Docker4Circ: A Framework for the Reproducible Characterization of circRNAs from RNA-Seq Data

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Circular RNAs (circRNAs) are novel transcript isoforms with roles in gene regulation and potential as disease biomarkers.
• Existing circRNA analysis workflows lack integration and computational reproducibility.

Purpose of the Study:

• To develop Docker4Circ, an integrated and reproducible workflow for comprehensive circRNA analysis from RNA-Seq data.
• To enhance accessibility of circRNA analysis for researchers.

Main Methods:

• Docker4Circ encapsulates circRNA prediction, classification, annotation, back-splice reconstruction, and quantification into Docker images.
• Utilizes an R interface, Java GUI, and alignment-free quantification for ease of use.
• Ensures computational reproducibility following established bioinformatics guidelines.

Main Results:

• Docker4Circ provides a complete pipeline for circRNA analysis in human and model organisms.
• The workflow includes prediction, annotation, quantification, and differential expression analysis.
• Offers an accessible and user-friendly platform, reducing the need for advanced scripting.

Conclusions:

• Docker4Circ facilitates easier and more reproducible circRNA analysis from RNA-Seq data.
• The tool enhances accessibility for researchers studying gene regulation and disease biomarkers.
• Promotes standardization and reproducibility in circRNA bioinformatics research.