Tag: Genomics

This post was contributed by U. Deva Priyakumar, Rakesh Srivatsava, Prathit Chatterjee, Vladimir Aladinskiy, Ramanathan Sethuraman, Yusong Wang, Alex Iankoulski, and Beryl Rabindran Today, we’re excited to announce the release of a comprehensive dataset featuring molecular dynamics (MD) trajectories for over 16,000 protein-ligand complexes (PLCs). This dataset, now available on AWS as part of the […]

Streamline your genomic and multiomic data analysis with DRAGEN on Amazon EC2 F2 instances. Our latest blog post explores the performance benefits of this hardware-accelerated solution, helping you unlock insights faster.

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.

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.

Genomics community: rejoice! Seqera and AWS have teamed up to announce Seqera Containers, an open-source, no cost, reliable way to generate containers.

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.

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.

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.

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.

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.