Pipeline Engine

bioAF includes a built-in pipeline execution engine that lets you run bioinformatics workflows without managing compute infrastructure.

Pipeline catalog

bioAF gives you a live library of every nf-core pipeline, not a fixed handful. Open the registry browser inside bioAF, search the full nf-core catalog, pick a version, and install any pipeline in a click. Installed pipelines show up in your catalog ready to launch. Popular choices include:

  • nf-core/scrnaseq: Single-cell RNA-seq analysis
  • nf-core/rnaseq: Bulk RNA-seq quantification
  • nf-core/atacseq: ATAC-seq peak calling and analysis

Beyond nf-core, you can run your own pipelines: see Custom pipelines below.

i What is nf-core?
nf-core is a community effort to collect, curate, and maintain high-quality Nextflow pipelines for bioinformatics. These pipelines are peer-reviewed, well-tested, and follow best practices. bioAF makes them available with one-click launching.

Custom pipelines

You are not limited to the curated catalog, and you are not limited to Nextflow. bioAF runs fully custom pipelines too:

  • Any program that runs on Linux: bash, Python, R, or arbitrary command-line tools, not just Nextflow workflows.
  • Bring your own workflow: point bioAF at a GitHub repository, or paste a script directly.
  • Develop, then promote: prototype in a Work Node, then promote your work to a versioned pipeline so your team can launch it self-serve.
  • The same guarantees as the catalog: version-pinned and reproducible, with full provenance from inputs to outputs and custom QC dashboards driven by your own metrics.

Launching a run

Select a pipeline, choose your experiment and samples, configure any parameter overrides, and launch. bioAF handles:

  • Scheduling compute resources
  • Staging input data
  • Executing the workflow
  • Collecting outputs to the results bucket
  • Notifying you when it’s done
Pipeline launch form with experiment selection and parameter configuration

Real-time monitoring

Every running pipeline shows:

  • Stage-by-stage status: See which steps are complete, running, or queued
  • DAG visualization: A visual graph of the workflow structure
  • Resource usage: CPU, memory, and timing per stage
  • Live logs: Stream logs from any running stage
Pipeline run detail showing DAG visualization with color-coded stage statuses

Outputs and results

When a pipeline completes, outputs are automatically:

  • Stored in the results bucket
  • Linked to the originating experiment and samples
  • Available for QC dashboards and visualization
  • Recorded in the audit log with full parameter provenance