A new method for enhancer prediction based on deep belief network

BMC Bioinformatics

Research

Jihong Guan¹  Hongda Bu¹  Yanglan Gan²  Yang Wang³  Shuigeng Zhou⁴ 

[1] Department of Computer Science and Technology, Tongji University, 4800 Cao’an Road, 201804, Shanghai, China;School of Computer, Donghua University, 2999 Renming North Road, 201620, Shanghai, China;School of Software, Jiangxi Normal University, 99 Ziyang Avenue, 330022, Jiangxi, China;Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, 220 Handan Road, 200433, Shanghai, China;The Bioinformatics Lab at Changzhou NO. 7 People’s Hospital, Changzhou, 213011, Jiangsu, China;

关键词: Enhancer prediction;    Chip-seq;    Deep belief network;   

DOI  :  10.1186/s12859-017-1828-0

来源: Springer

【 摘 要 】

BackgroundStudies have shown that enhancers are significant regulatory elements to play crucial roles in gene expression regulation. Since enhancers are unrelated to the orientation and distance to their target genes, it is a challenging mission for scholars and researchers to accurately predicting distal enhancers. In the past years, with the high-throughout ChiP-seq technologies development, several computational techniques emerge to predict enhancers using epigenetic or genomic features. Nevertheless, the inconsistency of computational models across different cell-lines and the unsatisfactory prediction performance call for further research in this area.ResultsHere, we propose a new Deep Belief Network (DBN) based computational method for enhancer prediction, which is called EnhancerDBN. This method combines diverse features, composed of DNA sequence compositional features, DNA methylation and histone modifications. Our computational results indicate that 1) EnhancerDBN outperforms 13 existing methods in prediction, and 2) GC content and DNA methylation can serve as relevant features for enhancer prediction.ConclusionDeep learning is effective in boosting the performance of enhancer prediction.

【 授权许可】

CC BY   
© The Author(s) 2017