available. Six startups have shown their machines work and are expected to roll out commercial machines soon. And the other six are still working on demonstrating their technology.
Pacific Biosciences introduced its third generation machine — it breaks up the DNA, compares it to the reference genome, and then pieces it back together like a puzzle. The company sold its machine for $695,000 to 10 customers, including Monsanto, The Broad Institute of MIT and Harvard, and The Genome Center at Washington University. The Pacbio RS instrument will be used to sequence influenza viruses and bacteria. And it can be used to detect structural variations and mutations in cancer.
Complete Genomics is in the game too. However, the company wants to outsource its DNA services, rather than sell their machines. For instance, The Institute of Systems Biology in Seattle placed an order of 100 genomes. And their outsource model seems to be working. At last count, Complete Genomics has sold 500 human genome tests to companies like Pfizer and The Flanders Institute.
Another contending company called Life Technologies, has launched a single molecule sequencing machine that can detect clinically relevant genes. This year, the company plans on upgrading its existing $6,000 DNA machine, the SOLiD 4 system — so it can sequence the entire human genome for $3,000.
Illumina has a good chance at winning. It just sold 128 HiSeq 2000 machines to the Beijing Genomics Institute. The machines can sequence two people’s genome for less than $10,000. Other companies such as Affymetrix, Agilent Technologies, and Helicos BioSciences are working on genetic sequence machines too.
Ion Torrent has a different approach. By mixing Jim Watson with Gordon Moore, the company uses semiconductors to read DNA code. Right now, it would cost $360,000 and 720 hours to sequence a human genome, so it’s behind in the $1,000 genome game. However, its technology could cater to scientists who need smaller genomes read, such as virus and bacteria genomes.
IBM is also playing the genome game. It wants to develop a machine that could read up to 3 billion base pairs of a human genome in a few hours.
Well, don’t they all.
As the competition heats up, each company is starting to create its own identity. Ultimately, researchers will choose the sequencer that best fits their wallet and their purpose.
Single molecule sequencing could reduce the error rates faced by next generation sequencing machines and add to the explosion of data. Just as the light microscope changed the way researchers looked at viruses and germs, the new machines will provide geneticists a new lens to see the human genome.
The companies are getting closer to the $1000 genome, but they aren’t there yet. As I reported before:
“Our DNA machine turns disease into a software problem by changing the speed and cost of data collection,” says Hugh Martin, chairman and CEO of Pacific Biosciences. As DNA sequencing becomes faster and more affordable, it should allow the building of a more complete database of genetic information. “Once we can build that sort of database for the human organism, it helps us much better understand disease, how to diagnose disease, how better to treat disease,” says Richard Wilson, the director of the Genome Sequencing Center at Washington University in St. Louis. With that information, he says, personalized medicine will become commonplace. Visits to the doctor could then produce treatments tailored not just to your lifestyle and family history, but also to your genetic profile.
That is exactly how James Watson, codiscoverer of the structure of DNA, once predicted such data would impact us: “It’s a giant resource that will change mankind, like the printing press.”
