CREsted (Cis-Regulatory Element Sequence Training, Explanation, and Design) is an easy-to-use deep learning package for training enhancer models on single-cell ATAC sequencing (scATAC-seq) data. CREsted provides comprehensive analyses and tutorials to study enhancer codes and the ability to design synthetic enhancer sequences at a cell type-specific, nucleotide-level resolution. Integrated into the scverse framework, CREsted is compatible with outcomes from established scATAC-seq processing tools. It employs novel scATAC-seq preprocessing techniques, such as peak height normalization across cell types, offers flexibility and variety in deep learning modeling architectures and tasks, and contains thorough analysis of the cell type-specific enhancer codes captured during modeling that can also be used for the design of synthetic sequences. PyPI link