Decoding Regulatory structure from RNA-seq Data: A Reproducible Python Framework for Gene Regulatory Network Inference Using GENIE3

This work presents a practical computational pipeline for inferring gene regulatory networks (GRNs) from RNA-seq data using the GENIE3 algorithm. Moving beyond differential expression analysis, this study focuses on reconstructing regulatory relationships between transcription factors and target genes using ensemble tree-based models. The guide includes:– Conceptual foundations of gene regulatory networks– Step-by-step Python implementation using arboreto– Data preprocessing and transcription factor selection strategies– Network construction, edge ranking, and hub gene identification– Publication-style visualization of inferred regulatory structures This resource is intended for researchers in computational biology, bioinformatics, and systems biology seeking to extract regulatory insights from transcriptomic data.