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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: Nov 9, 2025

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

Computational Analysis of circRNA Expression Data.

Giulio Ferrero^1,2, Nicola Licheri¹, Michele De Bortoli²

¹Department of Computer Science, University of Turin, Turin, Italy.

Methods in Molecular Biology (Clifton, N.J.)

|April 9, 2021

Summary

This summary is machine-generated.

Analyzing circular RNA (circRNA) expression from RNA-Seq data is complex. This study presents a reproducible computational workflow to predict, annotate, quantify, and analyze differential expression of circRNAs using public tools.

Keywords:

Circular RNAs Noncoding RNAs RNA sequencing

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Last Updated: Nov 9, 2025

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

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

Quantification of Circular RNAs Using Digital Droplet PCR

Quantification of Circular RNAs Using Digital Droplet PCR

Published on: September 16, 2022

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Circular RNAs (circRNAs) are a novel class of RNA molecules with diverse functions.
Analyzing circRNA expression from RNA-Seq data requires specialized algorithms and pipelines.
Existing methods for circRNA analysis can yield heterogeneous results.

Purpose of the Study:

To present a comprehensive and computationally reproducible workflow for circRNA expression analysis.
To integrate various public tools into an easy-to-use pipeline for circRNA analysis.
To illustrate key steps including prediction, annotation, quantification, and differential expression analysis.

Main Methods:

Utilized RNA-Seq data from a public experiment.
Developed a computational pipeline integrating multiple public tools.
Implemented steps for circRNA prediction, annotation, classification, sequence reconstruction, quantification, and differential expression analysis.

Main Results:

Demonstrated a complete workflow for circRNA expression analysis.
Showcased the integration of diverse bioinformatics tools.
Provided a reproducible method for analyzing circRNA expression from RNA-Seq data.

Conclusions:

A standardized and reproducible computational workflow facilitates robust circRNA expression analysis.
Leveraging public tools within integrated pipelines enhances the accessibility of circRNA research.
This approach enables detailed investigation of circRNA expression patterns and differential expression.