Analysis and Comparison of RNA Pseudouridine Site Prediction Tools

Pseudouridines in RNAs: switching atoms means shifting paradigms

The structure, stability, and function of various coding and noncoding RNAs are influenced by chemical modifications. Pseudouridine (Ψ) is one of the most abundant post‐transcriptional RNA base modifications and has been detected at individual positions in tRNAs, rRNAs, mRNAs, and snRNAs, which are referred to as Ψ‐sites. By allowing formation of additional bonds with neighboring atoms, Ψ strengthens RNA–RNA and RNA–protein interactions. Although many aspects of the underlying modification reactions remain unclear, the advent of new transcriptome‐wide methods to quantitatively detect Ψ‐sites has recently changed our perception of the functional roles and importance of Ψ. For instance, it is now clear that the occurrence of Ψs appears to be directly linked to the lifetime and the translation efficiency of a given mRNA molecule. Furthermore, the administration of Ψ‐containing RNAs reduces innate immune responses, which appears strikingly advantageous for the development of generations of mRNA‐based vaccines. In this review, we aim to comprehensively summarize recent discoveries that highlight the impact of Ψ on various types of RNAs and outline possible novel biomedical applications of Ψ.

Recent Advances in Predicting Protein S-Nitrosylation Sites

Protein S-nitrosylation (SNO) is a process of covalent modification of nitric oxide (NO) and its derivatives and cysteine residues. SNO plays an essential role in reversible posttranslational modifications of proteins. The accurate prediction of SNO sites is crucial in revealing a certain biological mechanism of NO regulation and related drug development. Identification of the sites of SNO in proteins is currently a very hot topic. In this review, we briefly summarize recent advances in computationally identifying SNO sites. The challenges and future perspectives for identifying SNO sites are also discussed. We anticipate that this review will provide insights into research on SNO site prediction.

Prediction of N7-methylguanosine sites in human RNA based on optimal sequence features

N-7 methylguanosine (m7G) modification is a ubiquitous post-transcriptional RNA modification which is vital for maintaining RNA function and protein translation. Developing computational tools will help us to easily predict the m7G sites in RNA sequence. In this work, we designed a sequence-based method to identify the modification site in human RNA sequences. At first, several kinds of sequence features were extracted to code m7G and non-m7G samples. Subsequently, we used mRMR, F-score, and Relief to obtain the optimal subset of features which could produce the maximum prediction accuracy. In 10-fold cross-validation, results showed that the highest accuracy is 94.67% achieved by support vector machine (SVM) for identifying m7G sites in human genome. In addition, we examined the performances of other algorithms and found that the SVM-based model outperformed others. The results indicated that the predictor could be a useful tool for studying m7G. A prediction model is available at https://github.com/MapFM/m7g_model.git.