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The Centre for Cellular and Molecular Biology (CCMB), a research organisation focused on modern molecular biology and population-scale genomics, has chosen AWS as its preferred cloud provider to accelerate its genomics research projects.

With AWS, CCMB would be able to reduce time taken for genomics analysis by up to 98%, accelerating research efforts on the study of genetics and human diseases, said the cloud provider on Friday.

Life sciences and genomics research organisations need to access, store, and analyse large amounts of data, generated from next generation high-throughput sequencers. Previously, these organisations have relied on on-premises servers to meet their storage and compute needs. The data-intensive nature of genomics research meant that CCMB had to procure more on-premises storage frequently to manage petabyte scale datasets, and store the raw data and the resultant output files generated from secondary and tertiary analysis.

According to AWS, CCMB was also relying on on-premises high-performance computing (HPC) clusters to perform this analysis, which was prone to downtime, impacting research timelines and output. Using on-premises servers created challenges for scalability and performance, so CCMB turned to cloud computing to seamlessly scale up its data storage and analysis needs.

“At a time when genetics research is becoming critical for life sciences advancement, disease diagnosis, and drug development, we must innovate using technologies like cloud computing to achieve outcomes faster and better,” said Dr. Divya Tej Sowpati, genomics scientist at the CSIR CCMB.’

Running on AWS, CCMB performed short tandem repeat (STR) genotyping — an analysis to determine a person’s DNA profile — on 3,200 samples from the 1,000 Genomes Project, an international research effort to establish a detailed catalogue of human genetic variation. Using services such as Amazon Aurora, Amazon Elastic Compute Cloud (Amazon EC2), EC2 Auto Scaling, Amazon Simple Storage Service (Amazon S3), and AWS Batch, CCMB was able to reduce the time taken for research analysis by up to 98%, from 550 days to just nine days on average, he said.