+ 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

Prediction of CRISPR sgRNA Activity Using a Deep Convolutional Neural Network

Prediction of CRISPR sgRNA Activity Using a Deep Convolutional Neural Network

Journal of Chemical Information and Modeling 59(1): 615-624

The CRISPR-Cas9 system derived from adaptive immunity in bacteria and archaea has been developed into a powerful tool for genome engineering with wide-ranging applications. Optimizing single-guide RNA (sgRNA) design to improve efficiency of target cleavage is a key step for successful gene editing using the CRISPR-Cas9 system. Because not all sgRNAs that cognate to a given target gene are equally effective, computational tools have been developed based on experimental data to increase the likelihood of selecting effective sgRNAs. Despite considerable efforts to date, it still remains a big challenge to accurately predict functional sgRNAs directly from large-scale sequence data. We propose DeepCas9, a deep-learning framework based on the convolutional neural network (CNN), to automatically learn the sequence determinants and further enable the identification of functional sgRNAs for the CRISPR-Cas9 system. We show that the CNN method outperforms previous methods in both (i) the ability to correctly identify highly active sgRNAs in experiments not used in the training and (ii) the ability to accurately predict the target efficacies of sgRNAs in different organisms. Besides, we further visualize the convolutional kernels and show the match of identified sequence signatures and known nucleotide preferences. We finally demonstrate the application of our method to the design of next-generation genome-scale CRISPRi and CRISPRa libraries targeting human and mouse genomes. We expect that DeepCas9 will assist in reducing the numbers of sgRNAs that must be experimentally validated to enable more effective and efficient genetic screens and genome engineering. DeepCas9 can be freely accessed via the Internet at https://github.com/lje00006/DeepCas9 .

Please choose payment method:

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

Accession: 065885015

Download citation: RISBibTeXText

PMID: 30485088

DOI: 10.1021/acs.jcim.8b00368

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

Chromatin accessibility prediction via a hybrid deep convolutional neural network. Bioinformatics 34(5): 732-738, 2018

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

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

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

Detection of Bleeding Events in Electronic Health Record Notes Using Convolutional Neural Network Models Enhanced With Recurrent Neural Network Autoencoders: Deep Learning Approach. Jmir Medical Informatics 7(1): E10788, 2019

Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network. Plos one 13(1): E0191493, 2018

Deep learning-based detection and classification of geographic atrophy using a deep convolutional neural network classifier. Graefe's Archive for Clinical and Experimental Ophthalmology 256(11): 2053-2060, 2018

Prediction of binding property of RNA-binding proteins using multi-sized filters and multi-modal deep convolutional neural network. Plos one 14(4): E0216257, 2019