MemVerge® and Analytical BioSciences announced the companies have accelerated the time-to-results for single-cell RNA sequencing (scRNA-seq) analysis by using Big Memory technology. A solution composed of Intel® Optane™ Persistent Memory (PMEM) and MemVerge Memory Machine software produced up to 800x faster scRNA-seq load times and 25x faster execution for some computational stages, compared to a traditional DRAM-based solution.
Scientists rely on genomic sequencing to identify new SARS-CoV-2 variants and understand how they will impact health.
"Single-cell RNA sequencing is one of the key fundamental research methods that drives advances in Cancer and COVID-19 research. We are proud to be a world's leading provider of the most advanced single-cell analytical tools," said Chris Kang, Head of Bioinformatics Operations at Analytical Biosciences. "The Big Memory platform that MemVerge and Intel developed accelerates our workflows and helps us generate results much faster, which will lead to more efficient ways to gain greater insights and knowledge in diseases mechanisms and improve healthcare."
Single-cell RNA sequencing analysis is a process containing multiple large scale data processing steps. The long-running process is compute-intensive and imposes significant demands on memory resources. Computation uses very large matrices that need to fit in memory and intermediate results must be saved and reloaded for other stages, which introduces storage and recovery bottlenecks. Furthermore, the process has many stages that must be repeated for parameter tuning.
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Memory Machine software from MemVerge addresses these memory resource issues. Its ZeroIO™ memory snapshots eliminate the storage IO bottleneck to significantly reduce overall time to execute the entire scRNA-seq analysis. Other bioinformatics analyses that use large matrices derived from next-generation sequencing techniques can also be accelerated with Big Memory.
"Until now, memory infrastructure did not offer a viable alternative to storage for genomic sequencing," said Charles Fan, CEO at MemVerge. "Big Memory offers the same high-performance as DRAM at a dramatically lower cost, and with the persistence and agility needed for complex data pipelines."
As a baseline, the complete analysis using R-based tools was completed using DRAM only. The analysis was repeated using Memory Machine and the combined DRAM plus PMEM memory pool. Each analysis stage included data loading and saving processes to mimic the real working scenarios. The testing showed that execution time was up to 25x faster with Memory Machine, saving a total combined execution time of 60 percent across all stages.