USA —Invent, an Amazon subsidiary has launched Amazon Omics to help researchers sift through genomic, transcriptomic, and proteomic data.
Amazon Omics, powered by Amazon Web Services, aims to help healthcare and life sciences organizations deliver precision medicine by storing, analyzing, and generating insights from genomic, transcriptomic, and other omics data.
The volume of such data is rapidly increasing. According to the National Human Genome Research Institute, researchers will require approximately 40 exabytes to store genome-sequence data generated globally by 2025.
Exabytes are equal to billions of gigabytes. A single human genome contains approximately three billion base pairs of DNA.
Amazon Omics is now available in North Virginia, Oregon, Ireland, London, Frankfurt, and Singapore, among other places.
Amazon’s rival Microsoft has similar offerings known as Microsoft Genomics and Microsoft Immunomics on its Azure cloud.
AWS notes that with Amazon Omics, it is possible to import and standardize petabytes of data to facilitate analytics with a few clicks of the Amazon Omics console.
Amazon Omics offers omics-optimized object storage, manage computing for bioinformatics workflows and optimized data stores for population-scale variant analysis.
The platform supports the storage of petabytes of genomics data. It can be used with FASTQ, BAM, and CRAM file formats.
In addition, the platform allows users to create and execute bioinformatics workflows while defining parameters and references to tools.
The analytics configuration can be used to store variant and annotation data. In addition, it can integrate with Amazon Athena, AWS LakeFormation, and Amazon SageMaker.
AWS also announced its beta/launch customers, which include the Children’s Hospital of Philadelphia, G42 Healthcare, Ovation, Element Biosciences, Ultima Genomics, BioTeam and Diamond Age Data Science.
At the event in Las Vegas, Amazon also announced the launch of AWS Supply Chain and a chip that gives the company high-performance computing capabilities.
Relevant genomic inferences are hard to make when genomic data is inaccessible or not in readily usable formats, such as those that electronic health records have typically been able to surface.
Accessing troves of genomic data all in one place is what enables machine learning to make predictions about what the best therapy is for an individual patient.
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