首页|RegVar:Tissue-specific Prioritization of Non-coding Regulatory Variants
RegVar:Tissue-specific Prioritization of Non-coding Regulatory Variants
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
万方数据
维普
Non-coding genomic variants constitute the majority of trait-associated genome varia-tions;however,the identification of functional non-coding variants is still a challenge in human genetics,and a method for systematically assessing the impact of regulatory variants on gene expression and linking these regulatory variants to potential target genes is still lacking.Here,we introduce a deep neural network(DNN)-based computational framework,RegVar,which can accurately predict the tissue-specific impact of non-coding regulatory variants on target genes.We show that by robustly learning the genomic characteristics of massive variant-gene expression associations in a variety of human tissues,RegVar vastly surpasses all current non-coding variant prioritization methods in predicting regulatory variants under different circumstances.The unique features of RegVar make it an excellent framework for assessing the regulatory impact of any vari-ant on its putative target genes in a variety of tissues.RegVar is available as a web server at https://regvar.omic.tech/.
万方数据
维普
