摘要：SummaryIdentifying the molecular mechanisms related to genomic breakage is an important goal of cancer mechanism studies. Among diverse locations of structural variants, fusion genes, which have the breakpoints in the gene bodies and are typically identified from the split reads of 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 DNA sequence 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.Graphical abstractDisplay OmittedHighlights•FusionAI predicts fusion gene breakpoints from a DNA sequence•FusonAI reduce the effort for validating fusion genes with other tools•High feature importance regions were apart 100nt from the exon junction BPs•High feature importance regions were overlapped with 44 human genomic featuresGenetics; Genomics; Computational bioinformatics; Artificial intelligence applications
