Currently submitted to: JMIR Bioinformatics and Biotechnology
Date Submitted: Jan 12, 2026
Open Peer Review Period: Jan 29, 2026 - Mar 26, 2026
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Circular Genome Comparison Tool (CGCT v1): Hybrid BLAST-Mauve-SibeliaZ Pairwise Visualization and Quantification Platform
ABSTRACT
Background:
Comparative genomics is essential for understanding evolutionary relationships, yet visualizing and analyzing circular genomes like plasmids and genomes of mitochondria or chloroplasts remains challenging. Current software often relies on fragmented, single-algorithm approaches that struggle to efficiently capture the complex architecture of non-coding regions and structural rearrangements.
Objective:
To address these limitations, we developed the Circular Genome Comparison Tool (CGCT), a hybrid platform designed to integrate global and local alignment strategies. This tool aims to provide a robust, interactive visualization of circular genomes, resolving both large-scale synteny and fine-scale nucleotide divergence in coding and non-coding regions.
Methods:
CGCT is implemented as a stand-alone Python-based desktop application that requires no external runtimes or internet connection. It employs a novel hybrid pipeline combining an improved progressiveMauve for global synteny, SibeliaZ for local topological adjacency, and BLASTn for sequence sensitivity, all accessed through an interactive visual interface for dynamic analysis and high-resolution export.
Results:
Validation on mitochondrial, plasmid, and chloroplast datasets showed CGCT effectively "sutures" circular topologies, and reveals hidden "pseudogene-gene graveyards" and ORFs not properly recognized by BLAST+. The hybrid approach resolved complex features like the mitochondrial D-loop and deep evolutionary homology in plant chloroplasts where single-algorithm methods frequently were insufficient.
Conclusions:
In conclusion, CGCT bridges the gap between global structure and local sensitivity, offering a comprehensive solution for circular genome analysis. By layering multi-algorithmic outputs into a single topology-aware framework, it enables researchers to reconstruct accurate evolutionary narratives and discover novel features without requiring advanced bioinformatics expertise.
Citation
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