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Related Concept Videos

RNA Structure01:23

RNA Structure

71.8K
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.
Different Types of RNA Have the Same Basic Structure
There are three main types of ribonucleic acid (RNA): messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). All three RNA types consist of a...
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RNA Stability01:53

RNA Stability

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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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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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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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Nonsense-mediated mRNA Decay02:27

Nonsense-mediated mRNA Decay

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The Upf proteins that carry out nonsense-mediated decay (NMD) are found in all eukaryotic organisms, including humans. Each protein has an individual role, but they need to work in collaboration. Upf1 is an ATP-dependent RNA helicase that unwinds the RNA helix. Because Upf1 can unwind any RNA, Upf2 and Upf3 are required to help Upf1 discriminate between nonsense and normal mRNAs.
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Predicting Molecular Geometry02:27

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VSEPR Theory for Determination of Electron Pair Geometries
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Related Experiment Video

Updated: Aug 3, 2025

RNA Secondary Structure Prediction Using High-throughput SHAPE
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RNA Secondary Structure Prediction Using High-throughput SHAPE

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StemP: A Fast and Deterministic Stem-Graph Approach for RNA Secondary Structure Prediction.

Mengyi Tang, Kumbit Hwang, Sung Ha Kang

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |April 7, 2023
    PubMed
    Summary
    This summary is machine-generated.

    We developed a new deterministic method for RNA secondary structure prediction using stem information, minimum stem length, and Stem-Loop scores. This approach accurately predicts structures, including pseudoknots, for short RNA and tRNA sequences efficiently.

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    Area of Science:

    • Computational Biology
    • Bioinformatics
    • Molecular Biology

    Background:

    • Accurate RNA secondary structure prediction is crucial for understanding RNA function.
    • Existing methods may face challenges with complex structures like pseudoknots or require significant computational resources.

    Purpose of the Study:

    • To introduce a novel, deterministic algorithm for predicting RNA secondary structures.
    • To identify key stem features essential for accurate structure prediction.

    Main Methods:

    • Developed a deterministic algorithm incorporating minimum stem length, Stem-Loop score, and stem co-existence.
    • Utilized graph notation to represent stems as vertices and their co-existence as edges, forming a Stem-graph.
    • Selected optimal sub-graphs from the Stem-graph based on energy matching for structure prediction.

    Main Results:

    • The algorithm demonstrated accurate structure predictions for short RNA and tRNA sequences.
    • Successfully predicted secondary structures, including those with pseudoknots.
    • The method proved efficient, yielding results in seconds on a standard laptop.

    Conclusions:

    • The proposed method offers a simple, flexible, and deterministic approach to RNA secondary structure prediction.
    • Minimum stem length, Stem-Loop score, and stem co-existence are important features for prediction.
    • The Stem-graph approach provides a comprehensive view of possible folding structures.