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Related Concept Videos

RNA Splicing01:32

RNA Splicing

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Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...
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Ribosomal RNA Synthesis02:53

Ribosomal RNA Synthesis

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Ribosome synthesis is a highly complex and coordinated process involving more than 200 assembly factors. The synthesis and processing of ribosomal components occurs not only in the nucleolus but also in the nucleoplasm and the cytoplasm of eukaryotic cells.
Ribosome biogenesis begins with the synthesis of 5S and 45S pre-rRNAs by distinct RNA polymerases. The primary transcripts are extensively processed and modified before they are bound and folded by ribosomal proteins and assembly factors,...
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RNA Stability01:53

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Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...
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RNA Interference01:23

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RNA interference (RNAi) is a process in which a small non-coding RNA molecule blocks the post-transcriptional expression of a gene by binding to its messenger RNA (mRNA) and preventing the protein from being translated.
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Transfer RNA Synthesis02:36

Transfer RNA Synthesis

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One of the unique features of tRNA is the presence of modified bases. In some tRNAs, modified bases account for nearly 20% of the total bases in the molecule. Altogether, these unusual bases protect the tRNA from enzymatic degradation by RNases.
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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.
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RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA
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circDeep: deep learning approach for circular RNA classification from other long non-coding RNA.

Mohamed Chaabane1, Robert M Williams1, Austin T Stephens1

  • 1Department of Computer Engineering and Computer Science, Louisville, KY 40208, USA.

Bioinformatics (Oxford, England)
|July 4, 2019
PubMed
Summary
This summary is machine-generated.

A new deep learning framework, circDeep, accurately identifies circular RNAs (circRNAs) from long non-coding RNAs (lncRNAs). This advancement improves disease research by enhancing the speed and precision of circRNA detection, crucial for understanding their roles.

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Last Updated: Jan 22, 2026

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Identification of Circular RNAs using RNA Sequencing
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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Circular RNAs (circRNAs) are crucial regulators of microRNA activity and are implicated in diseases like cancer.
  • Accurate detection of circRNAs is vital for understanding their biogenesis and function.
  • Current methods for distinguishing circRNAs from other long non-coding RNAs (lncRNAs) have accuracy limitations.

Purpose of the Study:

  • To develop a highly accurate and fast machine learning method for identifying circular RNAs.
  • To improve the systematic annotation of circRNAs by addressing classification challenges.

Main Methods:

  • An End-to-End deep learning framework named circDeep was developed.
  • circDeep integrates RCM, ACNN-BLSTM sequence, and conservation descriptors.
  • The framework utilizes high-level abstraction descriptors with shared representations across modalities.

Main Results:

  • circDeep demonstrates superior performance compared to existing tools.
  • The framework achieves a 12% increase in accuracy for circRNA identification.
  • circDeep is significantly faster than current available tools.

Conclusions:

  • circDeep offers a more accurate and efficient approach to circRNA classification.
  • This advancement facilitates better understanding of circRNA roles in disease.
  • The developed framework is publicly available for research use.