GLM-Prior: a nucleotide transformer model reveals prior knowledge as the driver of GRN inference performance

Abstract

Gene regulatory network (GRN) inference depends on high-quality prior knowledge, yet curated priors are often incomplete or unavailable across species and cell types. We present GLM-Prior, a genomic language model fine-tuned to predict transcription factor (TF)-target gene interactions from nucleotide sequence, and benchmark it as a sequence-derived strategy for constructing TF-gene prior matrices. We integrate GLM-Prior with PMF-GRN, a probabilistic matrix factorization model, to create a dual-stage pipeline that combines sequence-derived priors with single-cell gene expression data for prior-conditioned GRN inference. Across six human, mouse, and yeast cell-line contexts, GLM-Prior performance scales with positive label abundance and TF coverage in training data, and shows abovechance agreement with independent reference networks in well-annotated mammalian contexts. We evaluate single-species, species-transfer, and multi-species training paradigms, finding that GLM-Prior can construct informative priors across related mammalian species, while transfer to yeast remains near chance. Comparisons with accessibility-based priors across multiple GRN inference methods show that GLM-Prior achieves the highest prior performance in four of five mammalian cell lines. Under these benchmarks, prior quality largely constrains achievable GRN inference performance, with expression-based inference providing prior-dependent refinement. Together, these results position GLM-Prior as a benchmarked workflow for transferable, sequence-derived prior construction in systems where matched experimental assays are unavailable.