Speeding Innovation in Genomics with Modern-Day Infrastructure

February 22, 2021 - Next-generation sequencing is critical to the use of genomics to improve clinical diagnostics and therapeutics for a wide range of diseases, from coronavirus to cancer.

While platforms for next-generation sequencing (NGS) vary, each has its primary goal to sequence millions of small DNA fragments in parallel. With advancements in computational processing and storage, NGS has become a fertile ground for new genomics discoveries with implications for clinical applications and disease outcomes.

That said, technology can still prove a hindrance to discovery when genomic sequencing workflows running concurrently are not supported by modern data infrastructure. The success of primary, secondary, and tertiary analyses hinges on the availability of high-performance computing and storage architecture to ensure the lowest levels of latency and highest levels of throughput.

A single sequencer can produce terabytes of data. With large stores of sequencing instruments, life sciences organizations are producing petabytes of genomic data points. Preventing disruption to sequencing efforts and scientific analysis is critical to translating data into clinical practice.

With the coronavirus is affecting millions of individuals, time is of the essence for researchers to turn insights about the various strains of COVID-19 into treatments for sick patients and vaccines to achieve herd immunity. The ability to do so quickly depends on researchers' ability to leverage next-generation sequencing to understand and combat the virus. A large part of reducing that time-to-diagnosis for diseases centers around modern-day infrastructure that is scalable, rapid, and agile in terms of processing and storage.

The emergence of robust data infrastructure is leading to meaningful gains for researchers who’re now able to load genome indexes at a fraction of the time needed by traditional hardware. At McMaster University, where researchers focus on antimicrobial resistance and coronaviruses, analysis is occurring at twice the speed and a third of the cost. Hours rather than days are the new measure for sequencing an unknown pathogen.

Researchers and clinicians at the University of California–Berkeley are banking on modern-day infrastructure to eschew the traditional trial-and-error approach to drug development by speeding genetic sequencing of individual patients and opening the door to precision medicine. Through IT efforts to decouple data storage from computational activities, workloads have tripled in speed, from 30 to 11 minutes in some instances. Certain resource-heavy tasks crippled the organization's distributed file system. Using a modular and scalable data storage infrastructure removed this barrier.

Targeting a Leading Cause of Death

While the coronavirus pandemic remains a significant threat to public health in the United States, a coordinated response by federal, state, and local authorities is leading to a drop in positive cases and hospitalizations. As the pandemic continues to die down, the focus will again return to improving diagnostics and therapeutics for leading causes of death, most noticeably cancer.

The field of oncology is eager to tap into the potential of next-generation sequencing to personalize care for specific patients and populations. Over the past few years, next-generation sequencing is proving instrumental to driving improvements in understanding cancer vulnerabilities and predicting potential therapies for patients at scale.

"The application of NGS technologies to the characterization of human tumors has provided unprecedented opportunities to understand the biological basis of different cancer types, develop targeted therapies and interventions, discover genomic biomarkers of drug response and resistance, and to guide clinical decision-making regarding the treatment of patients," write authors of a 2018 review of genomics in cancer medicine.

Because genetic mutations play such a significant role in cancer development and its response to treatment, researchers are looking to large-scale sequencing efforts as a foundational step toward novel cancer therapies. The process is likely to generate large volumes of data and the need for powerful compute resources to aggregate, analyze, and report insights.

"The possibility of treating cancer on the basis of an individual tumor's genetic profile has led to a surge in cancer-genome profiling of patients," argues Bianca Nogrady of Nature Outlook.

"For example, in chronic lymphocytic leukaemia, the presence of a mutation in the TP53 gene means that the cancer probably won't respond to chemoimmunotherapy," she continues. "If physicians know that a person has that mutation, they might instead opt for a stem-cell transplant. And in colorectal cancers, mutations in the KRAS gene mean that patients will not respond to drugs such as cetuximab or panitumumab."

Technical barriers to genomics continue to disappear as new forms of data infrastructure come to market and mature. For life sciences researchers to speed the discovery of new treatments and therapies for novel and known conditions, they will need the support of technology suited to keeping pace with innovation and change.

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