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Updated: Dec 5, 2025

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

seekCRIT: Detecting and characterizing differentially expressed circular RNAs using high-throughput sequencing data.

Mohamed Chaabane¹, Kalina Andreeva², Jae Yeon Hwang¹

¹Department of Computer Science and Engineering, University of Louisville, Louisville, Kentucky, United States of America.

Plos Computational Biology

|October 20, 2020

Summary

This summary is machine-generated.

Researchers developed seekCRIT, a new tool to find differentially expressed circular RNAs (circRNAs) between conditions. This method identifies common circRNAs that change in abundance, advancing the study of these important RNA molecules.

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Last Updated: Dec 5, 2025

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

Published on: October 21, 2022

Quantification of Circular RNAs Using Digital Droplet PCR

Quantification of Circular RNAs Using Digital Droplet PCR

Published on: September 16, 2022

Area of Science:

Molecular Biology
Bioinformatics

Background:

Circular RNAs (circRNAs) are a special form of RNA produced by alternative splicing.
Current research often focuses on tissue-specific or disease-specific circRNAs, neglecting those common but differentially expressed between conditions.

Purpose of the Study:

To introduce seekCRIT, a novel computational tool for identifying differentially expressed circRNAs (DECs) between two conditions.
To address the gap in analyzing common circRNAs with altered abundance.

Main Methods:

Utilized high-throughput RNA sequencing data.
Applied the seekCRIT tool to analyze rat retina RNA-seq data from ischemic and normal conditions.
Employed quantitative PCR (qPCR) for validation.

Main Results:

Over 74% of identifiable circRNAs were expressed in both ischemic and normal conditions.
More than 40 circRNAs were found to be differentially expressed between the two conditions.
Achieved a 90% qPCR validation rate for DECs with a False Discovery Rate (FDR) < 5%.

Conclusions:

seekCRIT is an efficient and novel approach for detecting DECs from rRNA-depleted RNA-seq data.
The tool facilitates the study of circRNAs that are common but change in expression levels.
seekCRIT is freely available for research use.