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Detection of miRNA Targets in High-throughput Using the 3'LIFE Assay
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miRNAs target databases: developmental methods and target identification techniques with functional annotations.

Nagendra Kumar Singh1

  • 1Department of Biological Science and Engineering, Maulana Azad National Institute of Technology, Bhopal, 462003, India. nagendravns@gmail.com.

Cellular and Molecular Life Sciences : CMLS
|February 17, 2017
PubMed
Summary

MicroRNA (miRNA) databases are crucial for understanding gene regulation and disease. This review highlights current miRNA target databases, their annotations, and the need for improved computational methods for accurate target identification and functional analysis.

Keywords:
BioinformaticsBiomarkerData miningGenomeHigh-throughputPathology

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Area of Science:

  • Life sciences and medical research
  • Plant and animal biology
  • Gene regulation and expression

Background:

  • MicroRNAs (miRNAs) are key regulators of biological processes and metabolism.
  • miRNA biogenesis involves RNA-induced silencing complex (RISC) formation, leading to mRNA repression or cleavage.
  • Dysregulation of miRNA activity is implicated in various diseases.

Purpose of the Study:

  • To review existing microRNA target databases and their functional annotations.
  • To comprehend the significance of miRNA in biological systems and disease.
  • To present the current status of annotated miRNA target resources for diagnosis and prognosis.

Main Methods:

  • Review of diverse miRNA target databases and their applications.
  • Discussion of database construction methodologies and user interface design.
  • Comparative study of miRNA targets in diseases and biological processes, including associated data types like TFBSs, polymorphism, GO, KEGG pathways, and interaction networks.

Main Results:

  • miRNA target databases integrate various data, including disease associations, genomic information, and pathway analysis.
  • Databases contain both experimentally validated and computationally predicted miRNA targets.
  • Computational prediction methods often yield false positives due to a lack of standardized prediction theories.

Conclusions:

  • Existing miRNA target databases offer valuable resources for research.
  • There is a critical need for enhanced computational approaches for accurate miRNA target identification.
  • Further development of intelligent algorithms and robust database frameworks is essential for advancing miRNA research and its clinical applications.