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RNA Secondary Structure Prediction Using High-throughput SHAPE
Published on: May 31, 2013
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Identifying proximal RNA interactions from cDNA-encoded crosslinks with ShapeJumper
Thomas W Christy1,2, Catherine A Giannetti1, Alain Laederach3
1Department of Chemistry, University of North Carolina, Chapel Hill, North Carolina, United States of America.
Plos Computational Biology
|December 14, 2021
Summary
SHAPE-JuMP, a method for RNA interaction analysis, uses a novel reverse transcriptase to identify nucleotide proximity. The ShapeJumper pipeline accurately processes this data, improving RNA structure studies.
Area of Science:
- Molecular Biology
- Bioinformatics
- RNA Structure Analysis
Background:
- Identifying RNA-RNA interactions is crucial for understanding gene regulation.
- Existing methods like adapter-ligation have limitations in detecting close-in-space interactions.
- SHAPE-JuMP (Selective 2'-hydroxyl acylation analyzed by primer extension - Jumping-profiling of RNA Macromolecular Proximity) offers a new approach.
Purpose of the Study:
- To introduce ShapeJumper, a bioinformatics pipeline for processing SHAPE-JuMP sequencing data.
- To accurately identify through-space RNA-RNA interactions from complex datasets.
- To enhance the resolution and reliability of RNA proximity detection.
Main Methods:
- Utilizing a bi-reactive reagent to crosslink nucleotides in close 3D proximity.
- Employing an engineered reverse transcriptase that traverses crosslinked sites, creating cDNA deletions.
- Developing ShapeJumper, a bioinformatics pipeline with an optimized alignment strategy for SHAPE-JuMP data analysis.
Main Results:
- ShapeJumper accurately identifies proximal RNA interactions with near-nucleotide resolution.
- The pipeline effectively handles the unique reverse-transcription profile generated by the engineered enzyme.
- Demonstrated the capability of SHAPE-JuMP strategies to potentially replace adapter-ligation for RNA interaction detection.
Conclusions:
- ShapeJumper provides a robust method for analyzing SHAPE-JuMP data, enabling precise RNA interaction mapping.
- SHAPE-JuMP-based strategies represent a significant advancement in studying RNA structure and function.
- This approach is poised to become a standard for detecting RNA-RNA interactions in crosslinking experiments.
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