A new method for enhancer prediction based on deep belief network

• Published in: BMC bioinformatics Vol. 18; no. Suppl 12; p. 418
• Main Authors: Bu, Hongda, Gan, Yanglan, Wang, Yang, Zhou, Shuigeng, Guan, Jihong
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 16.10.2017; BioMed Central; BMC
• Subjects: Algorithms; Analysis; Belief networks; Bioinformatics; Cell culture; Chip-seq; Chromatin; Computation; Computational Biology - methods; Computer applications; Databases, Genetic; Deep belief network; Deoxyribonucleic acid; DNA; DNA methylation; DNA sequencing; Enhancer Elements, Genetic; Enhancer prediction; Enhancers; Epigenetic inheritance; Epigenetics; Gene expression; Gene regulation; Genomes; Humans; Machine learning; Mathematical models; Methods; Nucleotide sequence; Performance prediction; Regulatory sequences; ROC Curve; Stem cells; Deep belief network; Enhancer prediction; Chip-seq
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-017-1828-0

Studies 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. Here, 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. Deep learning is effective in boosting the performance of enhancer prediction.