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CompuBioGenome Intelligence

What Is the Dashboard?

The CompuBio dashboard acts as a single source of truth for your analyses.

From one unified interface, you can:

  • Manage datasets and metadata
  • Launch reproducible workflows
  • Monitor execution in real time
  • Explore and interpret results
  • Export outputs for reporting or downstream use

Everything is designed to reduce friction between data → computation → insight.

What You Can Do

Upload & Manage Datasets

Upload and organize your data in one place:

  • Sequencing data: FASTQ, BAM / CRAM
  • Variant data: VCF
  • Sample annotations: CSV / TSV metadata

Datasets are versioned and linked to analyses automatically.

Run Workflows

Launch analyses directly from the dashboard:

  • Select validated, prebuilt workflows
  • Configure parameters and references
  • Re-run analyses with identical settings for comparison

Every run is fully reproducible by design.

Track Jobs in Real Time

Each analysis progresses through clear states:

  • Queued
  • Running
  • Completed
  • Failed

You can inspect:

  • Step-by-step execution logs
  • Resource usage
  • Runtime and failure reasons

Explore Results

Once a job completes, results become immediately available:

  • Interactive plots
  • Filterable tables
  • Summary statistics
  • AI-generated insights (if enabled)

Dashboards replace static reports with explorable analysis views.

Export Outputs

Export results in formats suitable for collaboration and compliance:

  • Downloadable tables and figures
  • Reports for sharing or archiving
  • Artifacts accessible via API (plan-dependent)

Access Control & Plans

CompuBio uses plan-based feature gating to match different user needs.

Starter Plan

  • Core microbiome workflows
  • Basic variant analysis
  • Standard dashboards

Pro Plan

  • GWAS and population analysis
  • Advanced analytics modules
  • Richer interactive dashboards

Team Plan

  • Shared workspaces
  • Collaboration features
  • API access and automation (if enabled)

Best Practices

Following these best practices ensures reliable and interpretable results:

  • Keep sample IDs consistent across data and metadata
  • Use the same reference builds and parameters when comparing cohorts
  • Document analysis versions for traceability
  • Export reports for collaboration and long-term archiving