N4-acetylcytidine (ac4C) is the only acetylation modification that has been characterized in eukaryotic RNA, and is correlated with various human diseases. Laboratory identification of ac4C is complicated by factors, such as sample hydrolysis and high cost. Unfortunately, existing computational methods to identify ac4C do not achieve satisfactory performance. We developed a novel tool, DeepAc4C, which identifies ac4C using convolutional neural networks (CNNs) using hybrid features composed of physicochemical patterns and a distributed representation of nucleic acids. Our results show that the proposed model achieved better and more balanced performance than existing predictors. Furthermore, we evaluated the effect that specific features had on the model predictions and their interaction effects. Several interesting sequence motifs specific to ac4C were identified. The webserver is freely accessible at https://ac4c.webmalab.cn/, the source code and datasets are accessible at Zenodo with URL https://doi.org/10.5281/zenodo.5138047 and Github with URL https://github.com/wangchao-malab/DeepAc4C. Supplementary data are available at Bioinformatics online.

Several Coronaviruses (CoVs) are epidemic pathogens that cause severe respiratory syndrome and are associated with significant morbidity and mortality. In this paper, a machine learning method was developed for predicting the risk of human infection posed by CoVs as an early warning system. The proposed Spike-SVM (Support vector machine) model achieved an accuracy of 97.36% for Human-infective CoV (HCoV) and Nonhuman-infective CoV (Non-HCoV) classification. The top informative features that discriminate HCoVs and Non-HCoVs were identified. Spike-SVM is anticipated to be a useful bioinformatics tool for predicting the infection risk posed by CoVs to humans.

Fungi play essential roles in many ecological processes, and taxonomic classification is fundamental for microbial community characterization and vital for the study and preservation of fungal biodiversity. To cope with massive fungal barcode data, tools that can implement extensive volumes of barcode sequences, especially the internal transcribed spacer (ITS) region, are necessary. However, high variation in the ITS region and computational requirements for processing high‐dimensional features remain challenging for existing predictors. In this study, we developed Its2vec, a bioinformatics tool for the classification of fungal ITS barcodes to the species level. An ITS database covering more than 25,000 species in a broad range of fungal taxa was assembled. For dimensionality reduction, a word embedding algorithm was used to represent an ITS sequence as a dense low‐dimensional vector. A random forest‐based classifier was built for species identification. Benchmarking results showed that our model achieved an accuracy comparable to that of several state‐of‐the‐art predictors, and more importantly, it could implement large datasets and greatly reduce dimensionality. We expect the Its2vec model to be helpful for fungal species identification and, thus, for revealing microbial community structures and in deepening our understanding of their functional mechanisms.

Fungal pathogens have evolved the ability to cause serious plant diseases and threaten the world food security. Fungal effectors are proteins that exploit the host cellular functions to facilitate infection. Effector identification is crucial for disease control in crops and to understand plant-pathogen interactions. However, fungal effector identification has been challenging as most fungal effectors lack of consensus motifs and data imbalance problem. In this study, a fungal effector predictor was designed to effectively learn from an imbalanced dataset. A granular support vector-based under-sampling (GSV-US) strategy combined with a genetic algorithm was used for majority class sampling. When evaluating on an independent test dataset, the FunEffector-Pred significantly outperformed the existing predictors for fungal effector identification. Several informative feature patterns, such as the patterns of Ile, Gly, Val, Leu and Thr, as well as the combination of aromatic amino acids with positively-charged amino acids, are reported for fungal effector identification for the first time.

Enhancers are crucial for precise regulation of gene expression, while enhancer identification and strength prediction are challenging because of their free distribution and tremendous number of similar fractions in the genome. Although several bioinformatics tools have been developed, shortfalls in these models remain, and their performances need further improvement. In the present study, a two-layer predictor called Enhancer-FRL was proposed for identifying enhancers (enhancers or nonenhancers) and their activities (strong and weak). More specifically, to build an efficient model, the feature representation learning scheme was applied to generate a 50D probabilistic vector based on 10 feature encodings and five machine learning algorithms. Subsequently, the multiview probabilistic features were integrated to construct the final prediction model. Compared with the single feature-based model, Enhancer-FRL showed significant performance improvement and model robustness. Performance assessment on the independent test dataset indicated that the proposed model outperformed state-of-the-art available toolkits. The webserver Enhancer-FRL is freely accessible at http://lab.malab.cn/∼wangchao/softwares/Enhancer-FRL/ , The code and datasets can be downloaded at the webserver page or at the Github https://github.com/wangchao-malab/Enhancer-FRL/ .

The prevalence of calcium oxalate (CaOx) kidney stones is increasing, yet the underlying mechanisms remain incompletely understood. Emerging evidence sugge