MMDIT: A tool for the deconvolution and interpretation of mitochondrial DNA mixtures

Sammed N. Mandape, Utpal Smart, Jonathan L. King, Melissa Muenzler, Kapema Bupe Kapema, Bruce Budowle, August E. Woerner

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Short tandem repeats of the nuclear genome have been the preferred markers for analyzing forensic DNA mixtures. However, when nuclear DNA in a sample is degraded or limited, mitochondrial DNA (mtDNA) markers provide a powerful alternative. Though historically considered challenging, the interpretation and analysis of mtDNA mixtures have recently seen renewed interest with the advent of massively parallel sequencing. However, there are only a few software tools available for mtDNA mixture interpretation. To address this gap, the Mitochondrial Mixture Deconvolution and Interpretation Tool (MMDIT) was developed. MMDIT is an interactive application complete with a graphical user interface that allows users to deconvolve mtDNA (whole or partial genomes) mixtures into constituent donor haplotypes and estimate random match probabilities on these resultant haplotypes. In cases where deconvolution might not be feasible, the software allows mixture analysis directly within a binary framework (i.e. qualitatively, only using data on allele presence/absence). This paper explains the functionality of MMDIT, using an example of an in vitro two-person mtDNA mixture with a ratio of 1:4. The uniqueness of MMDIT lies in its ability to resolve mixtures into complete donor haplotypes using a statistical phasing framework before mixture analysis and evaluating statistical weights employing a novel graph algorithm approach. MMDIT is the first available open-source software that can automate mtDNA mixture deconvolution and analysis. The MMDIT web application can be accessed online at The source code is available at and archived on zenodo (

Original languageEnglish
Article number102568
JournalForensic Science International: Genetics
StatePublished - Nov 2021


  • Bioinformatics
  • Forensic genetics
  • Graph algorithm, massively parallel sequencing
  • Mitochondrial DNA
  • Mixture deconvolution
  • Statistical phasing


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