Mitochondria represents one of the most essential, investigated organelles of
eukaryotic cells. Due to the relevance of the functions, especially cellular respiration,
mitochondria are subject to continuous oxidative stress stimuli that, over time, can
impair this distinct genome, leading, for example, to several neurodegenerative and
age-related diseases. Today, the growth of next generation sequencing techniques
allows researchers to improve variant detection of mtDNA, increasing, in the
meantime, the quantity and complexity of data produced, making molecular diagnosis
of mitochondrial diseases more challenging. The main issues that will be faced working
with mtDNA high-throughput sequencing deal with detection and interpretation of low
heteroplasmy and homoplasmy levels, variants unrelated to exhibited phenotype and
identification of variants of unknown significance (VUS). To perform an accurate
analysis of mtDNA variants produced by next generation sequencing experiments, we
propose an integrated approach that foresees the complementary use of the most recent
algorithms applied to mtDNA data, trying to extract the maximum from each one. This
workflow foresaw four macro-phases (mitogenome alignment/assembly, variant
calling, variant annotation and in-silico variant effects predictions), each one
characterized by a mixed output coming from several tools and databases rich in
complementary information on mtDNA variants. In this way, a superior quality output
could be obtained, leading to improved genetic counseling for patients affected by
primary mitochondrial pathologies.
Keywords: AVM, CCM, CLC Genomics Workbench, Mitochondria, mtDNA,
mtDNA-Server, Mitos2, MitoBreak, MVTools, Mitoweb, MtoolBox, MitoTip,
MitImpact 3D, RNA-Seq, RP, SMART2, TRIMITOMICS, VUS, Variants, WES.
