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About miRDB

Why miRDB?

How many predicted gene targets are included in miRDB?

How to interpret the target prediction score?
My favorite organism is not included. What should I do?

About Target Search And Mining

How to perform target search?

How to perform target mining?
Why the retrieved GenBank accession sometimes is different from my submitted one?

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How to report problems or suggestions?

Policies For miRDB Usage

How to cite miRDB?

Can I make a link to miRDB?


Why miRDB?

miRDB is an online database for predicted microRNA targets in animals. MicroRNAs are involved in many diverse biological processes and they may potentially regulate the functions of thousands of genes. 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 select relevant biological features to build target prediction models with high specificity. We have developed a new miRNA target prediction program based on support vector machines (SVMs) and high-throughput training datasets. By systematically analyzing high-throughput experimental data, we have identified novel features that are important to miRNA target binding and expression downregulation. These new features as well as other known features have been integrated in an SVM machine learning framework for the training of our target prediction model. Our prediction algorithm has been validated by independent experimental data for its improved selectivity on predicting a large number of miRNA downregulated gene targets.


What is SVM?
support vector machines (SVMs) are universal constructive machine learning procedures based on statistical learning theory. SVM has been applied in many diverse applications such as pattern recognition, computational biology, and image analysis. The basic concept is that by maximizing the separation between the two classes in a nonlinear feature space, SVM not only reduces the training error but more importantly also achieves better generalization on unseen data.


How to interpret the target prediction score?
All the predicted targets have target prediction scores between 50 - 100. These scores are assigned by the new computational target prediction algorithm. The higher the score, the more confidence we have in this prediction. That is why the search result is ordered by prediction score. In our experience, a predicted target with prediction score > 80 is most likely to be real. If the score is below 60, you need to be cautious and it is recommended to have other supporting evidence as well. 


My favorite organism is not included. What should I do?
miRDB currently has predicted miRNA targets in five organisms: human, mouse, rat, dog and chicken. If your organism is not included, you may send us your request. If there is a high demand for your  organism, we will include its predicted miRNA targets in future updates.


How to perform target search?
There are two ways to search miRDB for predicted miRNA targets.

1) Search by miRNA names. Partial names are allowed. If there is more than one match, all the matched miRNAs will be returned and you may choose from one of these miRNAs to view their predicted targets. If there is only one match, the target prediction result will be presented directly. The partial name search can be useful if you need to do a general search. For example, by typing in "hsa", you will retrieved all human miRNAs with predicted targets.

2) Search by gene target information. There are three options to do gene search: GenBank Accession, NCBI Gene ID or Gene Symbol. You have to enter the exact ID or symbol and no partial match is allowed. In this way, a single gene record will be retrieved if it is predicted to be a miRNA target.


How to perform target mining?
The Target Mining page provides advanced search options for miRNAs or their gene targets. First, you need to click on one of the radio buttons to choose either miRNA search or gene target search. There are two optional check boxes for the exclusion of miRNAs with too many predicted targets or targets with low scores. You may adjust the threshold values to tailor for your needs. The default recommendation is to only include targets with scores >60 and miRNAs with <800 targets.

You may provide your own miRNA or gene list. Alternatively, you may choose the pre-compiled pathway gene list from the page. When searching for miRNA gene targets, full mature miRNA names are required. For the search of miRNA regulators, you may provide either NCBI gene IDs or official gene symbols. If symbols are used in the search, you also need to specify the species. Please use space or comma to separate the entries.


Why the retrieved GenBank accession sometimes is different from my submitted one?
Because of the sequence redundancy in GenBank, each gene is usually represented by more than one GenBank record. miRDB uses the NCBI Gene index files to map multiple sequence records to the same gene record. As a result, a different accession number other than originally submitted may be retrieved. However, both accessions represent the same gene.


Who developed miRDB?
miRDB was created by Xiaowei Wang's lab at the Department of Pharmacology, University of Illinois at Chicago.

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