Interpretable RNA-Seq Clustering with an LLM-Based Agentic Evidence-Grounded Framework

Elias Hossain, Mehrdad Shoeibi, Ivan Garibay, Niloofar Yousefi

Abstract: The interpretation of gene clusters derived from RNA sequencing (RNA-seq) experiments remains a persistent challenge in functional genomics, particularly in antimicrobial resistance studies where mechanistic context is essential. While clustering methods effectively identify co-expressed gene modules, their interpretation typically relies on enrichment statistics and manual literature review, limiting transparency, reproducibility, and scalability. We present BioGen, an agentic framework for post hoc interpretation of RNA-seq gene clusters that emphasizes evidence-grounded and traceable biological reasoning. Rather than introducing new predictive models or clustering algorithms, BioGen organizes existing biomedical knowledge through a structured pipeline that integrates literature retrieval, hypothesis formulation, and critic-based validation. The framework enforces explicit linkage between interpretive claims and external sources such as PubMed and UniProt, enabling systematic assessment of factual grounding and semantic consistency. We apply BioGen to RNA-seq data from Salmonella enterica, demonstrating that it produces concise, literature-supported cluster-level interpretations related to efflux regulation, virulence, and metabolic adaptation. Comparative and ablation analyses indicate that retrieval augmentation and critic-based filtering reduce unsupported statements relative to unconstrained large language model baselines albeit at the cost of reduced interpretive coverage. These results highlight the role of architectural constraints and verification logic in improving reliability of automated biological interpretation. Overall, BioGen is intended as an interpretive support layer that complements existing transcriptomic analysis workflows by improving auditability and reproducibility of RNA-seq cluster interpretation, rather than as a standalone discovery or predictive system.

External IDs:dblp:journals/corr/abs-2510-16082