Today, data torrents are affecting scientists and researchers in genomics and other life sciences in a very profound way. There are two reasons. First, as more and more data sources are added, researchers are unable to manage collected data such as avalanches. Second, researchers lack the ability to quickly calculate data and turn data into valuable. The ability of scientific insights.
At present, genomics is at such an inflection point: the cost of human genome sequencing has been less than $1,000 and is expected to continue to decline (compared to $3 billion in 2003). As sequencing costs decrease, genetic testing becomes more common and the corresponding genomic data continues to rise. Only a single person's genome "runs" produces 0.5TB (1TB = 1024GB) raw data image files, which are complex and contain decentralized, unstructured scientific data that is difficult to manage and analyze.
With the development of sequencing technology, the challenge for researchers is how to manage and analyze these large, unstructured genomic data. Typically, these data are generated in academic research, clinical trials, and pharmaceutical research around the world. Many organizations now require more advanced data analysis and management for drug discovery, disease genetic testing, and the creation of personalized treatments in clinical applications. However, genome sequencing is a complex multi-step process involving DNA sequence reads, genomic sequence splicing, variant region sequence analysis, and resequencing.
The reality is that the technology we have used in the past decade has not been strong enough to analyze these key data. Such technologies are destined to be replaced by new technologies in the future, as the demand for data analysis is growing faster as genetic sequencing companies continue to innovate. At the same time, there is a growing demand for genome sequencing.
Therefore, what technologists need to do is to analyze these data through sophisticated high-performance computing (HPC) or supercomputer and big data technologies to make the management and analysis of genomic data more convenient and efficient.
Big data itself
The pursuit of personalized medicine has produced explosive data growth, because doctors and researchers hope to achieve optimal personalized treatment for different patients based on patient's disease performance and drug tolerance through gene sequencing. . At the same time, with the increasing funding of genomics research, genetic sequencing has become more and more commercial, and has further promoted the development of personalized medicine.
In related cases, Kaiser Permanente collected DNA samples, medical records, etc. from more than 210,000 patients across the United States, and subsequently created the world's largest and most comprehensive database of precision medicine . On this basis, researchers hope to find specific genes affecting various genetic diseases, in order to improve the diagnosis, treatment and prevention of diseases in clinical applications.
Of course, in order to successfully sort out these complex, scattered, unstructured scientific data, researchers need large-scale computing and high-speed analysis of data and flexible computing systems, but traditional computing systems can not keep up with the development of data needs. pace.
Fortunately, with the advent of modern supercomputing technology, research institutions can continue to increase the amount of data and analyze valuable scientific insights.
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