MitoGEx: An Integrated Platform for Streamlined Human Mitochondrial Genome Analysis
Issued Date
2026-03-01
Resource Type
eISSN
20734425
Scopus ID
2-s2.0-105033926585
Journal Title
Genes
Volume
17
Issue
3
Rights Holder(s)
SCOPUS
Bibliographic Citation
Genes Vol.17 No.3 (2026)
Suggested Citation
Jeenkeawpiam K., Srifa P., Nokchan N., Khongcharoen N., Binkasem A., Sangkhathat S. MitoGEx: An Integrated Platform for Streamlined Human Mitochondrial Genome Analysis. Genes Vol.17 No.3 (2026). doi:10.3390/genes17030338 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/116003
Title
MitoGEx: An Integrated Platform for Streamlined Human Mitochondrial Genome Analysis
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
Background/Objectives: Mitochondrial DNA (mtDNA) is an important resource for understanding human ancestry, population diversity, and the molecular mechanisms of mitochondrial diseases. However, analyzing mtDNA thoroughly often requires advanced bioinformatics skills and command-line knowledge. To address this challenge, we created Mitochondrial Genome Explorer (MitoGEx), a user-friendly computational pipeline optimized for human mtDNA analysis that combines multiple mtDNA analysis modules within a single graphical user interface. Methods: The platform simplifies key analytical steps, such as quality control, sequence alignment, alignment quality assessment, variant detection, haplogroup classification, and phylogenetic reconstruction. Users can choose between Quick and Advanced modes, which offer default settings or customizable options based on their analysis needs. To demonstrate its effectiveness, we analyzed 15 whole-exome sequencing (WES) samples from Songklanagarind Hospital using MitoGEx. Results: The sequencing data were of high quality, with over 92 percent of bases scoring above a Phred score and consistent GC content across all samples. Variant detection using the GATK mitochondrial pipeline and annotation with ANNOVAR and the MitImpact database revealed multiple high-confidence variants. Haplogroup classification with Haplogrep 3 and phylogenetic analysis with IQ-TREE 2 confirmed diverse maternal lineages within the cohort. Conclusions: Taken together, MitoGEx facilitates mitochondrial genome analysis in a reproducible and accessible manner for both research and clinical bioinformatics applications. The analytical results produced by MitoGEx are concordant with those obtained using standalone bioinformatic tools, demonstrating analytical correctness. By integrating all analysis steps into a single automated workflow, MitoGEx reduces execution time and limits human error inherent to manual, multi-step pipelines.