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miRNAs and their Target Prediction Tools: An Overview



miRNAs are the small endogenous non-coding RNAs having a length less than or ~22 nucleotides. miRNAs are expressed from long transcripts formed in animals, viruses, single-celled eukaryotes and plants [1]. miRNAs cause various types of human diseases among which they are more involved in causing many types of cancer such as colon cancer [2], breast cancer [3], prostate cancer [4], lung cancer [5] and so on. miRNA regulates the target mRNAs to make small adjustments to the corresponding resulting protein which consequently leads to the dysregulation of miRNA function which results in a human disease [1].

How miRNAs are produced?

miRNAs are produced in the nucleus initially as long primary transcripts (pri-miRNA) which are done with the help of RNA polymerase II. They originate either from their own non-coding gene or from the intronic region of the protein-coding genes [1]. The pri-miRNA thus formed, fold into a hairpin. This hairpin later binds to the two members of RNase III families of enzymes which are called, Drosha and Dicer. They both play an important role in the formation of miRNA.

Drosha binds to the DGCR8 and form a microprocessor complex in the nucleus and cleaves the pri-miRNA ~70 nucleotides which are known as miRNA precursor (pre-miRNA) [1]. It is then transported to the cytoplasm by exportin-5. Dicer processes the pre-miRNA to produce the mature miRNA / double-stranded miRNA which is ~20 nucleotides in length[1].

miRNA Target Prediction:

miRNA binds to the target mRNA through complementary base pairing irrespective of the complete or incomplete binding. It has been observed that miRNA generally binds to the 3′-UTR of the target mRNA having either of the two binding patterns [6]. There are two classes of binding patterns:

  • one class of binding pattern include the Watson-Crick complementarity to the target sites at the 5′- end of the miRNA which is known as “seed region”. miRNAs are able to suppress their targets with the help of seed regions [1].
  • the second class deals with the improper base pairing to the target sites at the 5′- end of the miRNA.

A single mRNA may bind to the multiple target sites in a transcript and also several miRNAs can bind to a transcript. Their short length makes them be detected statistically by using statistical techniques [7].

miRNA  Target Prediction Tools:

As miRNAs are involved in causing various human diseases, therefore, there is a need to predict their targets so that the dysregulation of miRNAs can be controlled. Some of the most widely used miRNA target prediction programs are:

  • miRanda: It identifies the miRNA targets using two steps, first the miRNA is aligned against the 3′-UTR of the sequence, then in the second step thermodynamic stability of the complex is calculated for the highest scoring alignment and reported [8].
  • miRBase: It identifies the miRNA binding sites by applying miRanda algorithm [9]. It uses the dynamic programming to identify the most complementary sites.
  • DIANA-microT 3.0: It is a scoring based algorithm which scores the binding of the miRNA to the target sites of the transcript and then calculates the precision for each interaction [10].

This article gives an overview of the miRNAs and their targets. I will try to give some more information about the target prediction and miRNA function annotation in my upcoming articles.


1. Bing Liu, Jiuyong Li, and Murray J. Cairns. Identifying miRNAs, targets and functions. Briefings in Bioinformatics. page 1-19; doi:10.1093/bib/bbs075.

2. Akao Y, Nakagawa Y, Naoe T. MicroRNA-143 and -145 in colon cancer. DNA Cell Biol. 2007;26(5):311–20.

3. Iorio MV, Ferracin M, Liu C-G, et al. MicroRNA gene expression deregulation in human breast cancer. Cancer Res 2005;65(16):7065–70.

4. Porkka KP, Pfeiffer MJ, Waltering KK, et al. MicroRNA expression profiling in prostate cancer. Cancer Res 2007; 67(13):6130–5.

5. Yanaihara N. Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell 2006;9(3):189–98.

6. Rajewsky N. microRNA target predictions in animals. Nat Genet 2006;38:S8–13.

7. Karlin S, Altschul SF. Methods for assessing the statistical significance of molecular sequence features by using

general scoring schemes. Proc Natl Acad Sci USA 1990;87(6):2264–8.

8. John,B. et al. (2004) Human microRNA targets. PLoS Biol., 2, e363.

9.  Griffiths-Jones,S. et al.(2008) miRBase: tools for microRNA genomics. Nucleic Acids Res., 36, D154–D158.

10. Maragkakis,M. et al. (2009) Accurate microRNA target prediction correlates with protein repression levels. BMC Bioinformatics, 10, 295.

How to cite this article: Faiza, M., 2016. miRNAs and their Target Prediction Tools: An Overview, 2(7):page 19-23. The article is available at

Dr. Muniba is a Bioinformatician based in New Delhi, India. She has completed her PhD in Bioinformatics from South China University of Technology, Guangzhou, China. She has cutting edge knowledge of bioinformatics tools, algorithms, and drug designing. When she is not reading she is found enjoying with the family. Know more about Muniba

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