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Related Concept Videos

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.
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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 acids are the most important macromolecules for the continuity of life. They carry the cell's genetic blueprint and carry instructions for its functioning.
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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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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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RNA Secondary Structure Prediction Using High-throughput SHAPE
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Machine learning in RNA structure prediction: Advances and challenges.

Sicheng Zhang1, Jun Li1, Shi-Jie Chen2

  • 1Department of Physics and Institute of Data Science and Informatics, University of Missouri, Columbia, Missouri.

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|February 1, 2024
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Summary
This summary is machine-generated.

Machine learning models show promise for predicting RNA structures, crucial for biological functions. This perspective covers strategies, challenges in 2D/3D RNA structure prediction, and RNA language models.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Molecular biology

Background:

  • RNA molecules are essential for biological processes, with structure dictating function.
  • Advancements in protein structure prediction using machine learning offer potential for RNA structure prediction.
  • Understanding RNA structure is key to deciphering its diverse biological roles.

Purpose of the Study:

  • To discuss the progress and hurdles in developing machine learning models for RNA structure prediction.
  • To explore strategies for building these models and address challenges in predicting both secondary (2D) and tertiary (3D) RNA structures.
  • To highlight the benefits and difficulties associated with creating RNA language models.

Main Methods:

  • Review of current machine learning techniques applied to RNA structure prediction.
  • Analysis of model-building strategies for RNA secondary and tertiary structure prediction.
  • Evaluation of RNA language models in the context of structure prediction.

Main Results:

  • Machine learning approaches are increasingly viable for RNA structure prediction.
  • Specific challenges exist for predicting 2D and 3D RNA structures, with ongoing development of solutions.
  • RNA language models present unique advantages and challenges in this field.

Conclusions:

  • Machine learning-based models are poised to become vital tools for RNA structure prediction.
  • These tools will enhance our comprehension of RNA structures and their functional implications.
  • Continued research in machine learning will accelerate discoveries in RNA biology.