PlantDeepSEA, a deep learning-based web service to predict the regulatory effects of genomic variants in plants

Abstract Characterizing regulatory effects of genomic variants in plants remains a challenge. Although several tools based on deep-learning models and large-scale chromatin-profiling data have been available to predict regulatory elements and variant effects, no dedicated tools or web services have been reported in plants. Here, we present PlantDeepSEA as a deep learning-based web service to predict regulatory effects of genomic variants in multiple tissues of six plant species (including four crops). PlantDeepSEA provides two main functions. One is called Variant Effector, which aims to predict the effects of sequence variants on chromatin accessibility. Another is Sequence Profiler, a utility that performs ‘in silico saturated mutagenesis’ analysis to discover high-impact sites (e.g., cis-regulatory elements) within a sequence. When validated on independent test sets, the area under receiver operating characteristic curve of deep learning models in PlantDeepSEA ranges from 0.93 to 0.99. We demonstrate the usability of the web service with two examples. PlantDeepSEA could help to prioritize regulatory causal variants and might improve our understanding of their mechanisms of action in different tissues in plants. PlantDeepSEA is available at http://plantdeepsea.ncpgr.cn/. Graphical Abstract Graphical Abstract PlantDeepSEA integrates ATAC-seq data and genomic sequences based on a deep learning approach to predict the regulatory effects of genomic variants in multiple tissues of six plant species. It provides two main functions: "Variant Effector" to annotate the effects of genomic variants on chromatin accessibility, and "Sequence Profiler" to discover high-impact sites.