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Automated, validated analysis workflows from raw sequencing data to structured outputs, for research and clinical applications. Run any Nextflow workflow from the nf-core collection, from public GitHub, or import your own, without worrying about infrastructure.

The Problem

Genomic analysis should be standardised, reproducible, scalable and easily usable by coders and non-coders, even when you change a dataset, a collaborator, or a question.

Most teams still stitch together fragmented tools and ad-hoc scripts. The result: workflows that can't be repeated, compared, or scaled.

01

Fragmented tooling

Every project reinvents the wheel, custom scripts, local installs, undocumented workflows. Nothing connects, nothing carries over.

02

Slow time-to-insight

Weeks spent debugging environments and reformatting outputs instead of interpreting biology. The bottleneck is workflow engineering, not science.

03

Can't reproduce, collaborate, or scale

What a previous colleague did can't be redone by another. Results vary between analysts and across datasets. Collaborators being on the same page is a nightmare.

How It Works

From raw data to results in five steps

A streamlined path from sequencing files to clinical-grade outputs, automated, reproducible, and auditable.

sample_R1.fastq.gz

2.4 GB

sample_R2.fastq.gz

2.4 GB

samplesheet.csv

12 KB

Step 01

Upload sequencing data in standard formats, FASTQ files, sample sheets in CSV or TSV, and more.

  1. Step 01: Upload Your Data

    Upload sequencing data in standard formats, FASTQ files, sample sheets in CSV or TSV, and more.

  2. Step 02: Choose Your Analysis

    Select from validated nf-core pipelines or import your own Nextflow workflow directly from GitHub.

  3. Step 03: Pipeline Processes

    The pipeline handles quality checks, data alignment, variant or expression analysis, and preparation of structured outputs.

  4. Step 04: Explore in Notebooks

    Walk through results step by step in interactive notebooks, adjust thresholds, run custom analyses, and visualize findings.

  5. Step 05: Outputs & Reports

    Receive structured outputs ready for interpretation, tables, figures, VCFs, BAMs, and clinical reports.

Automated Workflows

Validated workflows. Consistent results.

Every workflow is organized, versioned, and production-ready, so your team can focus on biology, not infrastructure.

Nextflow EcosystemImport from GitHubAdaptive ComputeReproducible by Default

Featured Workflow

Pan.bio Clinical Workflow

A precision-engineered, dual-caller NGS workflow for reproducible germline variant detection across any gene panel, from ACMG Secondary Findings to fully custom targets.

Initial validation key stats

≥94 %

SNV precision

~99 %

SNV recall

100 %

INDEL recall

2 -caller

consensus

Scope: Somatic variants·Copy Number Variation (CNV)·Structural Variants (SV)

AI-Assisted Workflows

Powered by BioMind. Guided by your data.

The Workflow Agent works alongside every stage of your analysis, from selecting the right workflow to explaining why a run failed.

BioMind

AI

Your AI pipeline guide, for every stage of the run.

Pan.bio's Workflow Agent has deep knowledge of every nf-core workflow it supports. It guides parameter selection for your sequencing protocol, validates your sample sheet before submission, and explains outputs in plain language, so your team spends less time reading docs and more time interpreting results.

Parameter Guidance

Select the right nf-core workflow and configure run parameters for your sequencing protocol, genome, and variant calling mode before you start.

Documentation on Demand

Ask questions about any workflow step and get answers directly from nf-core documentation, with the relevant section cited.

Output Interpretation

Explain MultiQC metrics, identify quality flags, and suggest the right downstream steps based on what your workflow actually produced.

Failure Diagnosis

Read workflow failure logs, explain what went wrong in plain language, and suggest the right recovery path.

Built on nf-core Knowledge

Unlike a generic code assistant, BioMind's Workflow Agent is trained on nf-core workflow documentation, common parameter pitfalls, and protocol-specific configurations. Ask it about Sarek germline mode vs. tumor-normal, or why your RNA-seq run is flagging a low alignment rate, and it responds with workflow-specific guidance, not a generic answer.

Collaboration

Built for Teams, Not Just Individuals.

Pan.bio is designed so that every pipeline, every notebook, and every analysis is structured for collaboration, reproducible by default, auditable by design, and accessible to everyone on the team.

Shared Notebooks

Multiple users collaborate in the same notebook, sharing data and analysis while each maintaining their own interaction history with BioMind.

Reproducible Standards

Workflows are always available with specified versions. Turn promising exploratory ad hoc experiments into reusable multi-step analyses your entire team can standardize on.

Audit Trail & Logging

Every analysis is logged, versioned, and auditable. Know who ran what, when, and with which parameters, across every project.

Access Control

Role-based permissions and multi-tenant isolation ensure teams work securely. Each client or project stays separated without rebuilding the stack.

For Researchers

Workflows reduce complexity and save time. Instead of worrying about software setups or technical details, receive structured outputs that are ready for interpretation or reporting. Analyses become easier to repeat, compare, and scale across projects.

For Organizations

Workflows become consistent, easier to maintain, and faster to deploy. Teams collaborate more effectively because everyone works from the same structured process rather than individual scripts or ad-hoc methods.

Differentiators

What Makes Pan.bio Workflows Different?

What sets Pan.bio apart is the balance between structure and flexibility. Many platforms offer automation but limit exploration. Others allow full flexibility but become difficult to maintain at scale. Pan.bio connects the two, helping teams move faster without sacrificing clarity or trust in the results.

Reusable by Design

Turn promising exploratory ad hoc experiments into reusable multi-step analyses your entire team can standardize on.

AI-Powered Assistance

BioMind guides parameter configuration and answers questions about any pipeline, cited from its own documentation.

Designed for Real-World Use

Built to handle both research and production environments with enterprise-grade compliance.

Compliance as Architecture
HIPAA PHI safeguards
GDPR EU data privacy
SOC 2 Trust services criteria
ISO Infosec management

Enforced at the infrastructure level, your data stays in your jurisdiction, always.

See It in Action

Pan.bio bioWorkflows turns fragmented genomic analysis into a structured, reproducible workflow, from raw sequencing data to structured outputs, every time.

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