Artificial Intelligence | News | Insights | AiThority
[bsfp-cryptocurrency style=”widget-18″ align=”marquee” columns=”6″ coins=”selected” coins-count=”6″ coins-selected=”BTC,ETH,XRP,LTC,EOS,ADA,XLM,NEO,LTC,EOS,XEM,DASH,USDT,BNB,QTUM,XVG,ONT,ZEC,STEEM” currency=”USD” title=”Cryptocurrency Widget” show_title=”0″ icon=”” scheme=”light” bs-show-desktop=”1″ bs-show-tablet=”1″ bs-show-phone=”1″ custom-css-class=”” custom-id=”” css=”.vc_custom_1523079266073{margin-bottom: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;}”]

MemVerge and Analytical BioSciences Accelerate Cancer and COVID-19 Research With Big Memory

  • Big Memory Technology Speeds up Single-Cell RNA Sequencing Time-to-Results Tenfold

MemVerge, the pioneers of Big Memory software and Analytical BioSciences Ltd., a leader in single cell genomics, announced the companies have accelerated the time-to-results for single-cell RNA sequencing (scRNA-seq) analysis by using Big Memory technology. A bleeding edge 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. On February 17th, The White House announced The Centers for Disease Control and Prevention (CDC) will invest nearly $200 million to identify, track and mitigate emerging strains of SARS-COV-2 through genome sequencing.

Recommended AI News: Avantis Investors Continues to Build out Relationship Management Team

“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.

Related Posts
1 of 15,226

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.”

Recommended AI News: Carnegie Learning Launches Bold New Brand Highlighting Its Innovative Approach to Improving Student Outcomes

scRNA-seq Testing Results

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.

Testing also showed that data load times were up to 800x faster with Memory Machine.

The bigger memory capacity allows more tasks to run in parallel and easy rollback to run what-if scenarios. These benefits combined with faster execution times resulted in an overall 10x increase in project throughput per server.

Recommended AI News: Trifacta Partners with Google Cloud to Host First-Ever Data Engineering Summit

Leave A Reply

Your email address will not be published.