Genomic language models (gLMs): Emerging applications, challenges, and future directions in computational genomics

Genomic language models (gLMs) are rapidly becoming important tools for learning biological information directly from sequence data. By adapting concepts from natural language processing, these models aim to capture contextual dependencies, regulatory grammar, evolutionary constraint, and sequence-level functional patterns that may be difficult to detect using alignment-based, motif-based, or conventional supervised methods alone. This systematic review evaluates recent model-development studies of genomic, RNA, nucleotide, codon-level, and regulatory DNA language models, with emphasis on model architecture, tokenization, training objective, biological task, benchmarking strategy, and reported limitations. A structured search of PubMed, Scopus, and Web of Science identified 469 records. After duplicate removal, screening, and full-text eligibility assessment, 58 studies met the strict inclusion criteria for primary model development or substantial model adaptation. The included studies covered diverse applications, including regulatory sequence prediction, variant-effect modeling, genome annotation, microbial and viral genome analysis, RNA splicing and regulation, codon optimization, mRNA design, and generative design of regulatory or RNA sequences. Across the included studies, stronger evidence for gLM utility was generally associated with biologically informed or task-aligned model design, including evolutionary alignments, motif-aware objectives, long-context architectures, RNA structural priors, population-aware representations, and domain-specific pretraining. However, the evidence was heterogeneous and did not support a general claim of superiority over established bioinformatics tools or specialized supervised models. In several regulatory genomics tasks, specialized supervised models, k-mer-based approaches, or conventional deep-learning baselines remained competitive or superior to pretrained language-model representations. Generative models showed growing promise for RNA, codon, viral genome, and cis-regulatory element design, although many were evaluated mainly in silico. Overall, the field is advancing quickly, but broader impact will require standardized benchmarks, clearer reporting, stronger external validation, improved interpretability, and experimental confirmation of predicted or generated biological functions.