Predicting functional elements and variants effects in non-coding regions based on deep learning

Accurate recognition and annotation of the important functional elements in the genome is an important prerequisite to understand the coding mode of complex regulatory networks in the one-dimensional genome. Despite rapid advances in sequencing and recognition technologies, accurately calling non-coding variant effects from large-scale sequence reads remains challenging. Here we present a deep neural network-based algorithmic framework, DeepMSA, which directly learns a regulatory sequence code from large-scale chromatin-profiling data,enabling to evaluate chromatin effects caused by SNP(single nucleotide polymorphism).