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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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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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Riboswitches are non-coding mRNA domains that regulate the transcription and translation of downstream genes without the help of proteins. Riboswitches bind directly to a metabolite and can form unique stem-loop or hairpin structures in response to the amount of the metabolite present. They have two distinct regions – a metabolite-binding aptamer and an expression platform.
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Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a  complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...
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Translational regulation in prokaryotes ensures efficient protein synthesis by controlling ribosome access to mRNA. This regulation is mediated by secondary RNA structures, including translational riboswitches, RNA thermometers, and small RNAs (sRNAs), which respond to intracellular and environmental signals to modulate gene expression.Translational RiboswitchesRiboswitches in the leader region of mRNAs can regulate translation by altering the accessibility of the Shine-Dalgarno (SD) sequence,...
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A review on methods for predicting miRNA-mRNA regulatory modules.

Madhumita Madhumita1, Sushmita Paul1

  • 1Department of Bioscience and Bioengineering, Indian Institute of Technology, Jodhpur 342037, Rajasthan, India.

Journal of Integrative Bioinformatics
|March 31, 2022
PubMed
Summary

This review categorizes 26 algorithms for identifying microRNA-mRNA regulatory modules (MRMs) by integrating expression data and target information. It aids researchers in selecting appropriate methods for biological network analysis.

Keywords:
computational methodsmiRNA–mRNA regulatory modulessurvey

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Understanding gene regulation requires identifying microRNA-mRNA interactions.
  • Traditional methods relied solely on sequence-based predictions.
  • High-throughput omics technologies now provide integrated expression profiles.

Purpose of the Study:

  • To comprehensively review 26 algorithms for microRNA-mRNA regulatory module (MRM) detection.
  • To classify these methods based on their underlying mathematical approaches.
  • To guide researchers in selecting suitable algorithms for their specific biological questions.

Main Methods:

  • Systematic review of 26 existing MRM identification algorithms.
  • Classification of algorithms into eight groups based on mathematical methodologies.
  • Analysis of key features and suitability of each method.

Main Results:

  • A categorized overview of 26 MRM detection algorithms is presented.
  • Methods are grouped into eight distinct mathematical approaches.
  • Key features and comparative analysis of each algorithm are detailed.

Conclusions:

  • Integrative approaches combining expression data and target information offer robust MRM detection.
  • The classification aids in understanding algorithm functionalities and limitations.
  • Algorithm selection should be guided by available data and research objectives.