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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.
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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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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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Three main types of RNA are involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). These RNAs perform diverse functions and can be broadly classified as protein-coding or non-coding RNA. Non-coding RNAs play important roles in regulating gene expression in response to developmental and environmental changes. Non-coding RNAs in prokaryotes can be manipulated to develop more effective antibacterial drugs for human or animal use.
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RNA Secondary Structure Prediction Using High-throughput SHAPE
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Assessing Global-Local Secondary Structure Fingerprints to Classify RNA Sequences With Deep Learning.

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    Non-coding RNAs (ncRNAs) function is linked to structure. This study introduces secondary structure fingerprints to capture diverse RNA folding, improving function prediction accuracy over single structure models.

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    Area of Science:

    • Molecular Biology
    • Bioinformatics
    • Genomics

    Background:

    • Non-coding RNAs (ncRNAs) are crucial for biological processes and diseases.
    • RNA structure is vital for function, often conserved across species.
    • Predicting ncRNA function relies on secondary structure, but RNA can adopt multiple structures.

    Purpose of the Study:

    • To address limitations of using single RNA structures for function prediction.
    • To develop a novel method for capturing comprehensive RNA structural information.
    • To improve the accuracy of ncRNA function classification.

    Main Methods:

    • Proposed secondary structure fingerprints: RNA-As-Graphs (RAG) for high-level representation and free energy motifs for local structures.
    • Developed a deep learning architecture incorporating these fingerprints.
    • Evaluated performance using k-mers, specifically 6-mers.

    Main Results:

    • The proposed global-local structure fingerprints capture diverse RNA folding patterns.
    • Combining fingerprints with 6-mers achieved high classification performance.
    • Achieved 91.04% accuracy, 91.10% precision, and 91.00% recall.

    Conclusions:

    • Secondary structure fingerprints offer a more informative representation of ncRNA structure than single structures.
    • This approach enhances the accuracy of ncRNA function prediction.
    • The developed deep learning model demonstrates significant potential for ncRNA research.