De Winter, S. et al. Modelling and design of transcriptional enhancers. Nat. Rev. Bioeng. 3, 374–389 (2025). This review article presents different sequence-based techniques of modeling and designing enhancers.Article Google Scholar Pampari, A. et al. ChromBPNet: bias factorized, base-resolution deep learning models of chromatin accessibility reveal cis-regulatory sequence syntax, transcription factor footprints and regulatory variants. Preprint at bioRxiv https://doi.org/10.1101/2024.12.25.630221 (2025). This paper reports on an alternative method of modeling enhancer codes from accessibility data.Linder, J. et al. Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation. Nat. Genet. 57, 949–961 (2025). This paper presents a way of modeling transcriptomic and epigenomic assays at the gene-locus level.Article CAS PubMed PubMed Central Google Scholar Preissl, S. et al. Characterizing cis-regulatory elements using single-cell epigenomics. Nat. Rev. Genet. 24, 21–43 (2023). This review article highlights different methods of characterizing chromatin accessibility.Article CAS PubMed Google Scholar Johansen, N. J. et al. Evaluating methods for the prediction of cell-type-specific enhancers in the mammalian cortex. Cell Genom 5, 100879 (2025). This paper highlights the functionality of sequence-based modeling in predicting in vivo enhancer activity in the mouse cortex.Article CAS PubMed PubMed Central Google Scholar Download references