mito-overview: a mitochondrial report software
- 1Medical College of Wisconsin
Abstract
Interpreting mitochondrial DNA variation requires relating allele representation and genomic annotation to the sequencing evidence available for each sample. We developed mito-overview to integrate mitochondrial coverage, candidate allele fractions, genomic annotations and long read co-occurrence in mitochondrial genomic reports. Analysis of a subset of public GM12878 long read data from Vandiver and colleagues Cas9 enrichment study of full-length mitochondrial genomes (SRR18110025) identified 16 candidate variants. Among these candidates, co-occurrence analysis showed that m.8860A>G and m.14831G>A occurred together in all 366 jointly callable query names, demonstrating how mito-overview extends individual allele measurements to quantify shared read support between mitochondrial sites. Analysis of public GM11906 short read data (GSE142741), pooled from three single-cell ATAC-seq libraries, recovered the previously characterized disease-associated m.8344A>G variant in MT-TK at an alternate allele fraction of 72.1% (740 of 1,027 callable observations). The recovered variant identity and MT-TK assignment agree with Lareau and colleagues original study, providing a disease-relevant example of mito-overview combined candidate detection and genomic annotation. We also examined ONT data from blood, fibroblast and muscle of one patient using a separate internal workflow. Heteroplasmy was estimated from alternate allele fractions calculated separately for each tissue. The respective analyses yielded 90, 52 and 60 candidates, with 19 candidate identities shared across all three lists. For these shared candidates, the tissue-specific estimates enabled examination of both variant presence and allele representation across the sampled tissues, illustrating the application of mitochondrial genomic reports to heteroplasmy assessment in disease research. By combining candidate identification with allele measurements, genomic annotations and shared read support, mito-overview enables researchers to examine which variants are observed, their representation within a sample and their co-occurrence with other mitochondrial alleles. These complementary measurements support the selection and characterization of candidate variants for further study in mitochondrial disease research.
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BibTeX
@misc{gai2026mitooverview,
title = {{mito-overview: a mitochondrial report software}},
author = {X. Gai and K. Ahuja and E. N. LOPES},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.27.762311},
url = {https://www.biorxiv.org/content/10.64898/2026.10.27.762311v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - mito-overview: a mitochondrial report software
AU - X. Gai
AU - K. Ahuja
AU - E. N. LOPES
PY - 2026
DA - 2026/10/27
PB - bioRxiv
DO - 10.64898/2026.10.27.762311
UR - https://www.biorxiv.org/content/10.64898/2026.10.27.762311v1
AB - Interpreting mitochondrial DNA variation requires relating allele representation and genomic annotation to the sequencing evidence available for each sample. We developed mito-overview to integrate mitochondrial coverage, candidate allele fractions, genomic annotations and long read co-occurrence in mitochondrial genomic reports. Analysis of a subset of public GM12878 long read data from Vandiver and colleagues Cas9 enrichment study of full-length mitochondrial genomes (SRR18110025) identified 16 candidate variants. Among these candidates, co-occurrence analysis showed that m.8860A>G and m.14831G>A occurred together in all 366 jointly callable query names, demonstrating how mito-overview extends individual allele measurements to quantify shared read support between mitochondrial sites. Analysis of public GM11906 short read data (GSE142741), pooled from three single-cell ATAC-seq libraries, recovered the previously characterized disease-associated m.8344A>G variant in MT-TK at an alternate allele fraction of 72.1% (740 of 1,027 callable observations). The recovered variant identity and MT-TK assignment agree with Lareau and colleagues original study, providing a disease-relevant example of mito-overview combined candidate detection and genomic annotation. We also examined ONT data from blood, fibroblast and muscle of one patient using a separate internal workflow. Heteroplasmy was estimated from alternate allele fractions calculated separately for each tissue. The respective analyses yielded 90, 52 and 60 candidates, with 19 candidate identities shared across all three lists. For these shared candidates, the tissue-specific estimates enabled examination of both variant presence and allele representation across the sampled tissues, illustrating the application of mitochondrial genomic reports to heteroplasmy assessment in disease research. By combining candidate identification with allele measurements, genomic annotations and shared read support, mito-overview enables researchers to examine which variants are observed, their representation within a sample and their co-occurrence with other mitochondrial alleles. These complementary measurements support the selection and characterization of candidate variants for further study in mitochondrial disease research.
ER -