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Related Experiment Video

Updated: Feb 2, 2026

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

circtools-a one-stop software solution for circular RNA research.

Tobias Jakobi^1,2, Alexey Uvarovskii^1,2, Christoph Dieterich^1,2

¹Department of Cardiology, Angiology, and Pneumology, University Hospital Heidelberg.

Bioinformatics (Oxford, England)

|November 22, 2018

Summary

This summary is machine-generated.

This study introduces circtools, a Python framework for analyzing circular RNAs (circRNAs). It offers a harmonized workflow for circRNA detection, characterization, and functional insights, addressing the scarcity of available analysis tools.

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Identification of Circular RNAs using RNA Sequencing

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Quantification of Circular RNAs Using Digital Droplet PCR

Quantification of Circular RNAs Using Digital Droplet PCR

Published on: September 16, 2022

Area of Science:

Computational biology
Bioinformatics
Genomics

Background:

Circular RNAs (circRNAs) are generated via back-splicing and are highly specific to cell type and developmental stage.
CircRNA detection from high-throughput sequencing data is a complex bioinformatics process requiring robust analytical tools.
Existing tools for circRNA prediction are numerous, but those for downstream characterization and analysis are limited.

Purpose of the Study:

To provide a comprehensive, harmonized bioinformatics workflow for computational circRNA analysis.
To develop a modular, Python-based framework (circtools) for various stages of circRNA research.
To facilitate in silico analysis of circRNAs from prediction to functional insights.

Main Methods:

Development of circtools, a modular Python framework.
Implementation of modules for circRNA detection, sequence reconstruction, and quality control.
Inclusion of modules for statistical testing, RBP binding site analysis, RNase R resistance assessment, and primer design.

Main Results:

circtools offers a unified workflow for computational circRNA analysis.
The framework includes modules for detection, characterization, functional insights, and visualization.
Supports data export in common formats and includes options for quality checking and statistical testing.

Conclusions:

circtools provides a valuable resource for researchers studying circRNAs.
The framework streamlines circRNA analysis, from initial detection to functional exploration.
Enhances the ability to gain insights into circRNA biology through computational methods.