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Related Concept Videos

RNA Structure01:23

RNA Structure

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...
RNA Structure01:23

RNA Structure

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...
RNA Structure01:19

RNA Structure

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.
Different Types of RNA Have the Same Basic Structure
There are three main types of ribonucleic acid (RNA) involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). All three...
Nucleic Acid Structure01:25

Nucleic Acid Structure

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.
DNA Structure
DNA has a double-helix structure. The...
Ribozymes02:47

Ribozymes

The term ribozyme is used for RNA that can act as an enzyme. Ribozymes are mainly found in selected viruses, bacteria, plant organelles, and lower eukaryotes. Ribozymes were first discovered in 1982 when Tom Cech’s laboratory observed Group I introns acting as enzymes. This was shortly followed by the discovery of another ribozyme, Ribonulcease P, by Sid Altman’s laboratory. Both Cech and Altman received the Nobel Prize in chemistry in 1989 for their work on ribozymes.
Ribozymes can be...
RNA-seq03:21

RNA-seq

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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

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Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
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Directed acyclic graph kernels for structural RNA analysis.

Kengo Sato1, Toutai Mituyama, Kiyoshi Asai

  • 1Japan Biological Informatics Consortium (JBIC), 2-45 Aomi, Koto-ku, Tokyo 135-8073, Japan. sato-kengo@aist.go.jp

BMC Bioinformatics
|July 24, 2008
PubMed
Summary

We developed faster stem kernels for analyzing non-coding RNA (ncRNA) structures. This new method improves computational speed and accuracy for identifying ncRNAs and clustering RNA sequences.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Biology

Background:

  • Non-coding RNAs (ncRNAs) play crucial roles in biological processes.
  • Kernel methods, like support vector machines, are used for ncRNA analysis.
  • Existing stem kernels, while useful for RNA sequence similarity, face computational challenges with large datasets.

Purpose of the Study:

  • To enhance the computational efficiency of stem kernels for analyzing non-coding RNA sequences.
  • To introduce profile-profile stem kernels for multiple RNA sequence alignments.
  • To improve the accuracy of ncRNA detection and hierarchical clustering.

Main Methods:

  • Developed a novel technique using directed acyclic graphs (DAGs) derived from RNA base-pairing probability matrices.
  • Implemented profile-profile stem kernels that leverage base-pairing probability matrices for multiple alignments.
  • Optimized stem kernel computation speed.

Main Results:

  • The new DAG-based technique significantly accelerates stem kernel computations.
  • Profile-profile stem kernels demonstrate superior performance in detecting known ncRNAs.
  • The proposed methods achieved higher accuracy in kernel hierarchical clustering compared to existing approaches.

Conclusions:

  • Stem kernels are a dependable measure for structural RNA similarity.
  • The developed methods enhance the applicability of stem kernels in various kernel-based analyses.
  • This research provides a more efficient and accurate approach to ncRNA analysis.