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A functional genomics atlas enhanced by convolutional neural networks facilitates clinical interpretation of disease relevant variants in non-coding regulatory elements [ATAC-seq]

GSE263338 Homo sapiens Genome binding/occupancy profiling by high throughput sequencing 4 samples Submitted 2025/11/18 Platform GPL16791
Summary
We provide a novel functional genomics atlas of non-coding regulatory elements derived from human neural stem cells (NSCs), using the massively parallel reporter assay ChIP-STARR-seq. ChIP-STARR-seq was performed in NSCs using chromatin immunoprecipitation for H3K27ac, H3K4me1, YY1 and SOX2, and the generated STARR-seq plasmid reporter libraries were profiled for their enhancer activity in H9 derived NSCs and human embryonic stem cells. Subsequently a multi-omics data integration was performed, including the development of a convolutional neural network called BRAIN-MAGNET that allows to predict the effect of single nucleotide variants in non-coding regulatory elements on the enhancer activity. The various data sets generated in this study include ChIP-seq experiments in NSCs, DNA sequencing of the generated plasmid libraries, RNA-seq of transfected cells from the ChIP-STARR-seq experiments, generated ATAC-seq data sets used for data integration and the contribution scores from BRAIN-MAGNET for each nucleotide assessed in this ChIP-STARR-seq study.
Published in
BRAIN-MAGNET: A functional genomics atlas for interpretation of non-coding variants
Deng R, Perenthaler E, Nikoncuk A et al. · Cell 2026 · PMID 41265437 · doi:10.1016/j.cell.2025.10.029
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Also filed as BioProject PRJNA1096736 and SRA study SRP500034. Searching any of these in the dataset finder brings you back here.

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