Porcine MutBERT: a family of lightweight genomic foundation models for functional element prediction in pigs

The pig (Sus scrofa) is both an economically important livestock species and a valuable biomedical model . Its genome bears regulatory features shaped by domestication and selection that are often poorly captured by genomic language models (gLMs) trained on human or model organism data. To address these challenges, we developed Porcine MutBERT, a suite of lightweight gLMs with 86 million parameters that employs a probabilistic masking strategy targeting evolutionarily informative single-nucleotide polymorphisms. This design captures population-specific variation while reducing computational cost. We further propose PorcineBench, a benchmark that evaluates gLM performance across porcine functional genomics tasks, including chromatin accessibility (ATAC-seq), CTCF binding, and histone modifications (H3K27ac, H3K4me1, and H3K27me3). Results show that Porcine MutBERT family achieves highly competitive performance on PorcineBench relative to substantially larger models, while providing an explicitly porcine-adapted alternative for downstream functional genomics in pigs. These findings underscore the advantages of species-adapted, efficient architectures in agricultural genomics and demonstrate that compact gLMs can expand accessibility and impact in resource-constrained settings. The code and data are available at https://github.com/ai4nucleome/pigmutbert.