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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.

MOTIVATION: 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 targeted by miRNAs. Currently, most researchers rely on computational programs to initially identify target candidates for subsequent validation. Although considerable progress has been made in recent years for computational target prediction, there is still significant room for algorithmic improvement.
RESULTS: Here, we present an improved target prediction algorithm, which was developed by modeling high-throughput profiling data from recent CLIPL (crosslinking and immunoprecipitation followed by RNA ligation) sequencing studies. In these CLIPL-seq studies, the RNA sequences in each miRNA-target pair were covalently linked and unambiguously determined experimentally. By analyzing the CLIPL data, many known and novel features relevant to target recognition were identified and then used to build a computational model for target prediction. Comparative analysis showed that the new algorithm had improved performance over existing algorithms when applied to independent experimental data.
AVAILABILITY AND IMPLEMENTATION: All the target prediction data as well as the prediction tool can be accessed at miRDB (http://mirdb.org).
CONTACT: xwang@radonc.wustl.edu.

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. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

One critical step in miRNA functional studies is to identify the gene targets that are directly regulated by miRNAs. In this chapter, we describe a computational algorithm and an online database, miRDB, for miRNA target prediction. In miRDB, flexible Web search interface has been developed for the retrieval of target prediction results generated by the newly developed computational algorithm. In addition, a wiki editing interface has been established to allow anyone with Internet access to make contributions on miRNA functional annotation. All data stored in miRDB are freely accessible at http://www.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.