Abstract
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.
Highlights
MicroRNAs are a family of small non-coding RNAs that play important regulatory roles in many physiological and disease processes [1,2]
About 2000 human miRNAs have been discovered to date, and collectively these miRNAs regulate the expression of thousands of proteincoding gene targets at both post-transcriptional and translational levels [3,4,5]. miRNAs exert their functions via target downregulation
To facilitate the process of selecting functional targets at the genome level, we have previously developed an online resource, miRDB for miRNA target prediction and functional annotations [6]
Summary
MicroRNAs (miRNAs) are a family of small non-coding RNAs that play important regulatory roles in many physiological and disease processes [1,2]. We present major updates to miRDB, including updated target prediction data, a new web server interface for custom target prediction, as well as the inclusion of a set of functional miRNAs annotated by integrating computational analyses with literature mining. In this miRDB update, we have updated the MirTarget algorithm by including additional model training data, which were generated from miRNA-target pairs experimentally identified by RNA-seq [11].
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