AWS Genomics has released a new whitepaper, Genomics Data Transfer, Analytics, and Machine Learning using AWS Services, that provides essential information and guidance on how to build a Next Generation Sequencing (NGS) platform using AWS services, from instrument to interpretation. Accompanying the new whitepaper are three solutions, developed based on feedback from AWS life science customers currently running genomics workloads on AWS:
- Genomics Secondary Analysis Using AWS Step Functions and AWS Batch
- Genomics Tertiary Analysis and Data Lakes Using AWS Glue
- Genomics Tertiary Analysis and Machine Learning Using Amazon SageMaker
In the whitepaper, we provide recommendations and reference architectures for developing a genomics platform on AWS, including:
- Transferring genomics data to the AWS Cloud and establishing data access patterns
- Running secondary analysis workflows
- Performing tertiary analysis with data lakes
- Performing tertiary analysis using machine learning
We also address common concerns expressed by life science business leaders: How does an organization manage cost, optimize workload performance, and move fast with control? How does an organization secure sensitive information? What resources are available to help meet a team’s compliance needs?
Access Whitepaper Here
To learn more, visit Genomics in the Cloud on AWS, or explore our Genomics Executive Brief.
