Background: Functional RNA molecules participate in numerous biological processes, ranging from gene regulation to protein synthesis. Analysis of functional RNA motifs and elements in RNA sequences can obtain useful information for deciphering RNA regulatory mechanisms. Our previous work, RegRNA, is widely used in the identification of regulatory motifs, and this work extends it by incorporating more comprehensive and updated data sources and analytical approaches into a new platform. Methods and results: An integrated web-based system, RegRNA 2.0, has been developed for comprehensively identifying the functional RNA motifs and sites in an input RNA sequence. Numerous data sources and analytical approaches are integrated, and several types of functional RNA motifs and sites can be identified by RegRNA 2.0: (i) splicing donor/acceptor sites; (ii) splicing regulatory motifs; (iii) polyadenylation sites; (iv) ribosome binding sites; (v) rho-independent terminator; (vi) motifs in mRNA 5'-untranslated region (5'UTR) and 3'UTR; (vii) AU-rich elements; (viii) C-to-U editing sites; (ix) riboswitches; (x) RNA cis-regulatory elements; (xi) transcriptional regulatory motifs; (xii) user-defined motifs; (xiii) similar functional RNA sequences; (xiv) microRNA target sites; (xv) non-coding RNA hybridization sites; (xvi) long stems; (xvii) open reading frames; (xviii) related information of an RNA sequence. User can submit an RNA sequence and obtain the predictive results through RegRNA 2.0 web page. Conclusions: RegRNA 2.0 is an easy to use web server for identifying regulatory RNA motifs and functional sites. Through its integrated user-friendly interface, user is capable of using various analytical approaches and observing results with graphical visualization conveniently. RegRNA 2.0 is now available at http://regrna2.mbc.nctu.edu.tw.

KinasePhos is a novel web server for computationally identifying catalytic kinase-specific phosphorylation sites. The known phosphorylation sites from public domain data sources are categorized by their annotated protein kinases. Based on the profile hidden Markov model, computational models are learned from the kinase-specific groups of the phosphorylation sites. After evaluating the learned models, the model with highest accuracy was selected from each kinase-specific group, for use in a web-based prediction tool for identifying protein phosphorylation sites. Therefore, this work developed a kinase-specific phosphorylation site prediction tool with both high sensitivity and specificity. The prediction tool is freely available at http://KinasePhos.mbc.nctu.edu.tw/.

Previous studies have shown an association between gout and/or hyperuricemia and a subsequent increase in cardiovascular disease (CVD) outcomes. Allopurinol reduces vascular oxidative stress, ameliorates inflammatory state, improves endothelial function, and prevents atherosclerosis progression. Accordingly, we tested the hypothesis that a positive association between allopurinol therapy in gout patients and future cardiovascular outcomes is present using a population-based matched-cohort study design. Patients aged ≥40 years with newly diagnosed gout having no pre-existing severe form of CVD were separated into allopurinol (n = 2483) and non-allopurinol (n = 2483) groups after matching for age, gender, index date, diabetes mellitus, hypertension, hyperlipidemia, and atrial fibrillation. The two groups were also balanced in terms of uric acid nephrolithiasis, acute kidney injury, hepatitis, and Charlson comorbidity index. With a median follow-up time of 5.25 years, the allopurinol group had a modest increase in cardiovascular risk [relative risk, 1.20; 95% confidence interval (CI), 1.08–1.34]. A Cox proportional hazard model adjusted for chronic kidney disease, uremia, and gastric ulcer gave a hazard ratio (HR) for cardiovascular outcomes of 1.25 (95% CI, 1.10–1.41) in gout patients receiving allopurinol compared with the non-allopurinol group. In further analysis of patients receiving urate-lowering therapy, the uricosuric agent group (n = 1713) had an adjusted HR of 0.83 (0.73–0.95) for cardiovascular events compared with the allopurinol group. The current population-based matched-cohort study did not support the association between allopurinol therapy in gout patients with normal risk for cardiovascular sequels and beneficial future cardiovascular outcomes. Several important risk factors for cardiovascular disease, such as smoking, alcohol consumption, body mass index, blood pressure were not obtainable in the current retrospective cohort study, thus could potentially bias the effect estimate.

Because of the rapid development of multidrug resistance, conventional antibiotics cannot kill pathogenic bacteria efficiently. New antibiotic treatments such as antimicrobial peptides (AMPs) can provide a possible solution to the antibiotic-resistance crisis. However, the identification of AMPs using experimental methods is expensive and time-consuming. Meanwhile, few studies use amino acid compositions (AACs) and physicochemical properties with different sequence lengths against different organisms to predict AMPs. Therefore, the major purpose of this study is to identify AMPs on seven categories of organisms, including amphibians, humans, fish, insects, plants, bacteria, and mammals. According to the one-rule attribute evaluation, the selected features were used to construct the predictive models based on the random forest algorithm. Compared to the accuracies of iAMP-2L (a web-server for identifying AMPs and their functional types), ADAM (a database of AMP), and MLAMP (a multi-label AMP classifier), the proposed method yielded higher than 92% in predicting AMPs on each category. Additionally, the sensitivities of the proposed models in the prediction of AMPs of seven organisms were higher than that of all other tools. Furthermore, several physicochemical properties (charge, hydrophobicity, polarity, polarizability, secondary structure, normalized van der Waals volume, and solvent accessibility) of AMPs were investigated according to their sequence lengths. As a result, the proposed method is a practical means to complement the existing tools in the characterization and identification of AMPs in different organisms.

Post-translational modifications (PTMs) are essential for modulating protein function and influencing stability, activity and signaling processes. The dbPTM 2025 update significantly expands the database to include over 2.79 million PTM sites, of which 2.243 million are experimentally validated from 48 databases and over 80 000 research articles. This version integrates proteomic data from 13 cancer types, with a particular focus on phosphoproteomic data and kinase activity profiles, allowing the exploration of personalized phosphorylation patterns in tumor samples. Integrating kinase–substrate phosphorylations with E3 ligase–substrate interactions, dbPTM 2025 provides a detailed map of PTM regulatory networks, offering insights into cancer-specific post-translational regulations. This update also includes advanced search capabilities, enabling users to efficiently query PTM data across species, PTM types and modified residues. The platform’s new features—interactive visualization tools and streamlined data downloads—allow researchers to access and analyze PTM data easily. dbPTM 2025 also enhances functional annotations, regulatory networks and disease associations, broadening its application for cancer research and the study of disease-associated PTMs. Through these enhancements, dbPTM 2025 is a comprehensive, user-friendly resource, facilitating the study of PTMs and their roles in cancer research. The database is now freely accessible at https://biomics.lab.nycu.edu.tw/dbPTM/.