EMO: Predicting Non-coding Mutation-induced Up- and Down-regulation of Risk Gene Expression using Deep Learning

The challenge of understanding how alterations in non-coding DNA regulate gene expression is substantial, with far-reaching consequences for the advancement of human genetics and disease research. Accurately predicting the up- and down-regulation of gene expression quantitative trait loci (eQTLs) offers a potential avenue to accelerate the identification of associations between non-coding variants and phenotypic traits. However, current methods for predicting the impact of non-coding mutations on gene expression changes fail to predict the sign of eQTLs accurately. Additionally, the requirement for tissue-specific training models within these methods restricts their applicability, especially when extending predictive abilities to single-cell resolution. In this study, we present EMO, an innovative transformer-based pre-trained method, designed to predict the up- and down-regulation of gene expression caused by single non-coding mutations using DNA sequences and ATAC-seq data. EMO extends the effective prediction range up to 1Mbp between the non-coding mutation and the transcription start site (TSS) of the target gene. It demonstrates competitive prediction performance across various variant TSS distances and surpasses the state-of-the-art structure. To assess its effectiveness, EMO was fine-tuned using eQTLs from two brain tissues for external validation. We also evaluated EMOs transferability to single-cell resolution by fine-tuning it on eQTLs from six types of immune cells, achieving satisfactory results in each cell type (AUC > 0.860). Furthermore, EMO displayed promising potential in analyzing disease-associated eQTLs.