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Lysine crotonylation (Kcr) is one of the most important post-translational modifications (PTMs) that is widely detected in both histone and non-histone proteins. In fact, Kcr is reported to be involved in various biological processes, such as metabolism and cell differentiation. However, the available experimental methods for Kcr site identification are laborious and costly. To effectively replace existing experimental approaches, some computational methods have been developed in the last few years. The available computational methods still lack some important aspects, as they can only identify Kcr sites on either histone-only or combined histone and nonhistone proteins. Although a tool was developed to identify Kcr sites on non-histone proteins only, its performance is inadequate and the exploration of hidden Kcr patterns (motifs) has been completely ignored, which might be significant for detailed Kcr studies. Therefore, algorithms that can more effectively predict Kcr sites on non-histone proteins with their biological meaning need to be designed. Accordingly, we developed a novel deep learning (capsule network)-based model, named CapsNh-Kcr, for Kcr site prediction, particularly focusing on non-histone proteins. Based on the independent results, the proposed model achieves an AUC of 0.9120, which is approximately 6% higher than that of previous nhKcr model in the prediction of Kcr sites on non-histone proteins. Further, we revealed, for the first time, that the proposed model can represent obvious motif distribution across Kcr sites in non-histone proteins. The source code (in Python) is publicly available at https://github.com/Jhabindra-bioinfo/CapsNh-Kcr., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.)

DNA N4-methylcytosine (4mC), an epigenetic modification found in prokaryotic and eukaryotic species, is involved in numerous biological functions, including host defense, transcription regulation, gene expression, and DNA replication. To identify 4mC sites, previous computational studies mostly focused on finding hand-crafted features. This area of research, therefore, would benefit from the development of a computational approach that relies on automatic feature selection to identify relevant sites. We here report 4mC-w2vec, a computational method that learned automatic feature discrimination in the Rosaceae genomes, especially in Rosa chinensis (R. chinensis) and Fragaria vesca (F. vesca) , based on distributed feature representation and through the word embedding technique 'word2vec'. While a few bioinformatics tools are currently employed to identify 4mC sites in these genomes , their prediction performance is inadequate. Our system processed 4mC and non-4mC sites through a word embedding process, including sub-word information of its biological words through k-mer, which then served as features that were fed into a double layer of convolutional neural network (CNN) to classify whether the sample sequences contained 4mCs or non-4mCs sites. Our tool demonstrated performance superior to current tools that use the same genomic datasets. Additionally, 4mC-w2vec is effective for balanced and imbalanced class datasets alike, and the online web-server is currently available at: http://nsclbio.jbnu.ac.kr/tools/4mC-w2vec/., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Authors.)