Kim Pora, Tan Hua, Liu Jiajia, Yang Mengyuan, Zhou Xiaobo
School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
College of Electronic and Information Engineering, Tongji University, Shanghai, Shanghai 201804, China.
iScience. 2021 Sep 25;24(10):103164. doi: 10.1016/j.isci.2021.103164. eCollection 2021 Oct 22.
Identifying 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.
识别与基因组断裂相关的分子机制是癌症机制研究的一个重要目标。在结构变异的不同位置中,融合基因在基因体内具有断点,通常可从RNA测序数据的拆分读段中识别出来,它能为研究具有表达和潜在致病影响的基因组断裂提供突出的结构变异资源。在本研究中,我们开发了FusionAI,它利用深度学习基于DNA序列预测基因融合断点,并让我们能够识别融合断裂密码和基因组背景。FusionAI利用已知的融合断点,通过深度学习从原始基因组序列中提供融合基因的预测模型,从而帮助研究人员更准确地选择融合基因,并更好地理解基因组断裂。