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

Abstract

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.

Automatic building of annotated image datasets for training neural networks

“…The last group of methods is based on machine learning methods 5,6 and can automatically segment a point cloud based on various features, such as geometric characteristics, color, textures, etc. These features allow you to identify various objects and backgrounds in the image.…”

Ensemble Approach for Detection of Depression Using EEG Features

“…One of the most widely used methods to analyse EEG signals is to decompose the signal into functionally distinct frequency bands, such as delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45). In the current study, this was achieved by first calculating the power spectral density of the EEG signal by Welch's method, as done by Bachmann et al 2018.…”

“…Out of these techniques, EEG stands out as the simplest and most cost effective. Hence, detecting mental states and disorders by using various EEG feature representations, such as methods based on fast Fourier transform (FFT), discrete wavelet transform (DWT), power spectral analysis (PSA), and others [ 8 , 9 , 10 , 11 , 12 , 13 ], is an actively researched field showing promising results. Various advanced machine learning algorithms have been utilised in order to analyse different modalities of such data in order to introduce automated assessment of depression [ 13 , 14 , 15 , 16 , 17 , 18 , 19 ].…”

Deep convolutional neural networks for detection of abnormalities in chest X-rays trained on the very large dataset

“…Apostolopoulous et al [9] created a MobileNet CNN model utilizing extricated features. Various other methods, such as InceptionV3, ResNet50, GCN, and Inception-ResNetV2, were used for classification [10][11][12][13]. In [14], a transfer learning-based method was employed in order to classify existence or absence of COVID-19 chest X-ray pictures utilizing three models such as ResNet18, ResNet50, SqueezeNet, and DenseNet121.…”