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Related Experiment Video

Updated: Oct 20, 2025

Identification of Circular RNAs using RNA Sequencing
08:25

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

12.4K

A genome-wide circular RNA transcriptome in rat.

Disha Sharma1,2, Paras Sehgal3,2, Sridhar Sivasubbu3,2

  • 1GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Mathura Road, Delhi 110025, India.

Biology Methods & Protocols
|September 16, 2021
PubMed
Summary
This summary is machine-generated.

This study maps circular RNAs (circRNAs) in rats using multiple computational tools, identifying tissue-specific and development-stage specific circRNAs. Validation confirmed novel circRNA expression, enhancing our understanding of rat circRNA landscapes.

Keywords:
RNA-SeqcircRNAcircular RNAnoncoding RNAsrattranscriptome

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Last Updated: Oct 20, 2025

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Circular RNAs (circRNAs) are a novel class of noncoding RNAs with a unique circular structure.
  • While circRNAs are identified in many organisms, comprehensive studies in rats are limited.
  • Existing computational pipelines for circRNA discovery vary in performance, necessitating comparative analysis.

Purpose of the Study:

  • To explore the circRNA transcriptome landscape in rats across different tissues, developmental stages, and genders.
  • To compare the efficacy of different computational pipeline combinations for high-confidence circRNA identification.
  • To identify and validate tissue-specific, development-stage specific, and gender-specific circRNAs in rats.

Main Methods:

  • Utilized a comprehensive rat transcriptome dataset (11 tissues, 4 developmental stages, 2 genders).
  • Employed five different combinations of computational tools to identify circRNAs.
  • Performed comparative analysis of pipelines based on alignment, circRNA count, and read coverage.
  • Validated selected circRNA junctions and quantified expression of candidate circRNAs using RT-qPCR.

Main Results:

  • Identified a significant number of circRNAs across various rat tissues and developmental stages.
  • Demonstrated differences in circRNA identification efficiency among various computational pipeline combinations.
  • Discovered and validated tissue-specific, development-stage specific, and gender-specific circRNAs.
  • Quantitatively confirmed expression of three tissue-enriched circRNA candidates.

Conclusions:

  • This study provides one of the most comprehensive circRNA transcriptome maps in rats to date.
  • The findings highlight the importance of using combined computational tools for robust circRNA discovery.
  • The identified circRNAs offer valuable resources for future research into gene regulation and function in rats.