Genomics data and transfer storage use cases

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Data transfer and migration
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Secure collaboration
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Genomic data sharing
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Data transfer and migration

Data transfer and migration icon

Solutions 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.

Illumina logo

Illumina stores petabytes of infrequently accessed data in Amazon S3 Glacier Deep Archive—saving customers over 90% in storage costs.

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Genomics England logo

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).

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Paradigm4 logo

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

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DNAnexus logo

"Migrating to DNAnexus on AWS provides enterprises with a solid foundation for data growth without compromising pipeline portability. We help diagnostics and biopharma customers to significantly accelerate pipeline execution, in some cases by more than 2x."

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Illumina logo

"Committed to enabling researchers to manage massive volumes of multi-omic data, we built Illumina Connected Analytics, a comprehensive cloud-based platform facilitating data management, analysis, and interpretation of large volumes of multi-omics data on the secure, scalable, and flexible environment of AWS."

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Seven Bridges logo

"The Seven Bridges platform and Seven Bridges analysis tools such as GRAF™ and ARIA™ rely on the flexibility and scalability of AWS to enable researchers to take advantage of massive repositories of genomic and phenotypic data — which have the potential to reveal transformative insights into the underpinnings of disease, novel therapeutic approaches and in silico disease models."

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NetApp Cloud Manager is the management and automation platform used for deploying and operating NetApp's Cloud Data Services.

Secure collaboration

Secure collaboration icon

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

Lifebit logo

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

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Biogen logo

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

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Melbourne Genomics Health Alliance logo

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

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DNAnexus logo

"DNAnexus Apollo allows medical centers and biopharma companies to collaborate across partner sites and optimize the translation of basic discoveries into practical clinical applications in pursuit of more precise patient treatments.".

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Illumina logo

"Illumina empowers researchers to accelerate discovery with Illumina Connected Analytics (ICA) on AWS. With ICA, researchers can securely manage, analyze, and interpret large volumes of multi-omics data in a scalable, and flexible environment."

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Seven Bridges logo

"Seven Bridges leverages the power of AWS to ensure that the world’s most precious data — genomic and health care data gathered from individuals around the globe — remains secure while still being accessible for the collaborative use of researchers and drug developers in biopharma, clinical settings, academia and government."

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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 & guidances

Registry of Open Data on AWS

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

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Biobank logo

DNAnexus and AWS power technology behind UK Biobank research analysis platform.

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The University of British Columbia logo

The University of British Columbia uses the AWS Registry of Open Data to identify 130,000 new viruses in 11 days.

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Stanford University logo

With the help of AWS, the Wall Lab creates the iHART dataset and includes it in the Registry of Open Data on AWS.

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DNAnexus logo

"UK Biobank’s Research Analysis Platform, built by DNAnexus on AWS, democratizes genomic data access and enhances collaboration, helping to unleash the imaginations of the world’s best scientific minds—wherever they are— to make discoveries that improve human health."

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Illumina logo

"At Illumina, we empower researchers to accelerate discovery through global collaboration with Illumina Connected Analytics on AWS where researchers can easily manage, analyze, and interpret large volumes of multi-omics data in a secure, scalable, and flexible environment."  

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Seven Bridges logo

"Seven Bridges leverages the power of AWS to ensure that the world’s most precious data — genomic and health care data gathered from individuals around the globe — remains secure while still being accessible for the collaborative use of researchers and drug developers in biopharma, clinical settings, academia and government."

Learn more »

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.

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.

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