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Related Concept Videos

RNA Structure01:23

RNA Structure

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RNA Structure01:23

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Overview
The basic structure of RNA consists of a five-carbon sugar and one of four nitrogenous bases. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
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The basic structure of RNA consists of a string of ribonucleotides attached by phosphodiester bonds. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Nucleic Acid Structure01:25

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The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms  a 5′ to 3′ phosphodiester linkage.
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Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...
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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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Data-directed RNA secondary structure prediction using probabilistic modeling.

Fei Deng1, Mirko Ledda1, Sana Vaziri1

  • 1Department of Biomedical Engineering and Genome Center, University of California at Davis, Davis, California 95616, USA.

RNA (New York, N.Y.)
|June 3, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces an improved computational framework for predicting RNA secondary structures using diverse experimental data. The enhanced model offers more accurate RNA structure predictions by integrating various data types and refining modeling approaches.

Keywords:
RNA secondary structuredata-directedminimum free energyprobabilistic modelsstatistical inference

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Area of Science:

  • Computational Biology
  • Molecular Biology
  • Bioinformatics

Background:

  • Accurate RNA secondary structure prediction is crucial for understanding RNA function.
  • Existing computational methods often struggle with accuracy and integrating diverse experimental data.
  • Recent advances in RNA structure probing technologies generate large-scale, sensitive datasets.

Purpose of the Study:

  • To develop and evaluate an enhanced probabilistic framework for RNA secondary structure prediction.
  • To improve the integration of diverse experimental structure probing data into prediction algorithms.
  • To address limitations of existing methods by accommodating complex data dependencies and refining modeling.

Main Methods:

  • Implementation and extension of a probabilistic framework for RNA secondary structure prediction.
  • Utilizing direct likelihood-based calculations for pseudo-energy terms.
  • Integrating diverse structure probing data, including SHAPE reactivities.
  • Evaluating performance using real data and simulations, with refined modeling to address discrepancies.
  • Developing statistical preprocessing approaches for data standardization.

Main Results:

  • The enhanced framework successfully accommodates diverse data types and complex dependencies.
  • Integration of structural contexts leads to improved prediction accuracy compared to existing methods.
  • Refined modeling alleviates discrepancies between real data and simulations.
  • High reactivity data significantly drives SHAPE-directed predictions, highlighting the importance of understanding less informative reactivities.

Conclusions:

  • The developed probabilistic framework offers a robust and adaptable approach for RNA secondary structure prediction.
  • Effective integration of diverse experimental data and refined modeling are key to improving prediction accuracy.
  • Further improvements can be achieved by better understanding and utilizing less informative reactivity data.