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Related Concept Videos

RNA Structure01:23

RNA Structure

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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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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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DNA...
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Improving Translational Accuracy02:07

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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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. 
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Conserved Binding Sites01:49

Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Nucleic Acids02:43

Nucleic Acids

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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.
DNA and RNA
The two main types of nucleic acids are deoxyribonucleic acid (DNA) and ribonucleic acid (RNA). DNA is the genetic material in all living organisms, ranging from single-celled bacteria to multicellular mammals. It is in the nucleus of eukaryotes and in the organelles, chloroplasts, and mitochondria. In prokaryotes,...
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Updated: Jun 21, 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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LinearAlifold: Linear-time consensus structure prediction for RNA alignments.

Apoorv Malik1, Liang Zhang1, Milan Gautam1

  • 1School of EECS, Oregon State University, Corvallis, OR 97330, USA.

Journal of Molecular Biology
|July 6, 2024
PubMed
Summary
This summary is machine-generated.

We developed LinearAlifold, a fast RNA structure prediction tool that significantly outperforms RNAalifold for large genomic datasets. This new method accelerates viral diagnostics and therapeutics development by enabling rapid analysis of conserved RNA structures.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Predicting conserved RNA structures from homologous sequences aids in identifying functional elements crucial for viral diagnostics and therapeutics.
  • Existing tools like RNAalifold struggle with large datasets due to cubic time complexity, limiting their application for long RNA sequences.

Purpose of the Study:

  • To develop a significantly faster and accurate method for predicting consensus RNA secondary structures.
  • To overcome the computational limitations of existing tools for analyzing large sets of RNA sequences.

Main Methods:

  • LinearAlifold, a novel algorithm based on LinearFold, achieves linear time complexity concerning sequence length and number of sequences.
  • The tool was evaluated against RNAalifold using a dataset of 400 SARS-CoV-2 and related genomes, comparing prediction accuracy and speed.

Main Results:

  • LinearAlifold demonstrates a substantial speedup (approximately 36x) compared to RNAalifold, completing analysis in 0.7 hours versus over a day.
  • The predictions from LinearAlifold showed higher accuracy against known structures and better correlation with experimental data for SARS-CoV-2 than RNAalifold.
  • The tool supports multiple energy models and prediction modes, processing hundreds of SARS-CoV variants in under an hour.

Conclusions:

  • LinearAlifold offers a computationally efficient and accurate solution for predicting consensus RNA structures, particularly for large-scale genomic studies.
  • This advancement has significant implications for accelerating research in viral diagnostics, therapeutics, and understanding RNA genome organization.