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Related Concept Videos

Mismatch Repair01:20

Mismatch Repair

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Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
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Nucleic Acid Structure01:25

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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 Structure01:23

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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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Related Experiment Video

Updated: Jan 3, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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Alignment of Noncoding Ribonucleic Acids with Pseudoknots Using Context-Sensitive Hidden Markov Model.

Nayyer Mostaghim Bakhshayesh1, Mousa Shamsi1, Mohammad Hossein Sedaaghi2

  • 1Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran.

Journal of Medical Signals and Sensors
|November 19, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel algorithm using context-sensitive hidden Markov models (csHMMs) to accurately align RNA secondary structures, improving the identification of noncoding RNAs. The method shows high accuracy in classifying RNA families.

Keywords:
Context-sensitive hidden Markov modelsexpectation–maximization algorithmnoncoding ribonucleic acidsstructural alignment

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Predicting protein-coding genes is challenging with current signal processing techniques for RNA.
  • Gene network modeling has broad applications in medicine and agriculture, including drug discovery and disease treatment.
  • Accurate RNA identification is crucial for understanding biological functions and developing targeted therapies.

Purpose of the Study:

  • To design a context-sensitive hidden Markov model (csHMM) algorithm for aligning RNA secondary structures.
  • To enhance the identification of noncoding RNAs using structural alignment.
  • To compare RNA families and measure similarities in their structures.

Main Methods:

  • Developed a csHMM-based algorithm for RNA secondary structure alignment.
  • Employed an expectation-maximization algorithm to estimate csHMM parameters.
  • Validated the model on hepatitis delta virus and purine RNA families.

Main Results:

  • Achieved 83.33% accuracy, 89% specificity, and 97% sensitivity for hepatitis delta virus RNAs.
  • Attained 65% accuracy, 76% specificity, and 76% sensitivity for purine RNAs.
  • Demonstrated that csHMMs accurately align both primary sequences and secondary structures of RNAs.

Conclusions:

  • csHMMs provide a powerful tool for RNA secondary structure alignment.
  • The developed algorithm significantly improves the identification of noncoding RNAs.
  • This approach has implications for various fields, including disease research and drug development.