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

Mitochondrial DNA (mtDNA) exists in multiple slightly different versions within a single cell — a phenomenon called heteroplasmy — and shifts in the balance between these versions are linked to aging, cancer, and inherited mitochondrial diseases. Until now, researchers analyzing heteroplasmy across hundreds of patients had no good way to combine, compare, and visualize all that data at once. Mito_Plot is a new free, open-source software pipeline that takes standard genetic variant files (VCFs) from mitochondrial sequencing, automatically calculates the proportion of mutant copies (Mutant Allele Frequency, MAF) at every position in the mitochondrial genome, and merges results across an entire cohort into a single aligned data matrix. The pipeline then generates interactive circular maps that overlay MAF values onto a gene-annotated mitochondrial genome, making mutation hotspots immediately visible. An optional 3D visualization mode further separates variant patterns by sample and genomic region, which is especially helpful for large datasets. The tool was validated on real multi-sample sequencing datasets, where it handled both rare low-frequency and common high-frequency variants robustly. Because the software is modular and open-source, it can slot into existing bioinformatics workflows without major reconfiguration.

• Mito_Plot automatically calculates MAF from allelic depth fields in standard mitochondrial VCF files and aggregates values across all samples into a unified position-aligned matrix, eliminating manual preprocessing steps.
• Interactive 2D circular plots annotated with mitochondrial gene boundaries allowed rapid visual identification of mutation hotspots and sample-specific variant patterns across multi-sample cohorts.
• Optional 3D visualization mode demonstrated improved interpretability for large cohorts by spatially separating variant distributions across both genomic position and sample axes, compared with static or single-sample approaches.
• The pipeline handled both low-MAF (rare heteroplasmic) and high-MAF (near-homoplasmic) variants robustly in real multi-sample mitochondrial sequencing datasets, with efficient processing scaling to large cohort sizes.

This is a bioinformatics methods/tool development study, not a clinical trial. The authors designed and implemented Mito_Plot as a modular, open-source computational pipeline accepting standard mitochondrial VCF files as input. Performance and utility were demonstrated through application to real multi-sample mitochondrial sequencing datasets covering a range of MAF values; specific cohort sizes and sequencing platforms are not detailed in the abstract. Validation was assessed by evaluating MAF quantification accuracy, cohort-level aggregation correctness, and comparative interpretability of 2D and 3D visualizations versus static single-sample tools.

Because this is a tool validation study rather than a clinical or experimental investigation, it cannot establish whether any specific heteroplasmic variant pattern causes disease or represents a biomarker; it only enables detection and visualization. The abstract does not specify the sequencing depth thresholds, variant callers, or cohort sizes used in validation, making it difficult to independently gauge sensitivity for very low-frequency heteroplasmy (e.g., <1% MAF). The pipeline's performance on long-read sequencing data (Oxford Nanopore, PacBio) — which can phase heteroplasmic variants — is not addressed and would be an important next benchmark. Future work pairing Mito_Plot outputs with phenotypic or clinical outcome data would clarify whether cohort-level MAF landscapes have disease-predictive value.

Mito_Plot directly extends the analytical infrastructure needed to pursue questions raised by multiple articles in this corpus. It parallels [§99], which investigates the downstream inflammatory consequences of mtDNA release during aging, by providing the upstream quantification machinery required to characterize the heteroplasmic variant landscapes that may drive such release. It also parallels [§116], which examines tissue-specific mitochondrial metabolic changes in aging mice, since cohort-scale MAF profiling enabled by Mito_Plot would allow researchers to ask whether heteroplasmy accumulation correlates with the tissue-specific dysfunction that study documents. More broadly, the tool extends [§71], a review of mitochondrial dysfunction in polycystic kidney disease, by supplying a practical pipeline that could operationalize large-scale mtDNA sequencing studies in disease cohorts of that type. Because Mito_Plot addresses methodology rather than a specific biological mechanism, it does not contradict any article in the corpus but instead serves as enabling infrastructure for the experimental and clinical investigations represented across it.

• Mitigating Mitochondrial RNA Release During Aging to Control Inflammation and Senescence
  Directly complementary: that study characterizes inflammatory consequences of mtDNA release during aging, while Mito_Plot provides the quantification framework needed to profile the heteroplasmic variants that may initiate such release — reading both together links detection method to biological outcome.
• Impact of aldehyde dehydrogenase 2 deficiency on tissue-specific mitochondrial metabolism in aging mice
  Mito_Plot could operationalize the mtDNA dimension of that study's tissue-specific mitochondrial metabolic findings in aging mice; comparing the two highlights how visualization tools like Mito_Plot translate sequencing data into mechanistic hypotheses.
• The Role of Mitochondria in Polycystic Kidney Disease
  As a disease-focused review of mitochondrial dysfunction in polycystic kidney disease, it illustrates exactly the clinical context in which cohort-scale heteroplasmy profiling via Mito_Plot would be applied — pairing them clarifies the translational use case.
• Heterogeneous Mitochondrial Gene Expression in SH-SY5Y Cells Following ERRa and ERRy Depletion Reveals Divergent Stress Responses
  That study examines heterogeneous mitochondrial gene expression at the cellular level, while Mito_Plot addresses heteroplasmy at the genomic variant level across cohorts — together they represent complementary layers (transcriptional vs. mutational) of mitochondrial heterogeneity analysis.