Prediction of sgRNA Off-Target Activity in CRISPR/Cas9 Gene Editing Using Graph Convolution Network

CRISPR/Cas9 is a powerful genome-editing technology that has been widely applied in targeted 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 to predict the off-target propensity of single guide RNA (sgRNA) at specific DNA fragments by using artificial feature extract operations and machine learning techniques; however, this is a convoluted process that is difficult to understand and implement for researchers. In this research work, we introduce a novel graph-based approach to predict off-target efficacy of sgRNA in the CRISPR/Cas9 system that is easy to understand and replicate for researchers. This is achieved by creating a graph with sequences as nodes and by using a link prediction method 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. We used HEK293 and K562 t datasets in our experiments. GCN predicted the off-target gene knockouts (using link prediction) by predicting the links between sgRNA and off-target sequences with an auROC value of 0.987.

Zinc-finger exercisesThe ‘cutting edge’ of gene therapy … literally

The traditional gene-therapy approach relies on the delivery of a therapeutic transgene into a cell, typically to compensate for a gene that is not functional owing to a genetic defect. But why not just correct the genetic defects directly? The answer used to be that there was no methodology for making precise genetic modifications in a highly efficient manner. That is changing now. Over the last decade, researchers have devised a way to stimulate the natural DNA-repair mechanisms of the cell to occur at any desired site in the genome. The enabling technological advance has been the development of programmable nucleases, which use re-engineered ZF (zinc-finger) DNA-binding domains to cut the DNA in a living cell at a precise user-defined location. These methods have been shown to produce genetic modifications at frequencies of >1 correct event per ten treated cells, representing a 100 000-fold stimulation of targeted gene repair. The first Phase I clinical trial of a therapeutic ZFN (zinc-finger nuclease) is scheduled for 2008.