This Guidance shows how you can build and run production-grade bioinformatics workflows at scale. Using AWS services for automation, workflow analysis, storage, and operational and cost observability, you can follow DevOps best practices to manage the lifecycle of your bioinformatics workflows. You can use this architecture as the foundation for your own infrastructure and update certain aspects as needed to integrate it with your environment and meet your needs.
Please note: [Disclaimer]
[Architecture diagram description]
HealthOmics runs bioinformatics workflows in languages like Workflow Description Language (WDL), Nextflow, or Common Workflow Language (CWL) to analyze raw data. These workflows can be built as private or Ready2Run (hosted by HealthOmics).
Tools running within the workflows are stored as Docker images within Amazon Elastic Container Registry (Amazon ECR). Workflow outputs are uploaded to Amazon S3.
HealthOmics publishes workflow engine logs, task logs, and workflow run logs to Amazon CloudWatch for troubleshooting and monitoring.
Workflow developers and bioinformaticians can iterate on new and existing workflows and maintain version control using continuous integration and continuous delivery with AWS CodeCommit. AWS CodePipeline can be used to invoke an AWS CodeBuild job to automate the creation of new workflows in HealthOmics.
AWS Cost and Usage Reports (AWS CUR) facilitates cost monitoring. This service can be configured to create reports and upload them to an Amazon S3 bucket. An AWS Glue crawler is configured to ingest this data to AWS Glue Data Catalog, which can be queried using Amazon Athena to derive cost-related insights.
The AWS Well-Architected Framework helps you understand the pros and cons of the decisions you make when building systems in the cloud. The six pillars of the Framework allow you to learn architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable systems. Using the AWS Well-Architected Tool, available at no charge in the AWS Management Console, you can review your workloads against these best practices by answering a set of questions for each pillar.
The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.
This Guidance uses AWS CodeCommit, AWS CodeBuild, and AWS CodePipeline to create version control and automate the build and deployment of your bioinformatics workflow’s source code. Additionally, DynamoDB lets you track HealthOmics output files and run metadata. Because this Guidance uses DevOps best practices to manage your workflow code and give you visibility into workflow run metadata, you can make incremental changes to achieve accurate results. By tracking workflow run metadata, you can easily find relevant workflow run status and output files to perform downstream reporting or scientific analysis.
This Guidance provides encryption at rest using AWS Key Management Service (AWS KMS) and encryption in transit for all network traffic using DataSync. Additionally, AWS Identity and Access Management (IAM) provides fine-grained access control over potentially sensitive data so that only authorized users can perform specific actions to process and analyze it.
This Guidance lets you orchestrate computationally intensive bioinformatics workflows at scale by using HealthOmics. This service has certain service quotas, such as number of virtual CPUs, to prevent accidental overprovisioning. Additionally, Amazon S3 and DynamoDB provide high availability with built-in backup. This Guidance also uses EventBridge to capture events, such as failures, and Amazon SNS can provide real-time notifications in response so that you can take appropriate action. You can quickly investigate events using Amazon CloudWatch, which provides detailed logs to give you visibility into your HealthOmics workflows and underlying tools.
This Guidance lets you run concurrent workflows with different CPU and memory configurations for specific tasks. You can request resources by specifying the CPUs, memory, and storage you need, and HealthOmics provisions the appropriate infrastructure. This helps you scale based on your business needs with the right resources.
This Guidance uses an HealthOmics sequence store, which lets you store and share petabyte-scale genomics data files efficiently and at a low cost per gigabase, providing additional cost savings over Amazon S3. Additionally, you can use AWS CUR to access the most detailed information about your AWS costs and usage, identify areas for optimization, and understand your business’s trends based on attributes such as projects, departments, or users.
This Guidance uses managed and serverless services that help you avoid provisioning and managing your own infrastructure, helping you minimize the environmental impact of your projects. HealthOmics provisions resources only when you request a workflow run and tears down the resources when completed. Similarly, Lambda lets you run smaller tasks as functions without provisioning your own servers.
A detailed guide is provided to experiment and use within your AWS account. Each stage of building the Guidance, including deployment, usage, and cleanup, is examined to prepare it for deployment.
The sample code is a starting point. It is industry validated, prescriptive but not definitive, and a peek under the hood to help you begin.
Designing an event-driven architecture for Bioinformatics workflows using AWS HealthOmics and Amazon EventBridge
The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.
References to third-party services or organizations in this Guidance do not imply an endorsement, sponsorship, or affiliation between Amazon or AWS and the third party. Guidance from AWS is a technical starting point, and you can customize your integration with third-party services when you deploy the architecture.