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OMICX: An AI-powered web platform for integrative analysis of multi-condition single-cell transcriptomics data.

Yue Fan1, Jin Ning1, Long Wang1

  • 1Center for Single-Cell Omics and Health, School of Public Health, Xi'an Jiaotong University, Xi'an 710061, Shaanxi, PR China; Collaborative Innovation Center of Endemic Diseases and Health Promotion in Silk Road Region and NHC Key Laboratory of Environment and Endemic Diseases, Xi'an Jiaotong University, Xi'an 710061, Shaanxi, PR China.

Genomics
|April 6, 2026
PubMed
Summary

OMICX is a new code-free platform using an AI assistant for single-cell RNA sequencing (scRNA-seq) analysis. It makes complex bioinformatics accessible via natural language conversations for easier biological insight discovery.

Keywords:
AI-assistant analysisMulti-condition analysisMulti-omics integrationSingle-cell transcriptomicsWeb platform

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Advanced single-cell RNA sequencing (scRNA-seq) analysis requires specialized bioinformatics expertise.
  • Current tools often involve complex coding, limiting accessibility for many researchers.
  • Integrating diverse omics data, like scRNA-seq with bulk RNA-seq or GWAS, presents significant analytical challenges.

Purpose of the Study:

  • To introduce OMICX, an interactive, code-free web platform designed to simplify complex single-cell data analysis.
  • To leverage a large language model (LLM) assistant, OMICX-Agent, for natural language-driven bioinformatics.
  • To enhance accessibility of advanced analyses, including multi-sample, multi-condition, and integrative omics studies.

Main Methods:

  • Development of OMICX, a web platform featuring an LLM-powered assistant (OMICX-Agent).
  • OMICX-Agent guides users through scRNA-seq processing, multi-condition analysis, and integrative studies using natural language.
  • Demonstration of platform versatility through three real-world case studies involving scRNA-seq, bulk RNA-seq, and GWAS summary statistics.

Main Results:

  • OMICX successfully enables code-free, interactive analysis of single-cell and integrative omics data.
  • Case studies validated the platform's utility in complex multi-condition scRNA-seq analysis and integration with GWAS and bulk RNA-seq data.
  • The platform demonstrated ease of use and reproducibility in extracting biological insights.

Conclusions:

  • OMICX democratizes advanced bioinformatics by providing an intuitive, conversational interface for single-cell data analysis.
  • The LLM-powered OMICX-Agent significantly lowers the barrier to entry for complex omics data integration and analysis.
  • OMICX empowers researchers across disciplines to confidently perform sophisticated analyses and accelerate biological discovery.