Next-generation sequencing (NGS) has revolutionized genomic research by empowering scientists to sequence genomes at vastly higher speeds. In drug discovery research, NGS is now an indispensable tool for pharmaceutical firms—one that’s cheaper, faster, and produces more promising candidate medicines than conventional methods.
NGS is employed from the earliest stages of drug discovery to provide detailed genomic data that can be used in clinical applications. Those applications range from investigations into the molecular basis of drug resistance to vaccine development, to disease diagnostics.
For all its incredible potential, however, NGS brings with it a lot of tricky data management questions. That's where NetApp can help.
NGS is helping pharmaceutical companies derive greater insights into complex clinical causes of human diseases and enhance safety through multivigilance—the monitoring and reporting of risks (specifically for patients) in new and existing drugs, biologics, and vaccines.
In doing so however, NGS generates huge and complex datasets so rapidly that it creates a bottleneck for analysis. According to the National Human Genome Research Institute, part of the National Institutes of Health, it's estimated that within the next decade, genomic research will generate between 2EB and 40EB of data.
Pharma companies therefore must vastly upscale their data handling capacity to enable genomics teams to store and transfer large data volumes quickly and easily. They also must deploy computational tools that can handle, extract, and interpret the these large troves of data and unlock the valuable information hidden—especially tools that deploy heuristic algorithms and artificial intelligence (AI).
Gartner has identified a growing need to efficiently manage and make full use of genomic data. A 2021 Gartner report states, “By 2023, 40% of the top 25 healthcare and life science companies will have a genomics technology enterprise strategy and be actively leveraging genomics data in developing new products and therapies.”
Advanced analytics improve understanding by revealing hidden patterns in large and complex genomic datasets. By using conventional methods, those patterns would typically be far more difficult to discover—if they could be discovered at all. Even in the early stages of its deployment, NGS is transforming drug discovery by taking a deeper look into the genetic clues behind disease.
Machine learning (ML) and AI help process genomic data gathered from NGS with an extremely high degree of speed and accuracy, while still being a more cost-effective approach to the measurement of genetic variation. This can transform previously unwieldy volumes of data into tangible and valuable assets.
For researchers, too, AI and ML represent a significant time save. Algorithms that can read, group, and interpret data automatically offer a more efficient way to examine data, replacing the laborious task of manually processing and interpreting datasets. Researchers can then spend more time analyzing and drawing conclusions rather than collating data.
Good quality data and a strong data pipeline are the core ingredients for AI and ML success. To maximize effectiveness, the quality, amount, source, diversity, and reliability of the data must all be considered. NetApp® technology offers you the control needed to understand, manage and package your genomic data in an AI and ML-friendly fashion, meaning you’re able to extract valuable genomic insights.
Together with our partner offerings, NetApp life sciences solutions can help speed genomic research breakthroughs and cut time to market. For example:
Help your organization keep pace with the growth of genomic data. Learn more about NetApp life sciences solutions.
Linda leads the Life Sciences practice within the Healthcare and Life Sciences team. She brings her wealth of Pharmaceuticals and Life Sciences experience to conversations with customers, connecting the value of NetApp product and services to the line of business with focus on drug discovery, virtual and hybrid clinical trials, image analytics, digital, and beyond the pill initiatives. With 15 years of global experience, Linda started her career at GlaxoSmithKline R&D working as an innovation scientist at their Centre of Excellence. She then managed the clinical development of various brands while working in partnership with global commercial teams.
Wanting to be part of the digital transformation in the pharmaceutical industry, Linda joined Medidata, a pioneering clinical research and data management company where she helped customers across EMEA adopt the Medidata Rave platform. Linda then joined QAD, an end-to-end ERP provider, as a business development executive in their Life Sciences business unit. She advised pharma and medical devices clients on ERP solutions for their quality, supply chain, and digital manufacturing departments. In partnership with solution marketing Linda started an early ERP adoption program for Cell and Gene manufacturers. Linda is a member of Healthcare Businesswomen`s Association (HBA).
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