DNA Sequencing Technique Reported to Be Fastest Ever

Stanford Medicine researchers have developed a new ultra-rapid genome sequencing approach that was used to diagnose rare genetic diseases in an average of eight hours. In a paper published yesterday in The New England Journal of Medicine, Euan Ashley and his colleagues describe their mega-sequencing approach which they say redefines “rapid” for genetic diagnostics: Their fastest diagnosis was made in just over seven hours.

Over the span of less than six months, the team enrolled and sequenced the genomes of 12 patients, five of whom received a genetic diagnosis from the sequencing information in less than eight hours. (Not all ailments are genetically based, which is likely the reason some of the patients did not receive a diagnosis after their sequencing information was returned, Ashley said.) The team’s diagnostic rate, roughly 42%, is about 12% higher than the average rate for diagnosing mystery diseases.

To achieve super-fast sequencing speeds, the researchers needed new hardware. So Ashley contacted colleagues at Oxford Nanopore Technologies who had built a machine composed of 48 sequencing units known as flow cells. The idea was to sequence just one person’s genome using all flow cells simultaneously. The mega-machine approach was a success, almost too much. Genomic data overwhelmed the lab’s computational systems. 

“We weren’t able to process the data fast enough,” Ashley said. “We had to completely rethink and revamp our data pipelines and storage systems.” Graduate student Sneha Goenka found a way to funnel the data straight to a cloud-based storage system where computational power could be amplified enough to sift through the data in real time. Algorithms then independently scanned the incoming genetic code for errors that might cause disease, and, in the final step, the scientists conducted a comparison of the patient’s gene variants against publicly documented variants known to cause disease. 

From start to finish, the team sought to hasten every aspect of sequencing a patient’s genome. Researchers literally ran samples by foot to the lab, new machines were rigged to support simultaneous genome sequencing, and computing power was escalated to efficiently crunch massive data sets. Now, the team is optimizing its system to reduce the time even further. “I think we can halve it again,” Ashley said. “If we’re able to do that, we’re talking about being able to get an answer before the end of a hospital ward round. That’s a dramatic jump.”