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Deletions and duplications in mitochondrial DNA (mtDNA) cause mitochondrial disease and accumulate in conditions such as cancer and age-related disorders, but validated high-throughput methodology that can readily detect and discriminate between these two types of events is lacking. Here we establish a computational method, MitoSAlt, for accurate identification, quantification and visualization of mtDNA deletions and duplications from genomic sequencing data. Our method was tested on simulated sequencing reads and human patient samples with single deletions and duplications to verify its accuracy. Application to mouse models of mtDNA maintenance disease demonstrated the ability to detect deletions and duplications even at low levels of heteroplasmy., Author summary Deletions in the mitochondrial genome cause a wide variety of rare disorders, but are also linked to more common conditions such as neurodegeneration, diabetes type 2, and the normal ageing process. There is also a growing awareness that mtDNA duplications, which are also relevant for human disease, may be more common than previously thought. Despite their clinical importance, our current knowledge about the abundance, characteristics and diversity of mtDNA deletions and duplications is fragmented, and based to large extent on a limited view provided by traditional low-throughput analyses. Here, we describe a bioinformatics method, MitoSAlt, that can accurately map and classify mtDNA deletions and duplications using high-throughput sequencing. Application of this methodology to mouse models of mitochondrial deficiencies revealed a large number of duplications, suggesting that these may previously have been underestimated.