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Related Concept Videos

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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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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mirMachine: A One-Stop Shop for Plant miRNA Annotation
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A Post-Processing Algorithm for miRNA Microarray Data.

Stepan Nersisyan1, Maxim Shkurnikov2, Andrey Poloznikov3,4

  • 1Faculty of Mechanics and Mathematics, Lomonosov Moscow State University, Leninskie Gory 1, 119991 Moscow, Russia.

International Journal of Molecular Sciences
|February 16, 2020
PubMed
Summary

This study introduces a novel algorithm to improve microRNA (miRNA) expression profiling using DNA microarrays. The method reduces false positives by incorporating discovery time and pre-miRNA correlation, enhancing data accuracy.

Keywords:
TCGAmiRNA microarraysmiRNome of breast cancer

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Area of Science:

  • Biotechnology
  • Genomics
  • Molecular Biology

Background:

  • DNA microarrays face challenges in comparing microRNA (miRNA) expression values across different miRNAs, leading to false positives.
  • Accurate miRNA expression profiling is crucial for understanding biological processes and disease states.

Purpose of the Study:

  • To develop and validate a post-processing algorithm to enhance the accuracy of miRNA expression profiling from DNA microarray data.
  • To address the issue of false positives in miRNA expression analysis by incorporating additional biological information.

Main Methods:

  • A novel post-processing algorithm was developed to score miRNAs based on expression values, miRNA discovery time, and miRNA-pre-miRNA expression correlation.
  • The algorithm was validated by comparing its results on breast tumor samples with publicly available miRNA sequencing (miRNA-seq) data.
  • Paired miRNA sequencing and array data were utilized to investigate reasons for false positives in microarray studies.

Main Results:

  • The proposed algorithm successfully improved the accuracy of miRNA expression profiling from DNA microarray data.
  • Validation against miRNA-seq data confirmed the algorithm's ability to reduce false positives in breast tumor samples.
  • Analysis of paired data provided insights into the mechanisms underlying false positive miRNA detection in microarrays.

Conclusions:

  • The developed algorithm significantly enhances the reliability of miRNA expression profiling using DNA microarrays.
  • Incorporating factors like discovery time and pre-miRNA correlation can overcome limitations of traditional microarray analysis.
  • This approach offers a more accurate method for identifying genuine miRNA expression patterns in biological samples.