<1> Lysine Ubiquitination Resource

(1). PLMD: A comprehensive database for experimentally identified protein lysine modifications (PLMs) (Xu, et al., 2017).

<2> Ubiquitination Prediction Tools

(1). UbiPred: A tool for protein ubiquitination sites prediction using SVM algorithm which combined 31 physicochemical properties (Tung, et al., 2008).

(2). UbPred: A predictor for protein ubiquitination sites based on random forest algorithm (Radivojac, et al., 2010).

(3). UbSite: A method to identify ubiquitination sites using a radial basis function (RBF) network (Lee, et al., 2011).

(4). CKSAAP_UbSite: A New predictor for ubiquitination sites according to the composition of k-spaced amino acid pairs (Chen, et al., 2011).

(5). WPNNA: A new method to identify ubiquitination sites using weighted passive nearest neighbor algorithm (WPNNA) classifier (Feng, et al., 2013).

(6). UbiProber : Prediction for ubiquitination sites with the assistance of machine-learning approaches (Chen, et al., 2013).

(7). hCKSAAP_UbSite: An improved predictor for human protein ubiquitnation by using amino acid pattern and properties (Chen, et al., 2013).

(8). RUBI: A sequence-based ubiquitination prediction tool for large-scale ubiquitination sites (Walsh, et al., 2014).

(9). ModPred: A standalone tool to predict PTM sites on single and multiple sequences (Pejaver, et al., 2014).

(10). iUbiq-Lys: Prediction of lysine ubiquitination sites according to the combination of pseudo-amino acid composition, the evolutionary information and gray system model by SVM (Qiu, et al., 2015).

(11). UbiSite: A two-layered SVM model integration in protein ubiquitination prediction (Huang, et al., 2016).

(12). ESA-UbiSite: A predictor for ubiquitination sites by an evolutionary screening algorithm (ESA) applied in negtive samples (Wang, et al., 2017).

(13). PTM-ssMP: An online server for the prediction of multiple PTM sites, which realized by a SVM classifier (Liu, et al., 2018).

(14). PTMscape: A R package to predict the sites of vairous PTMs (Li, et al., 2018).

(15). deepUbiquitylation: Prediction of lysine ubiquitination sites by a large-scale training data via multiple-layer networks (He, et al., 2018).

(16). DeepUbi: Prediction of lysine ubiquitination sites by a deep learning framework (Fu, et al., 2019).

(17). MUscADEL: A comperihensive computational tool for the prediction of multiple lysine PTMs (Chen, et al., 2019).

(18). DL-plant-ubsites-prediction: A tool identifying protein lysine ubiquitination sites in plants via the multilayer convolutional neural network (Wang, et al., 2020).

(19). MusiteDeep: A comprehensive web service for general PTM site prediction (Wang, et al., 2021).

(20). DeepTL-Ubi: A deep transfer learning frame network to predict lysine ubiquitination sites in several species (Yang, et al., 2021).

(21). MultiLyGAN: A multi-classification pipeline to predict different types of lysine modified sites (Liu, et al., 2021).

(22). UbiSite-XGBoost: A ubiquitination sites predictor implemented by XGBoost with the integration of multiple features (Liu, et al., 2021).

(23). UbiComb: An open resource to predict ubiquitination sites in plants by long short-term memory (LSTM) followed by a max-pooling layer (Siraj, et al., 2021).

(24). CNNAthUbi: Computational prediction of ubiquitination sites in Arabidopsis thaliana by convolutional neural networks (Wang, et al., 2021).