miRNA Target Genes Finder Pro

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miRNA Target Genes Finder Pro

miRNA Target Genes Finder Pro

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Developer’s Description

By BioPharm Soft
A Useful Tool for miRNA Target Gene Analysis and Mechanism Research of miRNA Moleculars in Life Science. Application: miRNA Targets Research (Esay-to-Use tool for miRNA target genes’ evaluation); miRNA Function in Diagnosis; Roles miRNA involved in Tumor; miRNA Signal Transductuin Cascade; miRNA Mature Sequence; siRNA Targets.

miRDB is an online database for miRNA target prediction and functional annotations. All the targets in miRDB were predicted by a bioinformatics tool, MirTarget, which was developed by analyzing thousands of miRNA-target interactions from high-throughput sequencing experiments. Common features associated with miRNA binding and target downregulation have been identified and used to predict miRNA targets with machine learning methods. miRDB hosts predicted miRNA targets in five species: human, mouse, rat, dog and chicken. Users may also provide their own sequences for custom target prediction using the updated prediction algorithm. In addition, through combined computational analyses and literature mining, functionally active miRNAs in humans and mice were identified. These miRNAs, as well as associated functional annotations, are presented in the FuncMir Collection in miRDB. As a recent update, miRDB presents the expression profiles of hundreds of cell lines and the user may limit their search for miRNA targets that are expressed in a cell line of interest. To facilitate the prediction of miRNA functions, miRDB presents a new web interface for integrative analysis of target prediction and Gene Ontology data.


  • Yuhao Chen and Xiaowei Wang (2020) miRDB: an online database for prediction of functional microRNA targets. Nucleic Acids Research. 48(D1):D127-D131.
  • Weijun Liu and Xiaowei Wang (2019) Prediction of functional microRNA targets by integrative modeling of microRNA binding and target expression data. Genome Biology. 20(1):18.
BcmicrO combines the prediction of different algorithms with Bayesian Network (TargetScan, miRanda, PicTar, mirTarget, PITA, and DianamicroT). BCmicrO was evaluated using the training data and the proteomic data. The results show that BCmicrO improves both the sensitivity and the specificity of each individual algorithm.  



  • seed match
  • conservation
  • free energy
  • site accessibility
  • target-site abundance
  • machine learning
  • 3′ compensatory pairing
  • G:U pairs allowed in the seed
  • local AU content
  • Bayesian network

BioVLAB-MMIA-NGS is Cloud-based miRNA mRNA integraed analysis system using NGS data. System computes differentially/significantly expressed miRNAs (DEmiRNAs) and mRNAs/genes (DEGs), and with targeting information, DEGs targeted by DEmiRNAs and having negative correlation between them are extracted.

  • seed match
  • conservation
  • free energy
  • site accessibility
  • target-site abundance
  • 3′ compensatory pairing
  • G:U pairs allowed in the seed
  • local AU content

CleaveLand a generalizable computational pipeline for the detection of cleaved miRNA targets from degradome data. CleaveLand takes as input degradome sequences, small RNAs and an mRNA database and outputs small RNA targets.


  • seed match
  • conservation
  • free energy
  • G:U pairs allowed in the seed
  • degradome seq analysis

ComiR (Combinatorial miRNA targeting) predicts whether a given mRNA is targeted by a set of miRNAs. ComiR uses miRNA expression to improve and combine multiple miRNA targets for each of the four prediction algorithms: miRanda, PITA, TargetScan and mirSVR. The composite scores of the four algorithms are then combined using a support vector machine trained on Drosophila Ago1 IP data.



  • seed match
  • conservation
  • free energy
  • site accessibility
  • target-site abundance
  • machine learning
  • 3′ compensatory pairing
  • G:U pairs allowed in the seed
  • local AU content
  • miRNA expression level

miRBase: the microRNA database

miRBase provides the following services:

  • The miRBase database is a searchable database of published miRNA sequences and annotation. Each entry in the miRBase Sequence database represents a predicted hairpin portion of a miRNA transcript (termed mir in the database), with information on the location and sequence of the mature miRNA sequence (termed miR). Both hairpin and mature sequences are available for searching and browsing, and entries can also be retrieved by name, keyword, references and annotation. All sequence and annotation data are also available for download.
  • The miRBase Registry provides miRNA gene hunters with unique names for novel miRNA genes prior to publication of results. Visit the help pages for more information about the naming service.

To receive email notification of data updates and feature changes please subscribe to the miRBase announcements mailing list. Any queries about the website or naming service should be directed at [email protected]

miRBase is managed by the Griffiths-Jones lab at the Faculty of Biology, Medicine and Health, University of Manchester with funding from the BBSRC. miRBase was previously hosted and supported by the Wellcome Trust Sanger Institute.

