TransMAgent: Dynamic Transcriptional Regulation Analysis Using Multi-Omics Aware Multi-Agent Systems

Transcriptional regulation research has long been hindered by critical challenges, including the difficulty of integrating multi-omics data, the complexity of analytical toolchains, and the steep technical expertise required for computational workflows. To fundamentally address these limitations, we introduce TransMAgent, an advanced multi-agent network system specifically engineered for transcriptional regulation analysis. By leveraging task decomposition with reflective mechanisms, multi-agent collaboration, dynamic context management, and a domain-specific knowledge base coupled with automated tool expansion, the system achieves end-to-end automation that encompasses the entire workflow from tool deployment and data acquisition to task decomposition and execution. TransMAgent enables efficient and precise analysis across diverse scenarios, extending from super-enhancer identification to the inference of complex, multi-dimensional single-cell regulatory networks, which represents a substantial advancement from simple tool invocation to automated tool construction. Furthermore, through an efficient training strategy for domain-specific agent language models, we have significantly improved the performance of the original base model on transcriptional regulation tasks. By transforming complex transcriptional regulation analysis into minimalist interactions driven purely by dialogue, this research provides the field with a highly scalable, comprehensive agent context engineering paradigm centered on large language models.