Abstract
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.
Publication types
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Research Support, Non-U.S. Gov't
MeSH terms
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Animals
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DNA, Mitochondrial / chemistry
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DNA, Mitochondrial / genetics*
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Gene Deletion*
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Gene Duplication*
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High-Throughput Nucleotide Sequencing / methods*
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High-Throughput Nucleotide Sequencing / standards
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Mice
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Reproducibility of Results
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Sequence Analysis, DNA / methods*
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Sequence Analysis, DNA / standards
Grants and funding
The work described here was supported by the Swedish Research Council (2018-02439 to M.F., 2017-01257 to C.M.G., and 2018-02852 to E.L.), the Swedish Cancer Foundation (2019-816 to M.F., 2017-631 to C.M.G., and 2018-747 to E.L.), the Knut and Alice Wallenberg Foundation (KAW 2017.0080 to M.F. and KAW 2015.0144 to E.L.), the European Research Council (683191 to M.F.) and grants from the Swedish state under the agreement between the Swedish government and the county councils, the ALF agreement (ALFGBG-727491 to M.F., and ALFGBG-728151 to C.M.G). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.