Predicting Off-target Effects in CRISPR-Cas9 System using Graph Convolutional Network
Date
2021
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Tartu Ülikool
Abstract
CRISPR-Cas9 is a powerful genome editing technology that has been widely applied in target
gene repair and gene expression regulation. One of the main challenges for the CRISPR-Cas9
system is the occurrence of unexpected cleavage at some sites (off-targets) and predicting them
is necessary due to its relevance in gene editing research. Very few deep learning models have
been developed so far that predict the off-target propensity of single guide RNA (sgRNA) at
specific DNA fragments by using artificial feature extract operations and machine learning techniques.
Unfortunately, they implement a convoluted process that is difficult to understand and
implement by researchers. This thesis focuses on developing a novel graph-based approach to
predict off-target efficacy of sgRNA in CRISPR-Cas9 system that is easy to understand and
replicate by researchers. This is achieved by creating a graph with sequences as nodes and by
performing link prediction using Graph Convolutional Network (GCN) to predict the presence
of links between sgRNA and off-target inducing target DNA sequences. Features for the sequences
are extracted from within the sequences.
Description
Keywords
CRISPR-Cas9, Off-target, Synthetic Biology, Deep Learning, Graph Neural Network, Link Prediction