AWS HPC Blog
Tag: Genomics
How Caris Life Sciences processed 400,000 RNAseq samples in 2.5 days with AWS Batch
In the race to deliver precision medicine, time is of the essence. Caris Life Sciences, a pioneer in this field, leveraged AWS Batch to build a highly scalable solution that processed hundreds of thousands of genomic samples in record time. Discover how they achieved this remarkable feat and the key services that powered their breakthrough.
A guide to identity management in Research and Engineering Studio on AWS
Check out this new post to learn about identity options for Research and Engineering Studio on AWS. Understanding choices for SAML IdPs and Active Directory will help you plan secure VDI access.
Announcing: Seqera Containers for the bioinformatics community
Genomics community: rejoice! Seqera and AWS have teamed up to announce Seqera Containers, an open-source, no cost, reliable way to generate containers.
Linter rules for Nextflow to improve the detection of errors before runtime
Check out this post to learn how linter rules for Nextflow’s DSL can help you catch errors in your workflows before runtime, which means greater developer productivity, which leads directly to a faster time to science.
Intel Open Omics Acceleration Framework on AWS: fast, cost-efficient, and seamless
With genomics and multi-omics research generating more data than ever, the Open Omics Acceleration Framework from Intel Labs aims to provide a highly productive platform for researchers. Check out recent results in this new blog post.
Leveraging Seqera Platform on AWS Batch for machine learning workflows – Part 2 of 2
In this second part of using Nextflow for machine learning for life science workloads, we provide a step-by-step guide, explaining how you can easily deploy a Seqera environment on AWS to run ML and other pipelines.
Leveraging Seqera Platform on AWS Batch for machine learning workflows – Part 1 of 2
Nextflow is popular workflow framework for genomics pipelines, but did you know you can also use it for machine-learning? ML is already being used for medical imaging, protein folding, drug discovery, and gene editing. In this post, we explain how to build an example Nextflow pipeline that performs ML model-training and inference for image analysis.
Running accurate, comprehensive, and efficient genomics workflows on AWS using Illumina DRAGEN v4.0
In this blog, we provide a walkthrough of running Illumina DRAGEN v4.0 genomic analysis pipelines on AWS, showing accuracy and efficiency, copy number analysis, structural variants, SMN callers, repeat expansion detection, and pharmacogenomics insights for complex genes. We also highlight some benchmarking results for runtime, cost, and concordance from the Illumina DRAGEN DNA sequencing pipeline.
Cost-effective and accurate genomics analysis with Sentieon on AWS
In this blog post, we benchmark the performance of Sentieon’s DNAseq and DNAscope pipelines using publicly available genomics datasets on AWS. You will gain an understanding of the runtime, cost, and accuracy performance of these germline variant calling pipelines across a wide range of Amazon EC2 instances.
Helping bioinformaticians transition to running workloads on AWS
Calling budding bioinformaticians! If you learn through hands-on practicals and walkthroughs, AWS and GIS have developed training and resources to help you increase the scale and productivity of your research using the AWS cloud.