Today, Amazon Omics announces the availability of Ready2Run workflows, a set of pre-built workflows from third-party software companies and open-source pipelines. With just a few clicks or a single API call, customers can run pre-built pipelines to perform primary analysis such as converting base calls to FASTQ files, secondary analysis such as gene expression or variant calling, and tertiary analysis such as protein structure prediction. Ready2Run workflows are priced-per-run to give customers predictable pricing. We are launching with 35 Ready2Run workflows, which are a combination of workflows built by Element Biosciences, NVIDIA, and Sentieon Inc, as well as popular open-source pipelines developed by the life sciences community.

Today, Amazon Omics announces support for NVIDIA T4 and a10 graphical processing units (GPUs) for Omics workflows. Omics private workflows allows customers to bring their own workflow scripts and specify the compute resources that they need for each task in their workflow. Customers can now enable NVIDIA T4 and a10g GPUs for use in Omics private workflows to support accelerated and AI-based genomics analysis with NVIDIA Parabricks and open-source protein folding pipelines.

Today, Amazon Omics announces a new capability for direct data ingestion to Omics storage. Omics storage enables customers to store FASTQ, BAM, and CRAM files at a cost-effective price at scale. Previously, Omics had an asynchronous batch upload process for bulk loading of sequence readsets. This new capability adds a simple synchronous upload capability. The multi-part direct upload APIs will now allow customers to upload their data directly to the sequence stor­­e. This functionality allows customers to integrate existing processing pipelines and/or sequencers to directly write their outputs to a sequence store. Additionally, the transfer manager utility has been updated so that customers can directly upload large files with a single python command.

AWS Config supports 62 new resource types in advanced queries. The advanced queries feature provides a single query endpoint and a powerful query language to get current resource state metadata without performing service-specific describe API calls. You can use configuration aggregators to run the same queries from a central account across multiple accounts and AWS Regions.

Amazon Textract is a machine learning service that automatically extracts text, handwriting, and data from any document or image. We regularly improve the underlying machine learning models based on customer feedback to deliver better accuracy and latency. Today, we are pleased to announce quality enhancements to our Queries feature available via the AnalyzeDocument API. 

Amazon Timestream now enables you to export your query results to Amazon S3 in a cost-effective and secure manner using the new UNLOAD statement.

Today, we are announcing the general availability of the tail sampling processor and the group-by-trace processor in the AWS Distro for OpenTelemetry (ADOT) collector. ADOT is a secure, production-ready, AWS supported distribution of the OpenTelemetry project. With this release, customers can use the ADOT collector for advanced distributed trace sampling use cases. 

Amazon QuickSight is excited to announce the launch of Common Sub-expression Elimination (CSE) – a performance optimization initiative for better query generation for SPICE datasets. The CSE improves QuickSight user experience through pushing down repeated use of complex expressions into intermediate tables hence simplify complex queries, such as for totals/subtotals, top bottom filter, conditional formatting, and “others” bucket for charts, etc. Through the CSE query optimization, customers would observe faster loading dashboards, especially for complex dashboards and time-consuming interactions. Currently, the CSE is released with SPICE datasets only. There is no customer configuration needed, the backend change will seamlessly apply to the QuickSight usage.

“I feel we’ll have to put out something about the semiconductor strategy, Ed,” the PM told me after Cabinet, “people keep on about it and I believe you had some ideas.” “One idea, Prime Minister,” I told him. “That’s enough for a strategy – more than some government strategies have,” laughed the PM, “make it …

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The U.S. Air Force Research Laboratory (AFRL) has selected a joint research team from Carnegie Mellon University and the University of North Carolina at Chapel Hill as the Phase I winner of its recent Grand Challenge. The competition, to give its full name, is Active Artificial Intelligence, or AI, Planners for Chemistry/Materials Optimization and Discovery …

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