On-premises researchers analyze data in Amazon S3 in existing bioinformatics tools by using Network File System (NFS) or Server Message Block (SMB) through Amazon S3 File Gateway. Partnering entities like a contract research organization (CRO) can upload study results to Amazon S3 by using AWS Transfer for FTP, SFTP, or FTPS. You can optimize storage by writing instruments that run data to an S3 bucket configured for infrequent access. Identify your S3 storage access patterns to optimally configure your S3 bucket lifecycle policy and transfer data to Amazon S3 Glacier. Using Amazon FSx for Lustre, data is made accessible to high performance computing (HPC) on the cloud for genomics, imaging, and other intensive workloads to provide a low millisecondlatency shared file system. Research HPC workloads are orchestrated on the cloud with AWS Step Functions and AWS Batch, for flexible central processing unit (CPU) and graphics processing unit (GPU) computing on Amazon Elastic Compute Cloud (Amazon EC2) instances or Amazon Elastic Container Service (Amazon ECS) containers. Machine learning is conducted with a common artificial intelligence and machine learning (AI/ML) toolkit that uses Amazon SageMaker for feature engineering, data labeling, model training, deployment and ML operations. Amazon Athena is used for flexible SQL queries with exis
