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RNA Secondary Structure Prediction Using High-throughput SHAPE
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RCPred: RNA complex prediction as a constrained maximum weight clique problem.

Audrey Legendre1, Eric Angel1, Fariza Tahi2

  • 1IBISC, Univ Evry, Université Paris-Saclay, Evry, 91025, France.

BMC Bioinformatics
|March 31, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces RCPred, a novel computational method for predicting RNA complex secondary structures. RCPred optimizes combinations of predicted structures and interactions, offering competitive results for RNA-RNA interaction analysis.

Keywords:
Maximum weight clique heuristicPseudoknotRNA complexRNA interactionSecondary structure

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Molecular Biology

Background:

  • RNA molecules interact to form complexes with diverse biological functions.
  • Predicting the secondary structure of RNA complexes is crucial for understanding their 3D structure.
  • Existing tools for RNA secondary structure and interaction prediction can be leveraged.

Purpose of the Study:

  • To develop an original approach for predicting RNA complex secondary structures.
  • To formulate RNA complex prediction as an optimization problem.
  • To identify the optimal combination of predicted RNA secondary structures and RNA-RNA interactions based on free energy.

Main Methods:

  • Modeled predicted RNA structures and interactions as a graph.
  • Formulated the problem as a constrained maximum weight clique problem.
  • Developed a Breakout Local Search heuristic and implemented the RCPred tool.

Main Results:

  • RCPred identifies optimal combinations of predicted RNA secondary structures and interactions.
  • The tool returns multiple solutions, including internal and external pseudoknots.
  • RCPred demonstrates competitive performance against state-of-the-art methods on extensive datasets.

Conclusions:

  • RCPred is a novel method for predicting RNA complex secondary structures, including pseudoknots.
  • Future work includes refining global energy computation and incorporating 3D motifs.