Xiaowei Wang

MicroRNAs (miRNAs) are small noncoding RNAs that act as master regulators in many biological processes. miRNAs function mainly by downregulating the expression of their gene targets. Thus, accurate prediction of miRNA targets is critical for characterization of miRNA functions. To this end, we have developed an online database, miRDB, for miRNA target prediction and functional annotations. Recently, we have performed major updates for miRDB. Specifically, by employing an improved algorithm for miRNA target prediction, we now present updated transcriptome-wide target prediction data in miRDB, including 3.5 million predicted targets regulated by 7000 miRNAs in five species. Further, we have implemented the new prediction algorithm into a web server, allowing custom target prediction with user-provided sequences. Another new database feature is the prediction of cell-specific miRNA targets. miRDB now hosts the expression profiles of over 1000 cell lines and presents target prediction data that are tailored for specific cell models. At last, a new web query interface has been added to miRDB for prediction of miRNA functions by integrative analysis of target prediction and Gene Ontology data. All data in miRDB are freely accessible at http://mirdb.org.

MicroRNAs (miRNAs) are small non-coding RNAs that are extensively involved in many physiological and disease processes. One major challenge in miRNA studies is the identification of genes regulated by miRNAs. To this end, we have developed an online resource, miRDB (http://mirdb.org), for miRNA target prediction and functional annotations. Here, we describe recently updated features of miRDB, including 2.1 million predicted gene targets regulated by 6709 miRNAs. In addition to presenting precompiled prediction data, a new feature is the web server interface that allows submission of user-provided sequences for miRNA target prediction. In this way, users have the flexibility to study any custom miRNAs or target genes of interest. Another major update of miRDB is related to functional miRNA annotations. Although thousands of miRNAs have been identified, many of the reported miRNAs are not likely to play active functional roles or may even have been falsely identified as miRNAs from high-throughput studies. To address this issue, we have performed combined computational analyses and literature mining, and identified 568 and 452 functional miRNAs in humans and mice, respectively. These miRNAs, as well as associated functional annotations, are presented in the FuncMir Collection in miRDB.

We perform a large-scale RNA sequencing study to experimentally identify genes that are downregulated by 25 miRNAs. This RNA-seq dataset is combined with public miRNA target binding data to systematically identify miRNA targeting features that are characteristic of both miRNA binding and target downregulation. By integrating these common features in a machine learning framework, we develop and validate an improved computational model for genome-wide miRNA target prediction. All prediction data can be accessed at miRDB ( http://mirdb.org ).

MicroRNAs (miRNAs) are short noncoding RNAs that are involved in the regulation of thousands of gene targets. Recent studies indicate that miRNAs are likely to be master regulators of many important biological processes. Due to their functional importance, miRNAs are under intense study at present, and many studies have been published in recent years on miRNA functional characterization. The rapid accumulation of miRNA knowledge makes it challenging to properly organize and present miRNA function data. Although several miRNA functional databases have been developed recently, this remains a major bioinformatics challenge to miRNA research community. Here, we describe a new online database system, miRDB, on miRNA target prediction and functional annotation. Flexible web search interface was developed for the retrieval of target prediction results, which were generated with a new bioinformatics algorithm we developed recently. Unlike most other miRNA databases, miRNA functional annotations in miRDB are presented with a primary focus on mature miRNAs, which are the functional carriers of miRNA-mediated gene expression regulation. In addition, a wiki editing interface was established to allow anyone with Internet access to make contributions on miRNA functional annotation. This is a new attempt to develop an interactive community-annotated miRNA functional catalog. All data stored in miRDB are freely accessible at http://mirdb.org.

MOTIVATION: MicroRNAs (miRNAs) are involved in many diverse biological processes and they may potentially regulate the functions of thousands of genes. However, one major issue in miRNA studies is the lack of bioinformatics programs to accurately predict miRNA targets. Animal miRNAs have limited sequence complementarity to their gene targets, which makes it challenging to build target prediction models with high specificity. RESULTS: Here we present a new miRNA target prediction program based on support vector machines (SVMs) and a large microarray training dataset. By systematically analyzing public microarray data, we have identified statistically significant features that are important to target downregulation. Heterogeneous prediction features have been non-linearly integrated in an SVM machine learning framework for the training of our target prediction model, MirTarget2. About half of the predicted miRNA target sites in human are not conserved in other organisms. Our prediction algorithm has been validated with independent experimental data for its improved performance on predicting a large number of miRNA down-regulated gene targets. AVAILABILITY: All the predicted targets were imported into an online database miRDB, which is freely accessible at http://mirdb.org.