Benchmarking DNA large language models on quadruplexes

Large language models (LLMs) in genomics have successfully predicted various functional genomic elements. While their performance is typically evaluated using genomic benchmark datasets, it remains unclear which LLM is best suited for specific downstream tasks, particularly for generating whole-genome annotations. Current LLMs in genomics fall into three main categories: transformer-based models, long convolution-based models, and statespace models (SSMs). In this study, we benchmarked three different types of LLM architectures for generating whole-genome maps of G-quadruplexes (GQ), a type of flipons, or non-B DNA structures, characterized by distinctive patterns and functional roles in diverse regulatory contexts. Although GQ forms from folding guanosine residues into tetrads, the computational task is challenging as the bases involved may be on different strands, separated by a large number of nucleotides, or made from RNA rather than DNA. All LLMs performed comparably well, with DNABERT-2 and HyenaDNA achieving superior results based on F1 and MCC. Analysis of whole-genome annotations revealed that HyenaDNA recovered more quadruplexes in distal enhancers and intronic regions. The models were better suited to detecting large GQ arrays that likely contribute to the nuclear condensates involved in gene transcription and chromosomal scaffolds. HyenaDNA and Caduceus formed a separate grouping in the generated de novo quadruplexes, while transformer-based models clustered together. Overall, our findings suggest that different types of LLMs complement each other. Genomic architectures with varying context lengths can detect distinct functional regulatory elements, underscoring the importance of selecting the appropriate model based on the specific genomic task. The code and data underlying this article are available at https://github.com/powidla/G4s-FMs