Tag Archives: genome data storage

A New Era, New Vision for WuXi and NextCODE Health

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WuXi-NextCODE

WuXi PharmaTech has acquired NextCODE Health to create WuXi NextCODE Genomics, a global leader in genomic medicine. Pairing WuXi’s technology and existing reach with NextCODE’s leading analytics and database promises to advance the pace of genomics research today.

In the fast-paced genomics community, we continually look for new opportunities and strategies to enhance the value of genomics and use the increasingly robust body of genomic data for the advancement of clinical medicine.

We’re excited to announce a new, ambitious vision to do just that, with WuXi’s acquisition of NextCODE Health. NextCODE will be merged with WuXi’s existing Genome Center in wholly-owned subsidiary called WuXi NextCODE Genomics, with unique, comprehensive and global capabilities for using genomic data to deliver better medicine and improve healthcare.

WuXi, a Shanghai-based genomic laboratory service partner for companies in the pharma and biotech community, has already been collaborating with NextCODE to provide analysis services to customers of the WuXi Genome Center. Now, with the in-house capability to analyze, store, and manage the vast amount of genomic data, NextCODE’s industry-leading genome sequence analysis platform will expand WuXi’s core next-generation sequencing benefits and services.

Pairing WuXi’s technology and existing reach with NextCODE’s leading analytics and database promises to advance the pace of genomics research today. More importantly, however, this new era for NextCODE brings exciting opportunities to maximize the most advanced tools available today and contribute to major advances in genomic medicine.

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Genome Data Interpretation: How to Ease the Bottleneck

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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.

A Standard Database Architecture Will Build a Stronger Foundation for Genome Discoveries

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big data genome sequencing hannes smarason

The general adoption of the Genomically-Ordered Relational database (GOR) as a data standard for storing genomic data may greatly accelerate the spread of sequencing and its effectiveness as a tool for advancing medicine.

It is widely accepted that the ability to share the analysis and insights from DNA sequencing will be a key driver of discovery and innovation. But one current limitation to extending this knowledge is that sequencing and analysis platforms, as well as samples, are often proprietary to and stored at different institutions. Perhaps more important, the structures and formats in which genomic data has customarily been stored—the relational databases developed by the likes of IBM and Oracle—make it unwieldy to analyze as the amount of data grows, and very difficult to share. The upshot is that institutions cannot easily share and consolidate information to generate more robust analyses and clinically relevant insights. This presents a serious hurdle to discovery both in rare disorders, where samples need to be gathered in order to generated adequate analytical power, and in complex ones, where truly massive studies can tease apart different facets of disease and reveal their causes.

Over the past decade, a novel and comprehensive database model has been developed to solve this bottleneck, offering a flexible and fast means to overcome these problems. It is called the Genomically-Ordered Relational database, or GOR, and was designed to manage and query the detailed genomic data amassed by deCODE genetics in Iceland – the world’s first and still by far largest and most comprehensive population-based genomic database.

The thinking behind the GOR is as simple as it is revolutionary. Genomic data is a sort of big data but one with an important difference: It is divided up in distinct packets—the chromosomes—and then arranged within each chromosome in linear fashion. The GOR makes use of this by storing and querying sequence data according to its unique position in the genome, rather than as huge files as long as the sequence. This radically reduces the data burden of querying even large numbers of whole genomes, at the same time making it possible to store and visualize instantly the raw sequence underlying an analysis.

In practice, the GOR thereby enables researchers to home in on specific variants without having first to call up entire patient genomes, and separates raw data from annotations to focus in on only the most relevant search components. It’s these types of functions and features that can be consistently applied across data storing systems to allow for more multi-institutional, collaborative research and consistency in outcomes worldwide.

Leaders in the genomic research community are now beginning to create coalitions and working groups to underpin and coordinate the adoption of standards for sharing genomic data. As these groups create flexible and efficient policy frameworks, the GOR is tested and ready to support the fundamental data requirements of global data sharing and the acceleration of discoveries in genome-based medicine. The general adoption of the GOR as a data standard for storing genomic data may greatly accelerate the spread of sequencing and its effectiveness as a tool for advancing medicine around the world.