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RNA Secondary Structure Prediction Using High-throughput SHAPE
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RNA secondary structure prediction from multi-aligned sequences.

Michiaki Hamada1

  • 1Faculty of Science and Engineering, Waseda university, 55N-06-10, 3-4-1, Okubo Shinjuku-ku, Tokyo, 169-8555, Japan, mhamada@waseda.jp.

Methods in Molecular Biology (Clifton, N.J.)
|January 12, 2015
PubMed
Summary
This summary is machine-generated.

Predicting conserved RNA secondary structures from aligned sequences aids functional analysis and discovery of novel RNAs. This review classifies existing tools to improve selection for RNA bioinformatics tasks.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Biology

Background:

  • RNA secondary structures are crucial for non-coding RNA (ncRNA) function and are evolutionarily conserved.
  • Accurate prediction of these structures aids functional analysis, improves prediction accuracy, and facilitates novel ncRNA discovery.

Purpose of the Study:

  • To systematically review and classify existing computational tools for predicting conserved RNA secondary structures from aligned sequences.
  • To provide a unified viewpoint based on maximum expected gain (MEG) estimators for understanding these tools.
  • To assist users in selecting appropriate tools for their RNA bioinformatics research.

Main Methods:

  • Focuses on common secondary structure prediction from aligned RNA sequences.
  • Reviews and classifies existing algorithms and tools.
  • Employs a unified framework based on maximum expected gain (MEG) estimators.

Main Results:

  • Provides a systematic classification of common secondary structure prediction tools.
  • Offers a deeper understanding of the methodologies employed by different tools.
  • Facilitates informed selection of tools for specific RNA bioinformatics applications.

Conclusions:

  • Understanding conserved RNA secondary structures is vital for ncRNA function and discovery.
  • A systematic classification of prediction tools, viewed through the lens of MEG estimators, enhances tool selection.
  • This review serves as a valuable resource for researchers in RNA bioinformatics.