Evaluation of ArmedXpert software tools, MixtureAce and Mixture Interpretation, to analyze MPS-STR data

Abstract

Massively parallel sequencing (MPS) technologies have revolutionized studies of genomic variations and transformed DNA analysis in multiple fields. Assays based on MPS must be capable of discriminating variations introduced by the method, i.e. artifacts from true polymorphisms. In PCR-MPS methods targeting microsatellite markers, artifacts can arise from PCR mis-incorporation, PCR strand slippage (stutter), and sequencing error. Reliable detection of artifacts in mixed DNA samples is a significant challenge that must be addressed in forensic DNA analysis. The ArmedXpert (NicheVision) software tools, MixtureAce™ and Mixture Interpretation, can analyze MPS data by categorizing sequence reads in alleles, stutter, and non-stutter artifacts and analyzing autosomal STR loci of mixed samples. In this study, we evaluated the ArmedXpert tools for the analysis of STR profiles of single-sourced and mixed samples generated by the ForenSeq™ DNA Signature Prep kit (Verogen). Data from eight experimental runs (240 samples) were analyzed: one benchmark run, two runs testing sensitivity with down to 50 pg DNA input, one run testing artificially degraded samples and DNA derived from bones, blood cards and teeth, as well as four runs with mixed DNA samples of varying ratios, sex, and different number of contributors (two to six). The MixtureAce stutter thresholds were initially set following the recommendations from Verogen, plus a non-stutter artifact threshold was set at 5% of allele read counts. A benchmark run, of 30 samples, plus two controls, containing 2310 total alleles, revealed over 5000 artifacts, above an analytical threshold of 10. A total of 4869 artifacts were correctly classified, while 435 were mis-classified as alleles due to exceedance of initial threshold settings. False positives must be resolved by an analyst, which can be time consuming. Stutter thresholds were adjusted based on the benchmark data and the samples were re-tested, resulting in only 57 false positive allele calls. The revised settings were then used in the analysis of the remaining seven experimental runs. Results show that MixtureAce can accurately classify artifacts and alleles when laboratory-specific threshold settings are used. The Mixture Interpretation tool was applied on two- and three-person mixtures. This tool utilized the analyzed data from MixtureAce to calculate, based on the number of alleles at a locus and their read counts, possible deconvolution outcomes with their respective ratios.