Pan.bio is an AI-powered, end-to-end genomics platform that unifies bioinformatics workflows, variant interpretation, and patient cohort analytics in a single privacy-first environment. The platform combines validated nf-core pipelines, interactive notebooks, and guideline-aware clinical variant classification, all supported by bioMind, a specialized AI agent layer. Built with HIPAA, GDPR, SOC 2, and ISO 27001 compliance, it supports in-country deployment and de-identified data processing for healthcare and research use.
Funding
Funding not disclosed
Founders
Product
Problem
Genomic analysis teams often rely on fragmented tools and ad-hoc scripts, leading to workflows that cannot be reproduced, compared, or scaled across projects. This fragmentation creates slow time-to-insight, with researchers spending weeks debugging environments instead of interpreting biology, and it prevents effective collaboration when datasets, collaborators, or questions change.
Solution
Pan.bio provides an AI-powered, end-to-end genomics platform that connects bioinformatics workflows, variant interpretation, and patient cohort data in one unified system. The platform includes validated, version-controlled pipelines from the nf-core collection, interactive pre-configured notebooks with the scientific Python and R stack, and a guideline-aware clinical variant classification tool (VAIC) that codifies ACMG/AMP, CanVIG, ACGS, and ClinGen standards. A central AI copilot called bioMind powers all platform experiences through domain-specialist agents that share a common library of skills, enabling natural-language cohort discovery, code generation, and workflow guidance. The platform is built privacy-first with tenant isolation, de-identified patient data, in-country deployment, and full audit trails, ensuring compliance with HIPAA, GDPR, SOC 2, and ISO 27001 at the infrastructure level.
Target Audience
Primary users are clinical genomics laboratories, research institutions, and bioinformatics teams that need reproducible analysis workflows, clinical variant interpretation, and cohort analytics in a compliant, secure environment.
Features
- Validated nf-core pipeline execution with support for importing custom Nextflow workflows from GitHub, including a dual-caller clinical workflow for germline variant detection across any gene panel
- Pre-configured notebook environment with pandas, NumPy, Matplotlib, ggplot2, Samtools, and Bedtools, plus direct integration of public datasets from GEO, SRA, and IPG via accession numbers
- bioMind AI copilot with specialized agents per application, sharing a common skill library, providing live context-aware code generation, debugging, and natural-language cohort discovery
- Guideline-aware variant classification (VAIC) that codifies ACMG/AMP, CanVIG, ACGS, and ClinGen rules into structured, reproducible logic with AI assistance while keeping final classification with clinicians
- Multi-step analysis framework that converts exploratory work into reusable, team-standardized workflows with independently adjustable steps
- Enterprise-grade security architecture including row-level tenant isolation, role-based access control, in-country data residency enforcement, and complete audit logging of all AI actions and data access
- MCP server integration and agent-to-agent (A2A) protocol support for external tool connectivity and multi-agent collaboration across institutional boundaries