MicroRNAs (miRNAs) are small non-coding RNA molecules that function as diverse endogenous gene regulators at the post-transcriptional level. In the past two decades, as research effort on miRNA identification, function and evolution has soared, so has the demand for miRNA databases. However, the current plant miRNA databases suffer from several typical drawbacks, including a lack of entries for many important species, uneven annotation standards across different species, abundant questionable entries, and limited annotation. To address these issues, we developed a knowledge-based database called Plant miRNA Encyclopedia (PmiREN, http://www.pmiren.com/), which was based on uniform processing of sequenced small RNA libraries using miRDeep-P2, followed by manual curation using newly updated plant miRNA identification criteria, and comprehensive annotation. PmiREN currently contains 16,422 high confidence novel miRNA loci in 88 plant species and 3,966 retrieved from miRBase. For every miRNA entry, information on precursor sequence, precursor secondary structure, expression pattern, clusters and synteny in the genome, potential targets supported by Parallel Analysis of RNA Ends (PARE) sequencing, and references is attached whenever possible. PmiREN is hierarchically accessible and has eight built-in search engines. We believe PmiREN is useful for plant miRNA cataloguing and data mining, therefore a resource for data-driven miRNA research in plants.


MicroRNAs (miRNAs) are defined as small non-coding RNAs ~22 nt in length. They regulate gene expression at a post-transcriptional level through complementary base pairing with the target mRNA, leading to mRNA degradation and therefore blocking translation. In the last decade, the dysfunction of miRNAs has been related to the development and progression of many diseases. Currently, researchers need a method to identify precisely the miRNA targets, prior to applying experimental approaches that allow a better functional characterization of miRNAs in biological processes and can thus predict their effects. Computational prediction tools provide a rapid method to identify putative miRNA targets. However, since a large number of tools for the prediction of miRNA:mRNA interactions have been developed, all with different algorithms, the biological researcher sometimes does not know which is the best choice for his study and many times does not understand the bioinformatic basis of these tools. This review describes the biological fundamentals of these prediction tools, characterizes the main sequence-based algorithms, and offers some insights into their uses by biologists.

1. Introduction

Non-coding RNAs are classified as long and small non-coding. The small non-coding RNAs in animals are composed of piRNA (24–30 nt in length), microRNA (~22 nt in length) and siRNA (~21 nt in length) [1]. The microRNAs (miRNA) are transcribed by RNA polymerase II from miRNA genes, generating a primary miRNA (pri-miRNA) that is then processed by the microprocessor complex to yield a precursor to miRNA (pre-miRNA) [2]. In some instances, pre-miRNAs are spliced out of introns from host genes and are then called mirtrons [3]. In a few cases, miRNAs are transcribed by RNA polymerase III [4]. Pre-miRNAs are exported to the cytoplasm and further processed by the DICER/transactivation response RNA-binding protein (TRBP) complex and finally by the RNA-induced silencing complex (RISC) [5,6]. The mature single-stranded miRNA acts as a post-transcriptional regulator binding to the mRNA in a complementary base-pairing manner to prevent the translation of this mRNA target [7].

miRNAs represent a novel epigenetic mechanism that regulates gene expression in many homoeostatic processes and pathological conditions within the cells. The dysfunction of miRNAs has been associated with a large number of diseases. For instance, the importance of miR-21 in different types of diabetes mellitus has been described by Sekar et al. [8], and the miRNAs of the hsa-let-7 family and others are associated with obesity and related metabolic diseases [9]. miRNAs also participate in arthritic diseases [10], kidney disease [11], cardiovascular diseases [12], etc. In the case of cancer, miRNAs are involved in all cancer types and can act as either tumor suppressors or inducers. Oncogenic miRNAs (oncomiRs) act directly on mRNAs from genes with pro-apoptotic or anti-proliferative roles. Conversely, tumor-suppressor miRNAs repress the expression of genes with oncogenic functions [13]. Therefore, RNA has been targeted for the study of new drugs and therapeutic methods [14,15,16].


MicroRNAs (miRNAs) are short non-coding RNAs that regulate gene expression in plants and animals. Although their biological importance has become clear, how they recognize and regulate target genes remains less well understood. Here, we systematically evaluate the minimal requirements for functional miRNA–target duplexes in vivo and distinguish classes of target sites with different functional properties. Target sites can be grouped into two broad categories. 5′ dominant sites have sufficient complementarity to the miRNA 5′ end to function with little or no support from pairing to the miRNA 3′ end. Indeed, sites with 3′ pairing below the random noise level are functional given a strong 5′ end. In contrast, 3′ compensatory sites have insufficient 5′ pairing and require strong 3′ pairing for function. We present examples and genome-wide statistical support to show that both classes of sites are used in biologically relevant genes. We provide evidence that an average miRNA has approximately 100 target sites, indicating that miRNAs regulate a large fraction of protein-coding genes and that miRNA 3′ ends are key determinants of target specificity within miRNA families.

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