A Machine Learning Approach to Identify the Importance of Novel Features for CRISPR/Cas9 Activity Prediction

The reprogrammable CRISPR/Cas9 genome editing tool's growing popularity is hindered by unwanted off-target effects. Efforts have been directed toward designing efficient guide RNAs as well as identifying potential off-target threats, yet factors that determine efficiency and off-target activity...

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Bibliographic Details
Published in:Biomolecules (Basel, Switzerland) Switzerland), 2022-08, Vol.12 (8), p.1123
Main Authors: Vora, Dhvani Sandip, Verma, Yugesh, Sundar, Durai
Format: Article
Language:English
Subjects:
Algorithms
binding energy
CRISPR
CRISPR-Cas Systems - genetics
CRISPR/Cas9
Datasets
Deep learning
Design
DNA
Efficiency
Energy
Gene Editing
Genome editing
Genomes
gRNA
Learning algorithms
Machine Learning
Methods
off-targets
Predictions
Proteins
RNA, Guide, CRISPR-Cas Systems - genetics
SHAP values
Thermodynamics
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Summary:The reprogrammable CRISPR/Cas9 genome editing tool's growing popularity is hindered by unwanted off-target effects. Efforts have been directed toward designing efficient guide RNAs as well as identifying potential off-target threats, yet factors that determine efficiency and off-target activity remain obscure. Based on sequence features, previous machine learning models performed poorly on new datasets, thus there is a need for the incorporation of novel features. The binding energy estimation of the gRNA-DNA hybrid as well as the Cas9-gRNA-DNA hybrid allowed generating better performing machine learning models for the prediction of Cas9 activity. The analysis of feature contribution towards the model output on a limited dataset indicated that energy features played a determining role along with the sequence features. The binding energy features proved essential for the prediction of on-target activity and off-target sites. The plateau, in the performance on unseen datasets, of current machine learning models could be overcome by incorporating novel features, such as binding energy, among others. The models are provided on GitHub (GitHub Inc., San Francisco, CA, USA).
ISSN:2218-273X
2218-273X
DOI:10.3390/biom12081123