The Prominent Characteristics of the Effective sgRNA for a Precise CRISPR Genome Editing

Clustered regularly interspaced short palindromic repeats (CRISPRs) technique is the most effective and novelist technique for genome editing. CRISPR mechanism has been widely developed for gene editing, gene silencing, high-specific regulation of the transcription, and reducing off-target effects through double-strand breaks (DSBs) in the genomic DNA and then modifying nucleotide sequences of the target gene in diverse plant and animal species. However, the application may be restricted by a high rate of off-target effects. So, there are many studies on designing precise single-guide RNAs (sgRNAs) to minimize off-target effects. Thus, the high-efficiency design of a specific sgRNA is critical. First, in the chapter, the sgRNA origin and different types of gRNA will be outlined. Then, the off-target effect will be described. Next, the remarkable characteristics of the sgRNA will be highlighted to improve precise gene editing. Finally, some popular in silico tools will be introduced for designing sgRNA

Predicting Off-target Effects in CRISPR-Cas9 System using Graph Convolutional Network

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