Comprehensive databases of microRNA–disease associations are continuously demanded in biomedical researches. The recently launched version 3.0 of Human MicroRNA Disease Database (HMDD v3.0) manually collects a significant number of miRNA–disease association entries from literature. Comparing to HMDD v2.0, this new version contains 2-fold more entries. Besides, the associations have been more accurately classified based on literature-derived evidence code, which results in six generalized categories (genetics, epigenetics, target, circulation, tissue and other) covering 20 types of detailed evidence code. Furthermore, we added new functionalities like network visualization on the web interface. To exemplify the utility of the database, we compared the disease spectrum width of miRNAs (DSW) and the miRNA spectrum width of human diseases (MSW) between version 3.0 and 2.0 of HMDD. HMDD is freely accessible at http://www.cuilab.cn/hmdd. With accumulating evidence of miRNA–disease associations, HMDD database will keep on growing in the future.

Long noncoding RNAs (lncRNAs) represent a big category of noncoding RNA molecules, and increasing studies have shown that they play important roles in various critical biological processes. They show a diversity of functions through diverse mechanisms, among which regulating RNA molecules is one of the most popular ones. Given the big number of lncRNAs, it becomes urgent and important to predict the RNA targets of lncRNAs in a large scale for the comprehensive understanding of lncRNA functions and action mechanisms. Although several methods have been developed to predict RNA–RNA interactions, none of them can be used to predict the RNA targets of lncRNAs in a large scale. Here we presented a tool, LncTar, which shows the ability to efficiently predict the RNA targets of lncRNAs in a large scale. To test the accuracy of LncTar, we applied it to 10 experimentally supported lncRNA–mRNA interactions. As a result, LncTar successfully predicted 8 (80%) of the 10 lncRNA–mRNA pairs, suggesting that LncTar has a reliable accuracy. Finally, we believe that LncTar could be an efficient tool for the fast identification of the RNA targets of lncRNAs. LncTar is freely available at http://www.cuilab.cn/lnctar.

With the rapid accumulation of high-throughput microRNA (miRNA) expression profile, the up-to-date resource for analyzing the functional and disease associations of miRNAs is increasingly demanded. We here describe the updated server TAM 2.0 for miRNA set enrichment analysis. Through manual curation of over 9000 papers, a more than two-fold growth of reference miRNA sets has been achieved in comparison with previous TAM, which covers 9945 and 1584 newly collected miRNA-disease and miRNA-function associations, respectively. Moreover, TAM 2.0 allows users not only to test the functional and disease annotations of miRNAs by overrepresentation analysis, but also to compare the input de-regulated miRNAs with those de-regulated in other disease conditions via correlation analysis. Finally, the functions for miRNA set query and result visualization are also enabled in the TAM 2.0 server to facilitate the community. The TAM 2.0 web server is freely accessible at http://www.scse.hebut.edu.cn/tam/ or http://www.lirmed.com/tam2/.

MicroRNAs (miRNAs) are one class of important small non-coding RNA molecules and play critical roles in health and disease. Therefore, it is important and necessary to evaluate the functional relationship of miRNAs and then predict novel miRNA-disease associations. For this purpose, here we developed the updated web server MISIM (miRNA similarity) v2.0. Besides a 3-fold increase in data content compared with MISIM v1.0, MISIM v2.0 improved the original MISIM algorithm by implementing both positive and negative miRNA-disease associations. That is, the MISIM v2.0 scores could be positive or negative, whereas MISIM v1.0 only produced positive scores. Moreover, MISIM v2.0 achieved an algorithm for novel miRNA-disease prediction based on MISIM v2.0 scores. Finally, MISIM v2.0 provided network visualization and functional enrichment analysis for functionally paired miRNAs. The MISIM v2.0 web server is freely accessible at http://www.lirmed.com/misim/.

Background: A series of miRNA-disease association prediction methods have been proposed to prioritize potential disease-associated miRNAs. Independent benchmarking of these methods is warranted to assess their effectiveness and robustness.Results: Based on more than 8000 novel miRNA-disease associations from the latest HMDD v3.1 database, we perform systematic comparison among 36 readily available prediction methods. Their overall performances are evaluated with rigorous precision-recall curve analysis, where 13 methods show acceptable accuracy (AUPRC > 0.200) while the top two methods achieve a promising AUPRC over 0.300, and most of these methods are also highly ranked when considering only the causal miRNA-disease associations as the positive samples. The potential of performance improvement is demonstrated by combining different predictors or adopting a more updated miRNA similarity matrix, which would result in up to 16% and 46% of AUPRC augmentations compared to the best single predictor and the predictors using the previous similarity matrix, respectively. Our analysis suggests a common issue of the available methods, which is that the prediction results are severely biased toward well-annotated diseases with many associated miRNAs known and cannot further stratify the positive samples by discriminating the causal miRNA-disease associations from the general miRNA-disease associations.Conclusion: Our benchmarking results not only provide a reference for biomedical researchers to choose appropriate miRNA-disease association predictors for their purpose, but also suggest the future directions for the development of more robust miRNA-disease association predictors.

Background: The interactions between proteins and aptamers are prevalent in organisms and play an important role in various life activities. Thanks to the rapid accumulation of protein-aptamer interaction data, it is necessary and feasible to construct an accurate and effective computational model to predict aptamers binding to certain interested proteins and protein-aptamer interactions, which is beneficial for understanding mechanisms of protein-aptamer interactions and improving aptamer-based therapies. Results: In this study, a novel web server named PPAI is developed to predict aptamers and protein-aptamer interactions with key sequence features of proteins/aptamers and a machine learning framework integrated adaboost and random forest. A new method for extracting several key sequence features of both proteins and aptamers is presented, where the features for proteins are extracted from amino acid composition, pseudo-amino acid composition, grouped amino acid composition, C/T/D composition and sequence-order-coupling number, while the features for aptamers are extracted from nucleotide composition, pseudo-nucleotide composition (PseKNC) and normalized Moreau-Broto autocorrelation coefficient. On the basis of these feature sets and balanced the samples with SMOTE algorithm, we validate the performance of PPAI by the independent test set. The results demonstrate that the Area Under Curve (AUC) is 0.907 for prediction of aptamer, while the AUC reaches 0.871 for prediction of protein-aptamer interactions. Conclusion: These results indicate that PPAI can query aptamers and proteins, predict aptamers and predict protein-aptamer interactions in batch mode precisely and efficiently, which would be a novel bioinformatics tool for the research of protein-aptamer interactions. PPAI web-server is freely available at http://39.96.85.9/PPAI.