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Diverse functional elements in RNA predicted transcriptome-wide by orthogonal RNA structure probing.

Dalen Chan1, Chao Feng1, Whitney E England1

  • 1Department of Pharmaceutical Sciences, University of California, Irvine. Irvine, CA 92697, USA.

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We developed icLASER, a new method to map RNA solvent accessibility across the entire transcriptome. Combining this with RNA flexibility data predicts RNA-protein interactions and identifies RNA processing sites.

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Area of Science:

  • Molecular Biology
  • Chemical Biology
  • Genomics

Background:

  • RNA molecules possess complex structures crucial for biological functions.
  • Existing transcriptome-wide RNA structure mapping methods have limitations in predicting RNA-protein interactions and processing.
  • Novel chemical approaches are needed to advance the study of RNA biology.

Purpose of the Study:

  • To introduce icLASER, the first method for transcriptome-wide mapping of RNA solvent accessibility.
  • To demonstrate the utility of combining RNA solvent accessibility (icLASER) and RNA flexibility (icSHAPE) data.
  • To predict RNA-protein interactions and catalog RNA polyadenylation sites using RNA structure alone.

Main Methods:

  • Development of icLASER for mapping RNA solvent accessibility.
  • Integration of icLASER with existing icSHAPE data for RNA flexibility.
  • Application of combined data for transcriptome-wide prediction of RNA-protein interactions.
  • Utilizing RNA structure data to catalog RNA polyadenylation sites.

Main Results:

  • Successfully mapped RNA solvent accessibility transcriptome-wide using icLASER.
  • Efficiently predicted RNA-protein interactions by combining icLASER and icSHAPE data.
  • Cataloged RNA polyadenylation sites based solely on RNA structure.
  • Demonstrated the power of novel chemical approaches in RNA biology.

Conclusions:

  • Merging complementary methods like icLASER and icSHAPE provides powerful insights into RNA structure and function.
  • This integrated approach enables transcriptome-wide prediction of critical RNA-protein interactions.
  • RNA structure-based analysis can effectively catalog RNA processing events like polyadenylation.
  • Future applications include studying diverse cell types and conditions using RNA structure footprinting.