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Related Concept Videos

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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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.
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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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Updated: Jun 10, 2025

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
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RNAelem: an algorithm for discovering sequence-structure motifs in RNA bound by RNA-binding proteins.

Hiroshi Miyake1, Risa Karakida Kawaguchi2, Hisanori Kiryu1

  • 1Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, Chiba 277-8561, Japan.

Bioinformatics Advances
|October 14, 2024
PubMed
Summary
This summary is machine-generated.

RNAelem predicts RNA-binding protein (RBP) sequence-structure motifs. This tool enhances RBP-binding site analysis by combining grammar and energy models, revealing novel structural motifs and interactions.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Biology

Background:

  • RNA-binding proteins (RBPs) are critical for post-transcriptional gene regulation.
  • Identifying specific RNA patterns recognized by RBPs is a key bioinformatics challenge.
  • Current deep learning models for RBP-binding site prediction lack interpretability.

Purpose of the Study:

  • To develop an interpretable computational tool for predicting sequence-structure motifs in RBP-binding regions.
  • To improve the understanding of RNA-binding protein specificity.

Main Methods:

  • Developed RNAelem, integrating profile context-free grammar with the Turner energy model for RNA secondary structure.
  • Applied RNAelem to predict sequence-structure motifs in RBP-binding regions.

Main Results:

  • RNAelem demonstrated superior accuracy in detecting RNA sequences with structural motifs compared to existing methods.
  • Identified known primary sequence motifs and discovered novel secondary structural motifs, including sequence-nonspecific regions.
  • Provided interpretable insights, such as long-range base-pairing interactions for U2AF protein.

Conclusions:

  • RNAelem offers an accurate and interpretable approach for analyzing RBP-RNA interactions.
  • The tool facilitates the discovery of complex sequence-structure motifs governing RBP binding.
  • RNAelem advances the study of post-transcriptional regulation by elucidating structural determinants of RBP specificity.