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Related Concept Videos

RNA Structure01:23

RNA Structure

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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
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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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RNA Stability01:53

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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 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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RNA Editing02:23

RNA Editing

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RNA editing is a post-transcriptional modification where a precursor mRNA (pre-mRNA) nucleotide sequence is changed by base insertion, deletion, or modification. The extent of RNA editing varies from a few hundred bases, in mitochondrial DNA of trypanosomes, to a just single base, in nuclear genes of mammals. Even a single base change in the pre-mRNA can convert a codon for one amino acid into the codon for another amino acid or a stop codon. This type of re-coding can significantly affect the...
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Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
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Deep learning for RNA structure prediction.

Jiuming Wang1, Yimin Fan1, Liang Hong1

  • 1Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China.

Current Opinion in Structural Biology
|February 11, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and deep learning methods are revolutionizing RNA structure prediction, offering higher accuracy and efficiency than traditional computational approaches. This review summarizes AI applications, advances, and future opportunities in predicting RNA secondary structures.

Keywords:
Artificial intelligenceDeep learningRNA structure prediction

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Molecular Biology

Background:

  • Experimental RNA structure determination is costly and time-consuming.
  • Computational methods are crucial for predicting RNA structures from sequences.
  • Recent advancements in AI have significantly improved RNA structure prediction accuracy and efficiency.

Purpose of the Study:

  • To review the applications of Artificial Intelligence (AI), particularly deep learning, in RNA structure prediction.
  • To summarize methodological advancements in AI-driven RNA structure prediction.
  • To highlight current challenges and future opportunities in the field.

Main Methods:

  • Review of existing literature on AI and deep learning models for RNA structure prediction.
  • Analysis of methodologies employed by various AI models.
  • Synthesis of findings on accuracy, efficiency, and limitations.

Main Results:

  • AI, especially deep learning, has demonstrated significant improvements in RNA structure prediction.
  • Various deep learning architectures have been successfully applied, showing enhanced performance.
  • The review identifies key trends and emerging techniques in AI-based RNA structure prediction.

Conclusions:

  • AI and deep learning are transformative tools for RNA structure prediction.
  • Continued research is needed to address remaining challenges and explore new opportunities.
  • The field is rapidly evolving, promising more accurate and efficient RNA structure prediction in the future.