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Kioxia Accelerates Analysis of Large-Volume Data in Life Science Research Outcomes
Findings Have Been Published in the Journal Science
- January 29, 2021
- Kioxia Corporation
Kioxia Corporation is pleased to announce its contribution to the acceleration of large-volume data analysis in the research outcomes described in a new paper "Expansion Sequencing: Spatially Precise In Situ Transcriptomics in Intact Biological Systems", published in the journal Science(1). This acceleration was achieved by building a GPU cluster for life science research equipped with solid-state drives (SSDs) and by developing image analysis algorithms. This research on an RNA sequencing technology was led by the Synthetic Neurobiology Group at the MIT Media Lab and their collaborators, including Kioxia, and the results were obtained by utilizing the GPU cluster for image analysis.
This novel RNA sequencing technology captures multiple microscopy images of the specimen that has been physically expanded using swellable polymers, and identifies RNA sequences through image analysis, thereby maintaining the tissue structure. The image data size becomes terabytes, requiring acceleration of data analysis. By running the image analysis algorithms on the GPU cluster equipped with CM series enterprise SSDs developed by Kioxia, the researchers were able to analyze 10 terabytes of data from a large specimen in a few days, demonstrating the identification of RNA sequences as well as their high-resolution locations within the tissue in mouse brains and a human cancer sample.
Kioxia will continue to conduct research and development on the acceleration of large-volume data processing, and contribute to advancing science and technology by strengthening industry-academia collaboration to foster innovations.
(1) Alon et al. <https://science.sciencemag.org/cgi/doi/10.1126/science.aax2656 >
Please see our website for more information on our research collaboration with the Synthetic Neurobiology Group.
https://www.kioxia.com/en-jp/rd/collaboration/memory-topics01.html