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Born in the AWS Cloud: How Eagle Genomics Uses AWS to Process Billions of DNA Sequence Reads
Amazon AWS re:Invent 2017
With the increasing use of genomic sequencing for scientific discovery, the rate-limiting step for researchers is not in obtaining genetic code, but in having the capacity for storage and computing power to analyze it. In this session, you learn how Eagle Genomics built a cloud platform that uses an open-source workflow engine (eHive), Docker containers to process jobs, and a REST service to manage pipeline runs, all to help customers process genetic sequences up to 20 times faster without…
With the increasing use of genomic sequencing for scientific discovery, the rate-limiting step for researchers is not in obtaining genetic code, but in having the capacity for storage and computing power to analyze it. In this session, you learn how Eagle Genomics built a cloud platform that uses an open-source workflow engine (eHive), Docker containers to process jobs, and a REST service to manage pipeline runs, all to help customers process genetic sequences up to 20 times faster without additional costs. You also learn how Eagle used Amazon EC2 Spot instances to provide low-cost compute power, and services such as Amazon S3 to power their cost-effective and scalable genetic processing platform.
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