Human genetic variation can be present in many forms, including single nucleotide variants, small insertions/deletions, larger chromosomal gains and losses, and inter-chromosomal translocations. There is a need for robust and accurate algorithms to detect all forms of human genetic variation from large genomic data sets. The vast majority of such algorithms focus exclusively on the 24 chromosomes (22 auto-somes, X, and Y) comprising the nuclear genome. Usually ignored is the mitochondrial genome, despite the crucial role of the mitochondrion in cellular bioenergetics and the known roles of mitochondrial mutations in a number of human diseases [1], including cancer. The mitochondrial chromosome (mtDNA) may be present at up to tens of thousands of copies in a cell [2]. Therefore, variants may be present in a very small proportion of the cell's mtDNA copies, a condition known as heteroplasmy. Established computational tools used to identify biologically important nuclear DNA variants are often not adaptable to the mitochondrial genome. These tools have been developed to detect heterozygotic variants rather than heteroplasmic, so it is vitally important to develop new approaches to assess and quantify mtDNA genomic variation.
展开▼
