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Related Experiment Video

Updated: Jun 14, 2026

RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA
09:36

RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA

Published on: April 10, 2018

Computational methodologies for studying non-coding RNAs relevant to central nervous system function and dysfunction.

Anna Majer1, Stephanie A Booth

  • 1Department of Medical Microbiology and Infectious Diseases, Faculty of Medicine, University of Manitoba, Manitoba, Canada.

Brain Research
|April 13, 2010
PubMed
Summary
This summary is machine-generated.

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Non-coding RNAs (ncRNAs) are crucial for central nervous system (CNS) function. This review details computational methods for identifying ncRNAs and their gene targets, especially microRNAs (miRNAs), using advanced sequencing technologies.

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Non-coding RNAs (ncRNAs) are diverse genomic transcripts, with less than 2% encoding proteins.
  • ncRNAs play critical roles in central nervous system (CNS) functions, including gene regulation, development, and RNA metabolism.
  • Distinguishing ncRNAs and discovering novel families necessitate specialized computational tools.

Purpose of the Study:

  • To review computational methodologies for predicting ncRNAs based on sequence and structure.
  • To focus on prediction methods for regulatory ncRNAs, particularly microRNAs (miRNAs), and their gene targets.
  • To discuss the role of deep sequencing and associated computational resources in ncRNA identification.

Main Methods:

  • Genome scanning algorithms for ncRNA prediction.

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Last Updated: Jun 14, 2026

RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA
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Published on: April 10, 2018

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  • Mixed approaches combining various predictive features.
  • Machine learning algorithms for enhanced ncRNA identification.

    • Main Results:

    • Overview of diverse computational approaches for ncRNA prediction.
    • Emphasis on methods for predicting microRNAs and their target genes.
    • Highlighting the impact of deep sequencing on ncRNA discovery.

    Conclusions:

    • Computational tools are essential for identifying and characterizing ncRNAs.
    • Machine learning and deep sequencing are advancing ncRNA research.
    • Accurate prediction of ncRNAs and their targets is vital for understanding gene regulation.