+ Site Statistics
+ Search Articles
+ PDF Full Text Service
How our service works
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ Translate
+ Recently Requested

Chromatin accessibility prediction via a hybrid deep convolutional neural network



Chromatin accessibility prediction via a hybrid deep convolutional neural network



Bioinformatics 34(5): 732-738



A majority of known genetic variants associated with human-inherited diseases lie in non-coding regions that lack adequate interpretation, making it indispensable to systematically discover functional sites at the whole genome level and precisely decipher their implications in a comprehensive manner. Although computational approaches have been complementing high-throughput biological experiments towards the annotation of the human genome, it still remains a big challenge to accurately annotate regulatory elements in the context of a specific cell type via automatic learning of the DNA sequence code from large-scale sequencing data. Indeed, the development of an accurate and interpretable model to learn the DNA sequence signature and further enable the identification of causative genetic variants has become essential in both genomic and genetic studies. We proposed Deopen, a hybrid framework mainly based on a deep convolutional neural network, to automatically learn the regulatory code of DNA sequences and predict chromatin accessibility. In a series of comparison with existing methods, we show the superior performance of our model in not only the classification of accessible regions against background sequences sampled at random, but also the regression of DNase-seq signals. Besides, we further visualize the convolutional kernels and show the match of identified sequence signatures and known motifs. We finally demonstrate the sensitivity of our model in finding causative noncoding variants in the analysis of a breast cancer dataset. We expect to see wide applications of Deopen with either public or in-house chromatin accessibility data in the annotation of the human genome and the identification of non-coding variants associated with diseases. Deopen is freely available at https://github.com/kimmo1019/Deopen. ruijiang@tsinghua.edu.cn. Supplementary data are available at Bioinformatics online.

Please choose payment method:






(PDF emailed within 0-6 h: $19.90)

Accession: 059498767

Download citation: RISBibTeXText

PMID: 29069282

DOI: 10.1093/bioinformatics/btx679


Related references

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction. Sensors 17(4):, 2017

A generic deep convolutional neural network framework for prediction of receptor-ligand interactions-NetPhosPan: application to kinase phosphorylation prediction. Bioinformatics 35(7): 1098-1107, 2019

Prediction of CRISPR sgRNA Activity Using a Deep Convolutional Neural Network. Journal of Chemical Information and Modeling 59(1): 615-624, 2019

DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucleic Acids Research 44(11): E107, 2016

Incorporating dosimetric features into the prediction of 3D VMAT dose distributions using deep convolutional neural network. Physics in Medicine and Biology 64(12): 125017, 2019

A Cascaded Deep Convolutional Neural Network for Joint Segmentation and Genotype Prediction of Brainstem Gliomas. IEEE Transactions on Bio-Medical Engineering 65(9): 1943-1952, 2018

Dose distribution prediction in isodose feature-preserving voxelization domain using deep convolutional neural network. Medical Physics 46(7): 2978-2987, 2019

Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. Journal of Periodontal and Implant Science 48(2): 114-123, 2018

Prediction of RNA-protein interactions by combining deep convolutional neural network with feature selection ensemble method. Journal of Theoretical Biology 461: 230-238, 2019

Protein Solvent-Accessibility Prediction by a Stacked Deep Bidirectional Recurrent Neural Network. Biomolecules 8(2):, 2018

Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study. Physics in Medicine and Biology 62(21): 8246-8263, 2017

Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs. Computers in Biology and Medicine 103: 220-231, 2018

A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction. Bmc Bioinformatics 18(Suppl 16): 569, 2017

Chromatin accessibility prediction via convolutional long short-term memory networks with k-mer embedding. Bioinformatics 33(14): I92, 2017

Bearing Fault Diagnosis with a Feature Fusion Method Based on an Ensemble Convolutional Neural Network and Deep Neural Network. Sensors 19(9):, 2019