Predicting Off-target Effects in CRISPR-Cas9 System using Graph Convolutional Network
| dc.contributor.author | Vinodkumar, Prasoon Kumar | |
| dc.contributor.other | Tartu Ülikool. Loodus- ja täppisteaduste valdkond | et |
| dc.contributor.other | Tartu Ülikool. Tehnoloogiainstituut | et |
| dc.date.accessioned | 2021-07-01T11:30:48Z | |
| dc.date.available | 2021-07-01T11:30:48Z | |
| dc.date.issued | 2021 | |
| dc.description.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. | et |
| dc.identifier.uri | http://hdl.handle.net/10062/72919 | |
| dc.language.iso | eng | et |
| dc.publisher | Tartu Ülikool | et |
| dc.rights | openAccess | et |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.subject | CRISPR-Cas9 | et |
| dc.subject | Off-target | et |
| dc.subject | Synthetic Biology | et |
| dc.subject | Deep Learning | et |
| dc.subject | Graph Neural Network | et |
| dc.subject | Link Prediction | et |
| dc.subject.other | magistritööd | et |
| dc.title | Predicting Off-target Effects in CRISPR-Cas9 System using Graph Convolutional Network | et |
| dc.type | Thesis | et |