Deep Learning in Epigenomic Data Analysis

Abstract

Epigenomic assays including ChIP-seq, ATAC-seq, bisulfite sequencing, and Hi-C generate genome-wide signal tracks encoding the chromatin landscape that governs gene regulation, and deep learning models that predict epigenomic signals from DNA sequence have transformed the interpretation of non-coding genetic variation, yet adoption of these models varies across analytical task categories. We evaluated 216 deep learning epigenomic programmes across centres in France and Switzerland between 2017 and 2021, spanning five task categories: sequence-to-signal prediction of chromatin marks, regulatory element classification, variant effect prediction on chromatin state, single-cell epigenomic imputation, and three-dimensional genome structure prediction. A Deep Epigenomics Analysis Index (DEAI) was constructed from five sub-scores -- prediction accuracy on held-out genomic regions, biological interpretability of learned features, cross-cell-type generalisation, experimental validation concordance, and computational efficiency -- with weights from regression against sustained adoption into epigenomics research pipelines. DEAI correlated with adoption at r = +0.84 and discriminated adopted from non-adopted methods with an AUC of 0.884. Sequence-to-signal prediction scored highest (mean DEAI 0.826), while 3D genome prediction trailed at 0.600. Only 35.6 percent of programmes exceeded the 0.75 threshold. Prediction accuracy carried the largest regression weight (beta = +0.280), followed by cross-cell-type generalisation (beta = +0.228).