CRISPRLearner: A Deep Learning-Based System to Predict CRISPR/Cas9 sgRNA On-Target Cleavage Efficiency
by
Giovanni Dimauro 1,*, Pierpasquale Colagrande 1, Roberto Carlucci 2, Mario Ventura 2, Vitoantonio Bevilacqua 3 and Danilo Caivano 1
Giovanni Dimauro 1,*, Pierpasquale Colagrande 1, Roberto Carlucci 2, Mario Ventura 2, Vitoantonio Bevilacqua 3 and Danilo Caivano 1
1
Department of Computer Science, University of Bari, 70125 Bari, Italy
2
Department of Biology, University of Bari, 70125 Bari, Italy
3
Department of Electrical and Information Engineering, Polytechnic University of Bari, 70125 Bari, Italy
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(12), 1478; https://doi.org/10.3390/electronics8121478
Received: 3 November 2019 / Revised: 26 November 2019 / Accepted: 30 November 2019 / Published: 4 December 2019
(This article belongs to the Special Issue Computational Intelligence in Healthcare)
CRISPRLearner, the system presented in this paper, makes it possible to predict the on-target cleavage efficiency (also called on-target knockout efficiency) of a given sgRNA sequence, specifying the target genome that this sequence is designed for. After efficiency prediction, the researcher can evaluate its sequence and design a new one if the predicted efficiency is low. CRISPRLearner uses a deep convolutional neural network to automatically learn sequence determinants and predict the efficiency, using pre-trained models or using a model trained on a custom dataset. The convolutional neural network uses linear regression to predict efficiency based on efficiencies used to train the model. Ten different models were trained using ten different gene datasets. The efficiency prediction task attained an average Spearman correlation higher than 0.40. This result was obtained using a data augmentation technique that generates mutations of a sgRNA sequence, maintaining the efficiency value. CRISPRLearner supports researchers in sgRNA design task, predicting a sgRNA on-target knockout efficiency.
Keywords:
convolutional neural network; CRISPR; deep learning
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Dimauro, G.; Colagrande, P.; Carlucci, R.; Ventura, M.; Bevilacqua, V.; Caivano, D. CRISPRLearner: A Deep Learning-Based System to Predict CRISPR/Cas9 sgRNA On-Target Cleavage Efficiency. Electronics 2019, 8, 1478.
Show more citation formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
