FusionAI: Predicting fusion breakpoint from DNA sequence with deep learning

被引:10
|
作者
Kim, Pora [1 ]
Tan, Hua [1 ]
Liu, Jiajia [1 ,4 ]
Yang, Mengyuan [1 ,5 ]
Zhou, Xiaobo [1 ,2 ,3 ]
机构
[1] Univ Texas Hlth Sci Ctr Houston, Sch Biomed Informat, Houston, TX 77030 USA
[2] Univ Texas Hlth Sci Ctr Houston, McGovern Med Sch, Houston, TX 77030 USA
[3] Univ Texas Hlth Sci Ctr Houston, Sch Dent, Houston, TX 77030 USA
[4] Tongji Univ, Coll Elect & Informat Engn, Shanghai 201804, Peoples R China
[5] Tongji Univ, Sch Life Sci & Technol, Shanghai 200092, Peoples R China
基金
美国国家卫生研究院;
关键词
GENE FUSIONS; SITES;
D O I
10.1016/j.isci.2021.103164
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:19
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