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BackgroundPseudouridylation is the most prevalent type of posttranscriptional modification in various stable RNAs of all organisms, which significantly affects many cellular processes that are regulated by RNA. Thus, accurate identification of pseudouridine (Ψ) sites in RNA will be of great benefit for understanding these cellular processes. Due to the low efficiency and high cost of current available experimental methods, it is highly desirable to develop computational methods for accurately and efficiently detecting Ψ sites in RNA sequences. However, the predictive accuracy of existing computational methods is not satisfactory and still needs improvement.ResultsIn this study, we developed a new model, PseUI, for Ψ sites identification in three species, which are H. sapiens, S. cerevisiae, and M. musculus. Firstly, five different kinds of features including nucleotide composition (NC), dinucleotide composition (DC), pseudo dinucleotide composition (pseDNC), position-specific nucleotide propensity (PSNP), and position-specific dinucleotide propensity (PSDP) were generated based on RNA segments. Then, a sequential forward feature selection strategy was used to gain an effective feature subset with a compact representation but discriminative prediction power. Based on the selected feature subsets, we built our model by using a support vector machine (SVM). Finally, the generalization of our model was validated by both the jackknife test and independent validation tests on the benchmark datasets. The experimental results showed that our model is more accurate and stable than the previously published models. We have also provided a user-friendly web server for our model at http://zhulab.ahu.edu.cn/PseUI, and a brief instruction for the web server is provided in this paper. By using this instruction, the academic users can conveniently get their desired results without complicated calculations.ConclusionIn this study, we proposed a new predictor, PseUI, to detect Ψ sites in RNA sequences. It is shown that our model outperformed the existing state-of-art models. It is expected that our model, PseUI, will become a useful tool for accurate identification of RNA Ψ sites.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2321-0) contains supplementary material, which is available to authorized users.

5-methylcytosine (m5C) is one of the most common and abundant post-transcriptional modifications (PTCMs) in RNA. Recent studies showed that m5C plays important roles in many biological functions such as RNA metabolism and cell fate decision. Because most experimental methods that determine m5C sites across the transcriptome are time-consuming and expensive, it is urgent to develop accurate computational methods to identify m5C sites effectively. A benchmark dataset is important for developing and evaluating computational methods. In this work, we constructed four different datasets according to the data redundancy and imbalance. Based on these datasets, we generated three different kinds of features, i.e., KNFs (K-nucleotide frequencies), KSNPFs (K-spaced nucleotide pair frequencies), and pseDNC (pseudo-dinucleotide composition), and then used a support vector machine (SVM) to build our models. Based on the imbalanced and nonredundant dataset, Met935, we extensively studied the three kinds of features and determined an optimal combination of the features. Based on the feature combination, we built models on the three different datasets and compared them with state-of-the-art models. According to the predictive results of the stringent jackknife test, the models based on the three features, 4NF, 1SNPF, and pseDNC, are superior or comparable to other methods. To determine the best model between the models based on the imbalanced dataset Met935 and the balanced dataset Met240, we further evaluated the two models on an independent test set Test1157. Our results demonstrate that the model based on the balanced dataset Met240 achieved the highest recall (68.79%) and the highest Matthews correlation coefficient (MCC) (0.154). In addition, the model is also superior to other state-of-the-art methods according to the integrated parameter MCC on the independent test set. Thus, we selected the model based on Met240 as our final model, which was named RNAm5CPred. In addition, a web server for RNAm5CPred (http://zhulab.ahu.edu.cn/RNAm5CPred/) has been provided to facilitate experimental research.

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The T‑cell immunoglobulin and mucin domain‑containing protein 3 (Tim‑3)/galectin 9 (Gal‑9) pathway, which serves a pivotal role in immune regulation, is similar to the programmed death (PD)‑1/PD‑ligand 1 pathway. Recent evidence has suggested that Tim‑3 is differentially regulated in a variety of tumors and is a potential therapeutic target. The aim of the present study was to evaluate Tim‑3 and Gal‑9 expression and cluster of differentiation (CD)3+, CD8+ and forkhead box (FOX)p3+ T cell tumor‑infiltration in gastric cancer, as well as their impact on prognosis. Tissue samples from 587 patients with gastric cancer were used to create a tissue microarray (TMA). The immune markers Tim‑3, Gal‑9, CD3, CD8 and FOXp3 were immunostained in the TMA, and correlations with clinicopathological findings and prognosis were analyzed. Several Gene Expression Omnibus gastric cancer databases and the K‑M plotter website were used to analyze the association between the expression of Tim‑3, Gal‑9 and CD8A RNA and patient survival. The results demonstrated that Tim‑3 was mainly expressed in immune cells, with minimal expression in gastric cancer cells. Its ligand, Gal‑9, was significantly overexpressed in tumor cells. Tim‑3 and Gal‑9 expression and Foxp3+ T cell density were negatively associated with the patient overall survival (OS) rate. The density of CD8+ T cells was positively associated with the patient OS rate. Tim‑3 expression and CD8+ T cell density were revealed to be independent prognostic factors for patients with gastric cancer.

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