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RNA-seq

RNA-seq

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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Updated: Oct 15, 2025

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

Sensitive, reliable and robust circRNA detection from RNA-seq with CirComPara2.

Enrico Gaffo¹, Alessia Buratin², Anna Dal Molin¹

¹Department of Molecular Medicine, University of Padova, Padova, Italy.

Briefings in Bioinformatics

|October 26, 2021

Summary

We developed CirComPara2, a new computational pipeline for identifying circular RNAs (circRNAs) from RNA sequencing data. CirComPara2 achieves high accuracy and outperforms existing tools for comprehensive transcriptome characterization.

Keywords:

RNA-seq bioinformatics circRNAs computational pipeline

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Last Updated: Oct 15, 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:

Genomics
Bioinformatics
Molecular Biology

Background:

Circular RNAs (circRNAs) are a class of RNA molecules formed by back-splicing.
CircRNAs play roles in biological processes, diseases, and cancer.
Existing computational methods for circRNA identification have limitations in discovery rates and performance.

Purpose of the Study:

To develop an automated computational pipeline for improved circRNA discovery and quantification.
To enhance the accuracy and reliability of circRNA identification from RNA-seq data.

Main Methods:

Development of CirComPara2, an automated computational pipeline.
Integration of multiple circRNA detection methods within the pipeline.
Benchmark analysis comparing CirComPara2 against state-of-the-art tools.

Main Results:

CirComPara2 achieves high recall rates without compromising precision.
The pipeline demonstrated superior performance compared to existing circRNA discovery tools.
CirComPara2 proved to be a reliable and robust method for transcriptome characterization.

Conclusions:

CirComPara2 offers a significant advancement in circRNA identification and quantification.
The developed pipeline enables more comprehensive transcriptome analysis.
CirComPara2 is a valuable tool for researchers studying circRNAs in biological and disease mechanisms.