Vinodkumar, Prasoon KumarTartu Ülikool. Loodus- ja täppisteaduste valdkondTartu Ülikool. Tehnoloogiainstituut2021-07-012021-07-012021http://hdl.handle.net/10062/72919CRISPR-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.engopenAccessAttribution-NonCommercial-NoDerivatives 4.0 InternationalCRISPR-Cas9Off-targetSynthetic BiologyDeep LearningGraph Neural NetworkLink PredictionmagistritöödThesis