Mito_Plot: open-source pipeline for quantification and visualization of mitochondrial DNA heteroplasmy

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Mitochondrial DNA heteroplasmy impacts cellular function, aging, and disease.
• High-throughput sequencing enables heteroplasmic variant detection, but cohort analysis is challenging.
• Existing tools lack scalability and user-friendliness for large-scale MAF visualization.

Purpose of the Study:

• To develop a scalable, user-friendly computational pipeline for analyzing mitochondrial DNA heteroplasmy.
• To standardize quantification and enable intuitive visualization of Mutant Allele Frequency (MAF) across multiple samples.
• To address limitations in existing tools for cohort-level MAF comparison and interpretation.

Main Methods:

• Developed Mito_Plot, an open-source pipeline accepting standard mitochondrial VCF files.
• Automated MAF calculation and aggregation of data into a unified matrix for cross-sample comparison.
• Implemented interactive 2D circular plots and optional 3D visualizations for MAF mapping and variant exploration.

Main Results:

• Mito_Plot efficiently processes large cohorts and handles variants with varying MAF values.
• Interactive visualizations facilitate rapid identification of mutation hotspots and sample-specific patterns.
• Demonstrated improved interpretability of mitochondrial variant landscapes compared to existing methods.

Conclusions:

• Mito_Plot provides scalable, user-friendly quantification and visualization of mtDNA MAF for large datasets.
• The pipeline supports reproducible research and integrates into existing bioinformatics workflows.
• Offers a practical resource for mitochondrial genomics research and clinical applications.