Predicting enhancer-promoter interaction from genomic sequence with deep neural networks

被引:83
作者
Singh, Shashank [1 ]
Yang, Yang [2 ]
Poczos, Barnabas [1 ]
Ma, Jian [2 ]
机构
[1] Carnegie Mellon Univ, Sch Comp Sci, Machine Learning Dept, Pittsburgh, PA 15213 USA
[2] Carnegie Mellon Univ, Sch Comp Sci, Computat Biol Dept, Pittsburgh, PA 15213 USA
基金
美国国家科学基金会; 美国国家卫生研究院;
关键词
chromatin interaction; enhancer-promoter interaction; deep neural network; LONG-RANGE INTERACTIONS; CHROMATIN; BINDING; SITES; PRINCIPLES; INITIATION; LANDSCAPE; TOPOLOGY; PROTEINS; DNA;
D O I
10.1007/s40484-019-0154-0
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
BackgroundIn the human genome, distal enhancers are involved in regulating target genes through proximal promoters by forming enhancer-promoter interactions. Although recently developed high-throughput experimental approaches have allowed us to recognize potential enhancer-promoter interactions genome-wide, it is still largely unclear to what extent the sequence-level information encoded in our genome help guide such interactions.MethodsHere we report a new computational method (named "SPEID") using deep learning models to predict enhancer-promoter interactions based on sequence-based features only, when the locations of putative enhancers and promoters in a particular cell type are given.ResultsOur results across six different cell types demonstrate that SPEID is effective in predicting enhancer-promoter interactions as compared to state-of-the-art methods that only use information from a single cell type. As a proof-of-principle, we also applied SPEID to identify somatic non-coding mutations in melanoma samples that may have reduced enhancer-promoter interactions in tumor genomes.ConclusionsThis work demonstrates that deep learning models can help reveal that sequence-based features alone are sufficient to reliably predict enhancer-promoter interactions genome-wide.
引用
收藏
页码:122 / 137
页数:16
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