Zhang, G.; Song, C.; Liu, L.; Wang, Q.; Li, C.

Transcriptional regulation research, as a core area of life sciences, faces challenges such as scattered multi-omics data, complex joint data analysis, and difficulties in integrating transcriptional regulation tools. We propose TransAgent, an agent software specifically designed for the field of transcriptional regulation analysis. Through innovative designs such as planning/execution/automatic modes, dynamic memory management, rapid MCP tool expansion (integrating over 30 tools including TRAPT), 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 classic transcriptional regulation analysis scenarios such as super-enhancer regulatory network construction and identification of key regulators in cardiomyocyte differentiation, achieving meaningful results. 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.