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High-Performance Computing.
Training.
High-Speed Networking.

What is SCINet?

The SCINet initiative is an effort by the USDA Agricultural Research Service (ARS) to grow USDA’s research capacity by providing scientists with access to high-performance computing clusters, high-speed networking for data transfer, and training in scientific computing.

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ARS scientists/collaborators:
Register for an account

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Upcoming Trainings and Events

  • Bioinformatics Foundations · RNAseq and Variant calling pipelines in Galaxy

    In this workshop, participants will work through a complete RNA-seq analysis and a variant calling workflow using SCINet’s Galaxy interface.

    • June 22, 24-25, 2026, 1 – 5 PM ET
      • Registration: Register Here
      • Prerequisites:
        • Familiarity with basic command-line concepts, next-generation sequencing data types, and have a general understanding of gene expression

  • Bioinformatics Foundations · Automating Bioinformatics Pipelines with Nextflow

    This hands-on workshop introduces Nextflow, a workflow management system built for scalable, reproducible computational pipelines that can run seamlessly across laptops, HPC clusters, and cloud environments. Starting from simple examples, we’ll progressively build toward a real-world bioinformatics pipeline — learning how Nextflow’s dataflow programming model and channel-based design enable elegant parallel processing of multiple files, portable integration of tools via containers and modules, and production-ready pipelines that follow nf-core community best practices.

  • Bioinformatics Foundations · Automating Bioinformatics Pipelines with Snakemake

    This hands-on workshop introduces Snakemake, a workflow management system that brings the readability of Python to scalable, reproducible computational pipelines. We will start with simple examples and build to a real-world bioinformatics pipeline — learning how Snakemake’s rule-based, file-driven approach automatically determines job dependencies, handles parallel execution, and integrates seamlessly with Python scripts and virtual environments to produce publication-ready outputs.

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