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Related Concept Videos

RNA Structure01:19

RNA Structure

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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.
Different Types of RNA Have the Same Basic Structure
There are three main types of ribonucleic acid (RNA) involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). All three...
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RNA Structure01:23

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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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RNA-seq03:21

RNA-seq

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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

Nucleic Acid Structure

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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.
DNA Structure
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Protein Folding Quality Check in the RER01:29

Protein Folding Quality Check in the RER

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ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...
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RNA Secondary Structure Prediction Using High-throughput SHAPE
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RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

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Experiment-Assisted Secondary Structure Prediction with RNAstructure.

Zhenjiang Zech Xu1,2, David H Mathews3,4,5

  • 1Department of Biochemistry & Biophysics, University of Rochester Medical Center, 601 Elmwood Avenue, Box 712, Rochester, NY, 14642, USA.

Methods in Molecular Biology (Clifton, N.J.)
|September 26, 2016
PubMed
Summary
This summary is machine-generated.

Experimental probing data, including SHAPE reactivity, can significantly improve RNA secondary structure prediction accuracy. RNAstructure software integrates various experimental data types to refine RNA structure modeling and predictions.

Keywords:
Chemical modification dataRNA structure predictionSHAPEThermodynamics

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Accurate RNA secondary structure prediction is crucial for understanding RNA function.
  • Experimental probing methods provide valuable insights into RNA folding.
  • Integrating diverse experimental data can enhance prediction accuracy.

Purpose of the Study:

  • To provide protocols for utilizing experimental probing data in RNA structure prediction.
  • To demonstrate the application of RNAstructure software for integrating probing data.
  • To improve the accuracy of RNA secondary structure modeling.

Main Methods:

  • Utilizing enzymatic cleavage data (e.g., FMN cleavage).
  • Incorporating traditional chemical modification reactivity data.
  • Applying SHAPE (Selective 2'-hydroxyl acylation analyzed by primer extension) reactivity data.
  • Employing the RNAstructure software package for data integration and modeling.

Main Results:

  • Experimental probing data can be effectively integrated into RNAstructure.
  • The integration of diverse data types restrains and constrains secondary structure predictions.
  • Improved accuracy in RNA secondary structure prediction is achievable.

Conclusions:

  • RNAstructure facilitates the use of experimental probing data for enhanced RNA structure prediction.
  • Combining multiple experimental data sources leads to more accurate RNA secondary structure models.
  • This approach offers a powerful strategy for refining RNA structure predictions in molecular biology and bioinformatics.