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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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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...
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Genome-wide Screen for miRNA Targets Using the MISSION Target ID Library
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Min3: Predict microRNA target gene using an improved binding-site representation method and support vector machine.

Tinghua Huang1, Xiali Huang1, Min Yao1

  • 1College of Animal Science, Yangtze University, Jingzhou, Hubei 434025, P. R. China.

Journal of Bioinformatics and Computational Biology
|December 21, 2019
PubMed
Summary
This summary is machine-generated.

A new method improves microRNA target prediction accuracy. The Min3 software identifies 47% of validated microRNA targets, outperforming existing tools and aiding experimental validation.

Keywords:
MicroRNAMin3miR-155miR-92atarget prediction

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • MicroRNAs (miRNAs) are crucial regulators of gene expression, impacting cellular processes.
  • Accurate computational prediction of miRNA targets is vital for understanding miRNA function.
  • Current prediction algorithms lack sufficient specificity and sensitivity.

Purpose of the Study:

  • To develop an improved method for microRNA binding-site representation.
  • To create a novel software tool, Min3, for enhanced miRNA target prediction.
  • To validate the performance of Min3 against existing tools and experimental data.

Main Methods:

  • A novel representation method using four symbols to denote nucleotide base pair status in miRNA binding sites.
  • Feature engineering based on combinations of adjacent base pair symbols.
  • Support Vector Machine (SVM) model training using a comprehensive dataset from miRTarBase and pseudo-miRNA bindings.
  • Performance evaluation using known miRNA targets (miR-155, miR-92a) and comparison with TargetScan and miRanda.

Main Results:

  • Min3 identified an average of 47% of experimentally validated miRNA targets.
  • Min3 showed over 20% overlap with targets predicted by TargetScan and miRanda.
  • Analysis of public datasets indicated a negative regulatory effect of Min3-predicted targets upon miRNA knockdown.
  • Wet-lab validation confirmed regulatory effects for 5 out of 6 top-ranked predicted targets.

Conclusions:

  • Min3 offers a significant improvement in miRNA target prediction accuracy and reliability.
  • The novel binding-site representation and features enhance prediction specificity and sensitivity.
  • Min3 serves as a valuable alternative tool for miRNA target discovery and functional studies.