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Related Concept Videos

MicroRNAs01:22

MicroRNAs

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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...
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Biotin-based Pulldown Assay to Validate mRNA Targets of Cellular miRNAs
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Computational Methods for Predicting Mature microRNAs.

Malik Yousef1, Alisha Parveen2, Abhishek Kumar3,4

  • 1Department of Information System, Galilee Digital Health Research Center (GDH), Zefat Academic College, Zefat, Israel.

Methods in Molecular Biology (Clifton, N.J.)
|August 25, 2021
PubMed
Summary
This summary is machine-generated.

MicroRNAs (miRNAs) are key gene regulators involved in human diseases. This review covers computational methods and software for predicting mature miRNA sequences, addressing limitations of existing pre-miRNA predictors.

Keywords:
Machine learningMature miRNAMature miRNA predictionMicro RNARandom forestSupport vector machine

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Area of Science:

  • Molecular Biology
  • Genetics
  • Bioinformatics

Background:

  • MicroRNAs (miRNAs) are small noncoding RNAs crucial for gene regulation and implicated in human diseases.
  • Understanding miRNA biogenesis, particularly mature miRNA production from precursor mRNAs, is vital.
  • Experimental methods for miRNA identification are challenging due to time and cost constraints.

Purpose of the Study:

  • To provide an updated review of computational methods for mature miRNA sequence prediction.
  • To highlight the limitations of current pre-miRNA predictors that do not locate mature miRNAs.
  • To survey existing software suites for mature miRNA prediction.

Main Methods:

  • Review of computational approaches for miRNA identification.
  • Analysis of existing miRNA prediction software.
  • Comparison of prediction tools based on their ability to identify mature miRNA sequences.

Main Results:

  • Computational methods are increasingly used for miRNA identification due to experimental limitations.
  • Many existing tools predict precursor miRNAs (pre-miRNAs) but not mature miRNAs.
  • This work reviews various methods and 15 software suites for mature miRNA prediction.

Conclusions:

  • Accurate prediction of mature miRNAs is essential for understanding their regulatory roles.
  • There is a need for improved computational tools that specifically predict mature miRNA sequences.
  • This review offers a comprehensive overview of the current state-of-the-art in mature miRNA prediction.