DeepDualEnhancer: A Dual-Feature Input DNABert Based Deep Learning Method for Enhancer Recognition

Enhancers are cis-regulatory DNA sequences that are widely distributed throughout the genome. They can precisely regulate the expression of target genes. Since the features of enhancer segments are difficult to detect, we propose DeepDualEnhancer, a DNABert-based method using a multi-scale convoluti...

Full description

Saved in:
Bibliographic Details
Published in:International journal of molecular sciences Vol. 25; no. 21; p. 11744
Main Authors: Song, Tao, Song, Haonan, Pan, Zhiyi, Gao, Yuan, Dai, Huanhuan, Wang, Xun
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 01.11.2024
MDPI
Subjects:
Algorithms
Bioinformatics
Computational Biology - methods
Datasets
Deep Learning
DNA methylation
Enhancer Elements, Genetic - genetics
Gene expression
Genetic aspects
Genomes
Genomics - methods
Humans
Identification and classification
Machine learning
Neural networks
Neural Networks, Computer
Nucleotide sequence
Promoter Regions, Genetic
Sequence Analysis, DNA - methods
Support vector machines
China
deep learning
DNABert
enhancer
genomic signal
ISSN:1422-0067, 1661-6596, 1422-0067
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Enhancers are cis-regulatory DNA sequences that are widely distributed throughout the genome. They can precisely regulate the expression of target genes. Since the features of enhancer segments are difficult to detect, we propose DeepDualEnhancer, a DNABert-based method using a multi-scale convolutional neural network, BiLSTM, for enhancer identification. We first designed the DeepDualEnhancer method based only on the DNA sequence input. It mainly consists of a multi-scale Convolutional Neural Network, and BiLSTM to extract features by DNABert and embedding, respectively. Meanwhile, we collected new datasets from the enhancer–promoter interaction field and designed the method DeepDualEnhancer-genomic for inputting DNA sequences and genomic signals, which consists of the transformer sequence attention. Extensive comparisons of our method with 20 other excellent methods through 5-fold cross validation, ablation experiments, and an independent test demonstrated that DeepDualEnhancer achieves the best performance. It is also found that the inclusion of genomic signals helps the enhancer recognition task to be performed better.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1422-0067
1661-6596
1422-0067
DOI:10.3390/ijms252111744