Life Sciences

Genomics Data Transfer, Analytics, and Machine Learning using AWS Services AWS Whitepaper Document revisions To be notified about updates to this whitepaper, subscribe to the RSS feed. Change Description Date Initial publication Whitepaper first published. November 23, 2020 30

• Genomics Data Transfer, Analytics, and Machine Learning using AWS Services
• Table of Contents
• Genomics Data Transfer, Analytics, and Machine Learning using AWS Services (1)
• Abstract
• Introduction
• Transferring genomics data to the Cloud and establishing data access patterns using AWS DataSync and AWS Storage Gateway for files
• Recommendations
• Reference architecture
• File-based access to Amazon S3
• Running secondary analysis workflows using AWS Step Functions and AWS Batch
• Recommendations
• Reference architecture
• Performing tertiary analysis with data lakes using AWS Glue and Amazon Athena
• Recommendations
• Reference architecture
• Performing tertiary analysis with machine learning using Amazon SageMaker
• Recommendations
• Reference architecture
• Conclusion
• Appendix A: Genomics report pipeline reference architecture
• Appendix B: Research data lake ingestion pipeline reference architecture
• Appendix C: Genomics data transfer, analytics, and machine learning reference architecture
• Appendix D: Compliance resources
• Appendix E: Optimizing data transfer, cost, and performance
• Appendix F: Optimizing storage cost and data lifecycle management
• Appendix G: Optimizing secondary analysis compute cost
• Appendix H: Handling secondary analysis task errors and workflow failures
• Appendix I: Monitoring secondary analysis workflow status, cost, and performance
• Appendix J: Scaling secondary analysis
• Appendix K: Optimizing the performance of data lake queries
• Appendix L: Optimizing the cost of data lake queries
• Contributors
• Document revisions
• Notices