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RNA Secondary Structure Prediction Using High-throughput SHAPE
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RNA Secondary Structure Prediction Using High-throughput SHAPE

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Evolutionary Algorithm for RNA Secondary Structure Prediction Based on Simulated SHAPE Data.

Soheila Montaseri1, Mohammad Ganjtabesh1, Fatemeh Zare-Mirakabad2

  • 1Department of Computer Science, School of Mathematics, Statistics, and Computer Science, University of Tehran, Tehran, Iran.

Plos One
|November 29, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for predicting RNA secondary structure by integrating minimum free energy with SHAPE experiment data. This approach enhances prediction accuracy by simulating SHAPE data using a population of structures, overcoming previous computational limitations.

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Non-coding RNAs (ncRNAs) play crucial roles in cellular functions, largely determined by their structures.
  • Existing RNA secondary structure prediction algorithms based on minimum free energy (MFE) exhibit limited accuracy.
  • The SHAPE (Selective 2'-Hydroxyl Acylation analyzed by Primer Extension) experiment provides valuable structural information that can improve prediction accuracy.

Purpose of the Study:

  • To develop a novel computational method for predicting RNA secondary structure.
  • To enhance prediction accuracy by combining thermodynamic free energy with SHAPE experimental data.
  • To overcome the limitation of requiring a known secondary structure for SHAPE data simulation.

Main Methods:

  • A new method integrating MFE and SHAPE pseudo-free energy for RNA secondary structure prediction.
  • Generation of a population of secondary structures for each RNA sequence.
  • Simulation of SHAPE data for the population of structures.
  • Application of an evolutionary algorithm to optimize structures based on combined energy values.
  • Selection of the structure with the minimum summed free and pseudo-free energies as the final prediction.

Main Results:

  • The proposed method successfully simulates SHAPE data for any RNA sequence by utilizing a population of structures.
  • Experimental validation confirms a significant improvement in RNA secondary structure prediction accuracy.
  • The developed algorithm overcomes the dependency on pre-existing secondary structure information for SHAPE data simulation.

Conclusions:

  • Integrating SHAPE data with thermodynamic energy significantly enhances RNA secondary structure prediction accuracy.
  • The novel approach of using a population of structures enables accurate SHAPE data simulation for any RNA sequence.
  • The source code and a web server are available for public use, facilitating further research and application.