geneRNIB: a living benchmark for gene regulatory network inference

Gene regulatory networks (GRNs) underpin cellular identity and function, playing a key role in health and disease. Despite various benchmarking efforts, existing studies remain limited in the number of GRN inference methods, datasets, and evaluation metrics. The absence of a universally accepted ground truth further complicates the evaluation, requiring continuous refinement of benchmarking strategies. In addition, regulatory interactions are highly context-specific and vary between perturbations, cell types, tissues, and organisms. However, current benchmarks do not account for this complexity, limiting their applicability in personalized medicine. Here, we introduce geneRNIB, a comprehensive GRN bench-marking framework built on three key principles: context-specific evaluation, continuous integration, and holistic assessment in the absence of a true reference network. geneRNIB enables the seamless incorporation of new algorithms, datasets, and evaluation metrics to reflect ongoing developments. In the current version, we systematically integrated and assessed ten GRN inference methods, spanning single- and multiomics approaches across five diverse datasets including thousands of perturbation scenarios. We introduced eight novel metrics specifically designed to assess context-specific causal inference. Our findings indicate that simple models with fewer assumptions often outperformed more complex pipelines. Notably, gene expression-based correlation algorithms yielded better results than more advanced approaches incorporating prior datasets or pre-trained on large datasets. In addition, we identified several potential factors that influence the performance of GRN inference and offered actionable guidelines for the future development of the method. By addressing these critical limitations in existing benchmarks, geneRNIB advances GRN research and fosters progress toward personalized medicine.