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MicroRNAs01:22

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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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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...
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RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...
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RNA interference (RNAi) is a process in which a small non-coding RNA molecule blocks the post-transcriptional expression of a gene by binding to its messenger RNA (mRNA) and preventing the protein from being translated.
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Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
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Computational Biology in microRNA.

Yue Li1,2, Zhaolei Zhang2,3

  • 1Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.

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Summary

MicroRNAs (miRNAs) regulate gene expression, but identifying their targets is difficult. This review surveys computational methods for predicting miRNA targets, including sequence, expression, and network-based approaches.

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Area of Science:

  • Molecular Biology
  • Genetics
  • Bioinformatics

Background:

  • MicroRNAs (miRNAs) are small noncoding RNAs regulating gene expression post-transcriptionally.
  • Over 1000 human miRNAs exist, potentially targeting numerous genes and influencing biological processes, including cancer.
  • Accurate identification of miRNA targets is crucial but challenging due to imperfect base-pairing and dynamic regulation.

Purpose of the Study:

  • To review and categorize state-of-the-art computational methods for predicting microRNA (miRNA) targets.
  • To provide a comprehensive overview of sequence-based, expression-based, and network-based target prediction strategies.
  • To serve as a reference for researchers in the field of miRNA biology and cancer research.

Main Methods:

  • Survey of computational miRNA target prediction methods.
  • Categorization into sequence-based, expression-based, and network-based approaches.
  • Analysis of the technicalities, strengths, and limitations of each method.

Main Results:

  • Identified three main categories of computational miRNA target prediction methods.
  • Detailed the principles of sequence-based (seed-match, conservation, binding energy), expression-based (miRNA/mRNA data), and network-based approaches.
  • Highlighted the challenges and specificities of each prediction strategy.

Conclusions:

  • Computational methods are essential for identifying miRNA targets, despite inherent challenges.
  • The reviewed methods offer diverse strategies for understanding miRNA regulatory networks.
  • This review provides valuable insights for researchers navigating the complexities of miRNA target prediction.