Genome Data Interpretation: How to Ease the Bottleneck

Bloomberg NextCODE Hannes Smarason

Bloomberg BNA Business’ “Diagnostic Testing & Emerging Technologies,” highlights how NextCODE is providing a qualitatively different way to store and analyze genomic information to meet growing opportunities in personalized medicine.

With advances in sequencing technology and reduced costs, more and more data are generated every day on the genetic basis of disease. The challenge has become how to derive meaningful information from these mountains of data.

While various systems have been established in recent years to store the large amounts of genomic data from patients’ DNA, a remaining obstacle is to “break the bottleneck” so that researchers can process the vast data in multiple human genomes in order to identify and isolate a small, useful piece of information about disease. Conventional databases and algorithms have not been able to efficiently and reliably identify subset information among the millions of genetic markers in order to inform clinical decisions. This has become a major data management roadblock.

The key is to find new approaches for databases and algorithms that accommodate the unique ways that genomic information is analyzed and interpreted. As discussed in Bloomberg BNA, Diagnostic Testing & Emerging Technologies, NextCODE is already easing this bottleneck by providing a qualitatively different way to store and analyze genomic information and apply it to meet the growing opportunities for personalized medicine.

NextCODE’s Genomically Ordered Relational (or GOR) database infrastructure is a truly different way of storing this huge amount of data. The principle is very simple: rather than store sequence and reference data in vast unwieldy files, it ties data directly to its specific genomic position. As a result, the algorithms are vastly more efficient compared to a traditional relational database because they can isolate by location in the genome. That makes analysis faster, more powerful, and radically more efficient, both in terms of clinicians’ and researchers’ time, as well as computer infrastructure, I/O, and CPU usage.

This holistic approach applies broadly to the priorities of genome scientists around the world, helping them eliminate the data management bottleneck to identify more culprits to many inherited diseases, more quickly and cost effectively.

Read more about NextCODE’s work here.