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.
|Titolo:||CRISPRLearner: a deep learning-based system to predict CRISPR/Cas9 sgRNA on-target cleavage efficiency|
DIMAURO, Giovanni (Corresponding)
|Data di pubblicazione:||2019|
|Appare nelle tipologie:||1.1 Articolo in rivista|
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|CRISPRLearner- A Deep Learning-Based System to Predict CRISPR-Cas9 sgRNA On-Target Cleavage Efficiency.pdf||Articolo principale||Documento in Post-print||Open Access Visualizza/Apri|