Deep Learning Approach to Genomic Breakage Study from Primary Sequence

SUMMARY Identifying the molecular mechanisms related to genomic breakage is an important goal of cancer mechanism studies. Among the diverse location of the breakpoints of structural variants, the fusion genes, which have the breakpoints in the gene bodies and typically identified from RNA-seq data, can provide a highlighted structural variant resource for studying the genomic breakages with expression and potential pathogenic impacts. In this study, we developed FusionAI which utilizes deep learning to predict gene fusion breakpoints based on primary sequences and let us identify fusion breakage code and genomic context. FusionAI leverages the known fusion breakpoints to provide a prediction model of the fusion genes from the primary genomic sequences via deep learning, thereby helping researchers a more accurate selection of fusion genes and better understand genomic breakage. Highlights FusionAI, a 9-layer deep neural network, predicts fusion gene breakpoints from a DNA sequence FusonAI reduce the cost and effort for validating fusion genes by decreasing specificity High feature importance scored regions were apart 100nt on average from the exon junction breakpoints High feature importance scored regions overlapped with 44 different human genomic features Transcription factor fusion genes are targeted by the GC-rich motif TFs FusionAI gives less scores to the non-disease derived breakpoints