AWS HealthOmics resources

Learn how to use AWS HealthOmics in this step-by-step developer guide.

Read this reference guide for AWS HealthOmics APIs and parameters.

This guide provides instructions for installing and using the AWS HealthOmics Kiro Power and IDE Extension within the Kiro IDE to enhance the user experience when working with HealthOmics.

This guide walks through the process of creating, deploying, and running genomics workflows using AWS HealthOmics.

You can ask Kiro CLI to build workflows, analyze run logs, and make performance optimization recommendations.

Get started with HealthOmics workflows and storage

Open source repository on GitHub with example code that demonstrates how to store, process, and query genomic and biological datasets using AWS HealthOmics

Provides syntax highlighting, code completion, and troubleshooting guidance, along with HealthOmics engine compatibility checking, performance optimization recommendations, automated run analysis with failure diagnostics, and workflow import/export capabilities.

Packages the HealthOmics MCP server with guidance, giving the Kiro IDE agent expertise in HealthOmics workflow creation and optimization.

A Model Context Protocol (MCP) server that provides AI assistants with comprehensive access to AWS HealthOmics services for genomic workflow management, execution, and analysis.

Use this collection of AWS HealthOmics workflows to accelerate drug discovery

Build private workflows based on open source HealthOmics workflow examples

Run analyzer is a run performance optimization tool that retrieves run manifests logs from CloudWatch and generates statistics for a run, including CPU and memory utilization for each workflow task

Use the HealthOmics WDL linter to detect common workflow errors before running workflows

Lint for common Nextflow errors before a workflow script is run

Re-run archived runs from CloudWatch run manifest logs

This Guidance demonstrates how to build a scientific data management system that integrates both laboratory instrument data and software systems, such as electronic lab notebooks (ELNs) and lab information management systems (LIMS), with HealthOmics.

This Guidance helps you to connect life sciences data instruments and laboratory system files to the AWS Cloud, either through the internet or a direct connection with low latency.

This Guidance demonstrates how to import omics sequence data from Amazon Simple Storage Service (Amazon S3) into AWS HealthOmics Storage.

This Guidance demonstrates how to set up an end-to-end framework to analyze multimodal healthcare and life sciences (HCLS) data.

This Guidance shows how you can build and run production-grade bioinformatics workflows at scale and use AWS services for automation, workflow analysis, storage, and operational and cost observability.