Modeling transcriptional regulation of model species with deep learning

To enable large-scale analyses of transcription regulation in model species, we developed DeepArk, a set of deep learning models of the cis-regulatory activities for four widely studied species: Caenorhabditis elegans, Danio rerio, Drosophila melanogaster, and Mus musculus. DeepArk accurately predicts the presence of thousands of different context-specific regulatory features, including chromatin states, histone marks, and transcription factors. In vivo studies show that DeepArk can predict the regulatory impact of any genomic variant (including rare or not previously observed) and enables the regulatory annotation of understudied model species.

Automated summary

DeepArk is a set of deep learning models for model species, accurately predicting various context-specific regulatory features and enabling the prediction of regulatory impact of genomic variants, even rare or previously unobserved ones, as well as facilitating regulatory annotation of understudied model species through in vivo studies.