PromptGenix

Women & Minority-Owned BioAI Automation

End-to-end RNA-Seq & Flow Cytometry analysis, automated and explained.

PromptGenix turns raw FASTQ and FCS files into reproducible, publication-ready reports using automated pipelines and LLM-based scientific interpretation.

Discuss a pilot See how it works

What PromptGenix does

Automates RNA-Seq, scRNA-seq, and flow cytometry pipelines
Runs COMPASS, GSEA/GSVA, and advanced statistics by default
Generates clear English summaries of complex immune signatures
Deploys securely on local Mac Studio or AWS cloud

The Problem

Modern immunology and genomics labs collect terabytes of RNA-seq and flow cytometry data, but analysis still depends on fragmented tools, manual scripts, and a handful of experts. Turnaround time is measured in weeks, not hours, and every project feels like a one-off.

Today's bottlenecks

Multiple disconnected tools (STAR, RSEM, FlowJo, COMPASS, GSEA)
Custom scripts that are hard to reuse and harder to maintain
Limited access to biostatistics & bioinformatics experts
Scientific interpretation left to overworked PIs and analysts

The PromptGenix Platform

Automated pipelines

RNA-Seq: QC → alignment → quantification → DEG → GSEA/GSVA
Flow cytometry: preprocessing, auto-gating, batch normalization
COMPASS: automated polyfunctionality analysis for vaccines & trials
scRNA-seq: Seurat / scanpy integration and cell-type reporting

LLM-based interpretation

Plain-language summaries of pathways and immune signatures
Contextualization with disease area and literature
Exportable HTML/PDF reports ready for lab meetings or manuscripts
Runs locally or on the cloud for secure environments

Python & R pipelines

BioGPT & LangChain

Hybrid local + AWS

Reproducible analytics

Team

Yon Ji, Ph.D.

CEO & Co-Founder

Molecular and cell biologist with 20+ years of experience across NIAID/NIH, Johns Hopkins, and NCI. Expertise in ChIP-seq, RNA-seq, CRISPR, stem cells, C. elegans and Xenopus models, and cancer biology. Leads strategy, partnerships, and operations for PromptGenix.

Dohoon Kim, M.S.

CTO & Co-Founder

Senior data scientist and computational biologist with 12+ years in infectious disease research at Walter Reed. Specializes in biostatistics, ML, RNA-seq and flow cytometry automation, COMPASS workflows, and LLM-powered bioinformatics agents in R and Python.

SBIR Project Overview (For Reviewers)

Project Title

PromptGenix: An automated, LLM-assisted platform for RNA-Seq and flow cytometry analysis in translational immunology and infectious disease research.

Small Business: PromptGenix LLC | Proposed PI/Contact: Dohoon Kim, M.S. (CTO) & Yon Ji, Ph.D. (CEO)

Unmet Need

RNA-Seq, scRNA-seq, and flow cytometry data are critical in vaccine and immunology studies, but analysis remains slow, manual, and fragmented across tools.
Many labs lack in-house biostatistics/bioinformatics support, leading to long delays and inconsistent pipelines.
Existing commercial tools focus on either NGS or cytometry, rarely integrating both with modern ML/LLM-based interpretation.

Innovation & Aims (Phase I)

Aim 1: Build a reproducible, containerized pipeline that automates RNA-Seq and flow cytometry analysis (QC → DEG/GSEA → COMPASS) from raw FASTQ/FCS files.
Aim 2: Integrate a domain-tuned LLM that produces clear, reviewer-friendly reports summarizing key immune signatures and pathways.
Aim 3: Validate the platform on publicly available immunology datasets and generate benchmarks vs. current manual workflows.

Approach & Milestones

Implement modular pipelines in Python/R (Snakemake/Nextflow-ready) for RNA-Seq and flow cytometry, containerized for secure deployment.
Develop an LLM “analysis copilot” that links statistical outputs (DEG tables, COMPASS scores, pathways) to concise English summaries.
Demonstrate feasibility with 2–3 use cases (e.g., vaccine trial, HIV or COVID-19 cohort) using public data.
Deliverables: prototype web UI, reproducible code repository, and example Phase I report packets.

Team & Environment

Yon Ji, Ph.D. (CEO): 20+ years in molecular and cell biology (NIH/NIAID, NCI, Johns Hopkins); leads strategy and translational relevance.
Dohoon Kim, M.S. (CTO/PI): 10+ years as data scientist/biostatistician in infectious disease at Walter Reed; expert in RNA-Seq, flow cytometry, COMPASS, and ML.
Infrastructure: secure local Mac Studio + cloud (AWS-ready) environment for development and testing.

Phase I Outcomes & Next Steps

Phase I will produce a working prototype capable of turning raw FASTQ/FCS data into integrated, LLM-assisted reports suitable for lab meetings and manuscripts.
Phase II will extend to multi-site clinical trials, add user management and audit trails, and integrate with hospital or CRO data environments.
Commercialization strategy focuses on subscriptions for research labs, hospitals, and CROs, with emphasis on government and minority-owned contracts.

For SBIR reviewers: This page summarizes the proposed Phase I scope. A detailed work plan, budget, and regulatory/compliance discussion are provided in the full application.