TGen Will Use Big Memory Technology to Accelerate IPF Research
The Translational Genomics Research Institute (TGen) will use a big-memory technology, called MemVerge Memory Machine Big Memory virtualization software, to accelerate research on the cellular mechanisms underlying idiopathic pulmonary fibrosis (IPF).
“MemVerge Memory Machine has quickly resulted in research value for TGen,” Jonathan Jiang, chief operating officer of MemVerge, said in a press release.
IPF is a lung disease of unknown origin characterized by the formation of scar tissue (fibrosis) in the lungs. It is estimated to affect 100,000 people in the U.S. every year.
There are several types of cells involved in the development of IPF, such as those that make up the epithelium, or the protective lining, of the lungs. However, not all of these epithelial cells participate in disease onset and progression.
Researchers at TGen, an affiliate of City of Hope, use a method called single-cell RNA sequencing to quantify the expression, or activity, of many genes across thousands of these individual cells. This allows researchers to determine whether a particular group of cells are involved in IPF onset and progression, based on their individual gene activity profile.
However, this technique can take nearly seven hours to analyze an array of 30,000 genes in a set of 114,000 cells, which is much longer than desired for disease research.
Moreover, traditional memory technology is not able to store these large amounts of data, and a scaling solution like dynamic random access memory (DRAM) is too costly.
Now, TGen will use MemVerge’s memory virtualization technology that combines DRAM with another type of memory machinery, PMem, which stands for persistent memory. With this combination, the memory pool needed for data processing is increased without involving additional costs.
This technology will also expedite single-cell sequencing, as it uses ZeroIO snapshot, an in-memory snapshot service. This service is able to pick up large amounts of information at any point within seconds. It also enables a 1,000-times faster reloading time during data processing, compared with current storage snapshot options.
ZeroIO can also run several processing workflows at the same time, ensuring a prompt restart if the system fails.
“By utilizing the snapshotting and cloning capabilities of Memory Machine, we were able to parallelize the processing workflow,” said Glen Otero, PhD, vice president of scientific computing at TGen.
“As a result, we can now save nearly 36% of computational time while also taking advantage of the big memory nodes. This will save a lot of time in downstream analysis,” Otero added.
There is a growing need for a modern in-memory computing model to support emerging applications that call for real-time studies, accurate in-memory processing, and fault-tolerant storage to speed up the processing of large amounts of data. According to its developers, MemVerge Memory meets these needs by using all available memory capacity.
“We have removed performance barriers from [TGen’s] research process so that they are able to perform vital, life-saving, research faster than ever possible,” Jiang said. “Now TGen is expanding the use of Big Memory technology across other research use cases where results and discoveries can produce findings for a healthier tomorrow.”
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