The blog post discusses the utilization of Amazon Web Services (AWS) by life sciences organizations for analyzing transcriptomic sequencing data using public data sets. By employing AWS HealthOmics and Step Functions, the heavy lifting associated with processing large RNA-Seq data sets is eliminated. The blog outlines a case study where research teams process RNA-Seq data sets in FAST file format and further expand their knowledge using public data sets like GEO. With the automated workflow, users can simply provide GEO IDs, and AWS manages data ingestion, analysis, and archiving seamlessly. Through a combination of HealthOmics and Step Functions, the process is streamlined, allowing scientists to focus on analysis instead of infrastructure management. The solution involves creating Amazon S3 buckets and DynamoDB tables for tracking data ingestion status, ensuring cost-effective processing. Implementation details are provided for data set preparation and analysis, showcasing how Step Functions orchestrate the workflow from sample ingestion to results archiving. The blog highlights the efficiency gained by automating the workflow and provides resources for further exploration of HealthOmics capabilities.
Article Source
https://aws.amazon.com/blogs/architecture/genomics-workflows-part-7-analyze-public-rna-sequencing-data-using-aws-healthomics/
