Barresi Marco, Dal Santo Giulia, Izzo Rossella, Zauli Andrea, Lamantea Eleonora, Caporali Leonardo, Ghezzi Daniele, Legati Andrea
Unit of Medical Genetics and Neurogenetics, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20126 Milan, Italy.
Department of Pathophysiology and Transplantation (DEPT), University of Milan, 20122 Milan, Italy.
BioTech (Basel). 2025 Feb 12;14(1):9. doi: 10.3390/biotech14010009.
The unique features of mitochondrial DNA (mtDNA), including its circular and multicopy nature, the possible coexistence of wild-type and mutant molecules (i.e., heteroplasmy) and the presence of nuclear mitochondrial DNA segments (NUMTs), make the diagnosis of mtDNA diseases particularly challenging. The extensive deployment of next-generation sequencing (NGS) technologies has significantly advanced the diagnosis of mtDNA-related diseases. However, the vast amounts and diverse types of sequencing data complicate the interpretation of these variants. From sequence alignment to variant calling, NGS-based mtDNA sequencing requires specialized bioinformatics tools, adapted for the mitochondrial genome. This study presents the use of new bioinformatics approaches, optimized for short- and long-read sequencing data, to enhance the accuracy of mtDNA analysis in diagnostics. Two recent and emerging free bioinformatics tools, Mitopore and MitoSAlt, were evaluated on patients previously diagnosed with single nucleotide variants or large-scale deletions. Analyses were performed in Linux-based environments and web servers implemented in Python, Perl, Java, and R. The results indicated that each tool demonstrated high sensitivity and specific accuracy in identifying and quantifying various types of pathogenic variants. The study suggests that the integrated and parallel use of these tools offers a significant advantage over traditional methods in interpreting mtDNA genetic variants, reducing the computational demands, and provides an accurate diagnostic solution.
线粒体DNA（mtDNA）的独特特征，包括其环状和多拷贝性质、野生型和突变分子可能共存（即异质性）以及核线粒体DNA片段（NUMTs）的存在，使得mtDNA疾病的诊断极具挑战性。下一代测序（NGS）技术的广泛应用显著推动了与mtDNA相关疾病的诊断。然而，大量且多样的测序数据使这些变异的解读变得复杂。从序列比对到变异检测，基于NGS的mtDNA测序需要适用于线粒体基因组的专门生物信息学工具。本研究介绍了针对短读长和长读长测序数据进行优化的新生物信息学方法的应用，以提高诊断中mtDNA分析的准确性。对两种最新出现的免费生物信息学工具Mitopore和MitoSAlt，在先前诊断为单核苷酸变异或大规模缺失的患者身上进行了评估。分析在基于Linux的环境以及用Python、Perl、Java和R实现的网络服务器中进行。结果表明，每种工具在识别和定量各种类型的致病变异方面都表现出高灵敏度和特异性准确性。该研究表明，这些工具的综合并行使用在解读mtDNA遗传变异方面比传统方法具有显著优势，减少了计算需求，并提供了准确的诊断解决方案。
