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dc.contributor.authorVinodkumar, Prasoon Kumar
dc.contributor.otherTartu Ülikool. Loodus- ja täppisteaduste valdkondet
dc.contributor.otherTartu Ülikool. Tehnoloogiainstituutet
dc.description.abstractCRISPR-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
dc.publisherTartu Ülikoolet
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.subjectSynthetic Biologyet
dc.subjectDeep Learninget
dc.subjectGraph Neural Networket
dc.subjectLink Predictionet
dc.titlePredicting Off-target Effects in CRISPR-Cas9 System using Graph Convolutional Networket

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