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Related Experiment Video

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Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
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A high-throughput approach to profile RNA structure.

Riccardo Delli Ponti1,2, Stefanie Marti1,2, Alexandros Armaos1,2

  • 1Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr Aiguader 88, 08003 Barcelona, Spain.

Nucleic Acids Research
|December 1, 2016
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Summary
This summary is machine-generated.

We developed the Computational Recognition of Secondary Structure (CROSS) method to predict RNA structural profiles with high accuracy using only sequence data. This tool enhances secondary structure prediction, aiding RNA research.

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Area of Science:

  • Computational Biology
  • RNA Structure Prediction

Background:

  • Accurate RNA secondary structure prediction is crucial for understanding RNA function.
  • Existing methods often face limitations in sequence length and resolution.

Purpose of the Study:

  • To introduce the Computational Recognition of Secondary Structure (CROSS) method for RNA structural profiling.
  • To achieve single-nucleotide resolution and overcome sequence length restrictions.

Main Methods:

  • Trained the CROSS algorithm using high-throughput experimental data (SHAPE, PARS) and structural databases (PDB).
  • Utilized primary RNA sequence information for predictions.
  • Integrated CROSS with thermodynamics-based methods.

Main Results:

  • CROSS predicts experimental structural profiles with >80% accuracy.
  • Demonstrated high performance on large RNAs like Xist (AUC of 0.75 for DMS experiments).
  • Improved secondary structure prediction power by up to 30% when integrated with thermodynamic models.

Conclusions:

  • CROSS is an accurate and versatile tool for RNA structural profiling.
  • The method advances RNA structure prediction capabilities, particularly for large and complex RNA molecules.