Computational methods are progressively required for data reproducibility and quality control in Genetics. This is especially true for mitochondrial DNA (mtDNA), which is used in a variety of disciplines, from archeogenetics, population- and forensic genetics or as well as clinical disease association studies. New methods for data generation increase the sample output and the data volume per sample, requiring sophisticated computational methods for quality control. This work presents new algorithms and workflows to manage and analyze mtDNA data derived from high-throughput devices, like massive parallel sequencing data. One central aspect of this thesis is the classification of mtDNA data to phylogenetic clusters, used for mtDNA quality control and for detection of contamination patterns. Subsequently all required computational steps from sequence alignment, mapping, mutation detection, annotation and quality control are presented. To process the huge amount of data, all algorithms were parallelized within this work. The developed tools help researchers working with mtDNA data on a daily base with in-depth quality control and are highly accepted by the community. Additionally contamination can be uncovered in massive parallel sequencing studies, where mtDNA data is available to prevent false and misleading results.data is available to prevent false and misleading results.