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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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Related Experiment Video

Updated: May 17, 2025

Identification of Circular RNAs using RNA Sequencing
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Circular RNA discovery with emerging sequencing and deep learning technologies.

Jinyang Zhang1, Fangqing Zhao2,3,4

  • 1Institute of Zoology, Chinese Academy of Sciences, Beijing, China. zhangjinyang@ioz.ac.cn.

Nature Genetics
|April 17, 2025
PubMed
Summary

Circular RNAs (circRNAs) are novel RNA molecules with crucial roles in gene regulation and disease. Advanced sequencing and AI algorithms are improving their detection and functional analysis for biomedical applications.

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Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • Circular RNAs (circRNAs) are non-coding RNA molecules with a unique closed-loop structure.
  • circRNAs play significant roles in gene regulation and are implicated in various diseases.
  • Challenges in circRNA research include low expression levels and high sequence similarity to linear RNAs.

Purpose of the Study:

  • To review recent advancements in circRNA discovery, characterization, and functional analysis algorithms.
  • To highlight the impact of long-read and single-cell RNA sequencing on circRNA research.
  • To discuss the integration of large-scale circRNA sequencing data and the future role of AI.

Main Methods:

  • Review of recent literature on circRNA detection and analysis.
  • Discussion of technological advancements in RNA sequencing (long-read, single-cell).
  • Exploration of deep learning and AI-driven algorithms for circRNA research.

Main Results:

  • Recent technological and algorithmic breakthroughs have enabled high-resolution circRNA investigation.
  • Sophisticated algorithms are crucial for overcoming challenges in circRNA detection and characterization.
  • AI-driven approaches show promise for advancing circRNA research.

Conclusions:

  • Advances in sequencing and AI are revolutionizing circRNA research.
  • Addressing data integration challenges is key for future progress.
  • AI holds significant potential for unlocking circRNA's role in biomedicine.