Separation of SNP profiles from DNA mixtures with two contributors via massively parallel sequencing technology

Massively parallel sequencing technology offers the opportunity to analyse forensically challenging samples, such as degraded samples and mixtures. In the current study, we developed a perl-based pipeline to separate the DNA mixture into its components and to predict the most probable single nucleotide polymorphism (SNP) genotypes of each contributor to the mixed profile. We examined the usefulness of this method by detecting both artificially constructed DNA mixtures and mixtures from crime cases using the Precision ID Identity Panel on the Ion PGM platform. The separated genotypes of mixtures were validated by genotypes of each of the donors detected independently. The results indicate that the method performed well in identifications of both the artificially constructed mixtures and case-type mixtures, even when the two contributors are immediate relatives (mother and son), which demonstrated the practical usefulness of this method in forensic casework. Our research presents an efficient and different strategy for identification and paternity testing of DNA mixtures in forensic genetics.