TransAgent: Dynamizing Transcriptional Regulation Analysis via Multi-omics-Aware AI Agent

Transcriptional regulation research, as a core area of life sciences, faces challenges such as scattered multi-omics data, complex joint analysis, and difficulties in integrating data processing tools. To address these issues, we propose TransAgent, an agent software specifically designed for transcriptional regulation analysis. Through innovative designs such as multi-mode operation (planning/execution/automatic), dynamic memory management, rapid MCP tool expansion (integrating over 30 tools), integration of transcriptional regulation annotation data (over 20 data sources including epigenomics and gene expression profiles), and cloud Docker computing, TransAgent significantly improves analysis efficiency. We have successfully applied TransAgent to various transcriptional regulation analysis scenarios such as re-construction of super-enhancer regulatory circuit in esophageal squamous cell carcinoma and identification of key regulators in cardiomyocyte differentiation, demonstrating analytical robustness and uncovering biological insights. TransAgent automates the entire process from raw data processing to advanced analysis, such as joint prediction of multi-omics data, transforming traditionally time-consuming and labor-intensive tasks into a conversation-driven approach. This provides a new paradigm for transcriptional regulation research, centered around large models as the core driver of scalable agent application analysis.