Data transfer and storage solutions
Reduce storage costs and accelerate genomics innovation with secure data transfer, global collaboration, and access to genomic datasets.
AWS HealthOmics Ready2Run Workflows
Transform genomic and biological data into insights
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Powering genomics innovations with AWS for Genomics Featured partner solutions Featured resource
Leading genomics organizations including
Ancestry,
DNAnexus,
Illumina,
Genomics England, and
GRAIL leverage AWS to securely migrate genomics data to the cloud for store, analysis, and collaboration. AWS offers genomics-aware object storage with AWS HealthOmics Sequence Store that helps reduce short and long-term storage costs. With the largest global infrastructure, genomics organizations can securely collaborate and access industry data while adhering to data sovereignty requirements.
Data transfer and migration
Solutions and services for the secure migration and transfer of genomics data to AWS for cost. AWS solutions enable you to transfer and store large scale genomics data and take advantage of high throughput data ingestion, cost effective storage options, and secure access in a secure, compliant manner. AWS HealthOmics provides genomics-aware object storage that helps you reduce storage costs, manage data life cycles, and securely share data.
Featured customers
Illumina stores petabytes of infrequently accessed data in Amazon S3 Glacier Deep Archive—saving customers over 90% in storage costs.

Genomics England migrates data from its 100,000 genome project with AWS Professional Services and AWS Partner Kainos as part of the AWS Migration Acceleration Program (MAP).

Paradigm4 gives researchers rapid access and scalable analysis for complex scientific data on AWS.

Featured consulting partners
AWS Marketplace
NetApp Cloud Manager is the management and automation platform used for deploying and operating NetApp's Cloud Data Services.

Secure collaboration
Solutions that enable secure industry collaboration and data sharing. AWS HealthOmics Storage and AWS Partners offer scalable, cost-efficient, and maintainable solutions for quick, secure global collaboration.

Featured customers
Lifebit creates a system for researchers to securely collaborate on large datasets in seconds.

Biogen's AWS architecture enables it to effortlessly work with partners to analyze data for 500,000 participants.

Melbourne Genomics Health Alliance builds GenoVic on AWS to enable sharing of genomic data across Alliance members.

Featured consulting partners
AWS Marketplace
DataEz is a modular, scalable, secure, and compliant data analytics platform designed for Life Sciences. Customers can deploy core infrastructure, configure managed data services, and chain multiple data services together to create custom data pipelines.

Genomic data sharing
Solutions for the responsible and secure sharing of genomic data. AWS and its partners provide the tools, solutions, and security needed to host, access, and contribute to genomics datasets worldwide.

AWS solutions
The Registry of Open Data on AWS hosts more than 70 life sciences databases, including the Cancer Genome Atlas and Genome Aggregation Database.
Explore the registry
Featured customers
Biobank

The University of British Columbia

Stanford University

Featured partner solutions
DNAnexus

Illumina

Seven Bridges

Featured consulting partners
AWS Marketplace
DataEz is a modular, scalable, secure, and compliant data analytics platform designed for Life Sciences. Customers can deploy core infrastructure, configure managed data services, and chain multiple data services together to create custom data pipelines.

Featured resource: Whitepaper: Genomics Data Transfer, Analytics, and Machine Learning using AWS Services
Whitepaper: Genomics Data Transfer, Analytics, and Machine Learning using AWS Services
This white paper describes how to build a next-generation sequencing (NGS) platform from instrument to interpretation using AWS services, with recommendations and reference architectures for developing the platform including: 1) transferring genomics data to the AWS and establishing data access patterns, 2) running secondary analysis workflows, 3) performing tertiary analysis with data lakes, and 4) performing tertiary analysis using machine learning. Read the whitepaper
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