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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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Pre-mRNA Processing: Modification of pre-mRNA Ends01:35

Pre-mRNA Processing: Modification of pre-mRNA Ends

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In eukaryotic cells, transcripts made by RNA polymerase are modified and processed before exiting the nucleus. Unprocessed RNA is called precursor mRNA or pre-mRNA to distinguish it from mature mRNA.
Once about 20-40 ribonucleotides have been joined together by RNA polymerase, a group of enzymes adds a cap to the 5' end of the growing transcript. In this process, a 5' phosphate is replaced by modified guanosine that has a methyl group attached (7-methyl guanosine). This 5' cap helps...
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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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RNA Structure01:19

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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.
Different Types of RNA Have the Same Basic Structure
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Ribosome Profiling02:24

Ribosome Profiling

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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.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
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RNA Editing02:23

RNA Editing

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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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Related Experiment Video

Updated: Dec 15, 2025

Author Spotlight: Decoding RNA Methylation's Role in Pancreatic Cancer - A Single-Base Resolution Study
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Prediction of m5C Modifications in RNA Sequences by Combining Multiple Sequence Features.

Lijun Dou1, Xiaoling Li2, Hui Ding3

  • 1School of Automotive and Transportation Engineering, Shenzhen Polytechnic, Shenzhen, China; Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.

Molecular Therapy. Nucleic Acids
|July 10, 2020
PubMed
Summary

We developed iRNA-m5C_SVM, a machine learning tool to accurately predict 5-Methylcytosine (m5C) RNA modifications in plants. This SVM-based approach offers a faster, more cost-effective alternative to traditional methods for identifying crucial RNA changes.

Keywords:
5-methylcytosinePC-PseDNC-generalelectron-ion interaction pseudopotentials of trinucleotidenucleotide compositionposition-specific propensitysupport vector machine

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Methylated RNA Immunoprecipitation Assay to Study m5C Modification in Arabidopsis
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Methylated RNA Immunoprecipitation Assay to Study m5C Modification in Arabidopsis

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Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data
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Author Spotlight: Decoding RNA Methylation's Role in Pancreatic Cancer - A Single-Base Resolution Study
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Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data
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Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Computational Biology

Background:

  • 5-Methylcytosine (m5C) is a critical RNA modification involved in RNA metabolism and stress responses.
  • Traditional methods for m5C identification are time-consuming and costly, hindering research in the era of big data.
  • Accurate and efficient prediction of m5C sites is essential for understanding its biological roles.

Purpose of the Study:

  • To develop a novel machine learning tool, iRNA-m5C_SVM, for accurate prediction of RNA m5C modification sites.
  • To combine multiple sequence features to enhance the predictive performance of m5C site identification.
  • To provide a cost-effective and rapid alternative to experimental methods for m5C site detection in plants.

Main Methods:

  • A Support Vector Machine (SVM) algorithm was employed as the core predictive model.
  • Eight feature extraction methods were systematically investigated to identify optimal sequence descriptors.
  • Four high-performing features, including Position-Specific Propensity (PSP), Nucleotide Composition (NAC, DNC, TNC), Electron-Ion Interaction Pseudopotentials (PseEIIPs), and PC-PseDNC-general, were integrated.

Main Results:

  • The iRNA-m5C_SVM model achieved high predictive accuracies of 73.06% (10-fold cross-validation) and 80.15% (independent test).
  • The integrated feature set significantly improved the performance compared to individual features.
  • The model demonstrated superior predictive performance for m5C sites in *Arabidopsis thaliana* compared to existing methods.

Conclusions:

  • iRNA-m5C_SVM provides a highly accurate and efficient computational tool for predicting m5C modification sites in plants.
  • This machine learning approach offers a promising alternative for large-scale m5C site identification, facilitating further research.
  • The study highlights the effectiveness of combining diverse sequence features for robust RNA modification prediction.