Ratios and distances of pull-up peaks observed in GlobalFiler kit data

Combining artificial neural network classification with fully continuous probabilistic genotyping to remove the need for an analytical threshold and electropherogram reading

Standard processing of electrophoretic data within a forensic DNA laboratory is for one (or two) analysts to designate peaks as either artefactual or non-artefactual in a process commonly referred to as profile 'reading'. Recently, FaSTR™ DNA has been developed to use artificial neural networks to automatically classify fluorescence within an electropherogram as baseline, allele, stutter or pull-up. These classifications are based on probabilities assigned to each timepoint (scan) within the electropherogram. Instead of using the probabilities to assign fluorescence into a category they can be used directly in the profile analysis. This has a number of advantages; increased objectivity in DNA profile processing, the removal for the need for analysts to read profiles, the removal for the need of an analytical threshold. Models within STRmix™ were extended to incorporate the peak label probabilities assigned by FaSTR™ DNA. The performance of the model extensions was tested on a DNA mixture dataset, comprising 2-4 person samples. This dataset was processed in a 'standard' manner using an analytical threshold of 50rfu, analyst peak designations and STRmix™ V2.9 models. The same dataset was then processed in an automated manner using no analytical threshold, no analysts reading the profile and using the STRmix™ models extended to incorporate peak label probabilities. Both datasets were compared to the known DNA donors and a set of non-donors. The result between the two processes was a very close performance, but with a large efficiency gain in the 0rfu process. Utilising peak label probabilities opens up the possibility for a range of workflow process efficiency gains, but beyond this allows full use of all data within an electropherogram.

Evaluation of probability distribution models for stutter ratios in the typing system of GlobalFiler and 3500xL Genetic Analyzer

As DNA typing systems have become increasingly sensitive in recent years, probability distribution models for back, forward, double-back, and minus 2-nt stutter ratios have been desired to be considered in DNA evidence interpretation using specific software programs. However, experimental investigations have been insufficient, especially for forward, double-back, and minus 2-nt stutters. In this study, we experimentally reevaluated the probability distribution models for each stutter ratio in the typing systems of GlobalFiler™ PCR Amplification Kit and 3500xL Genetic Analyzer from Thermo Fisher Scientific. In addition, to enhance the reliability of longest uninterrupted stretch (LUS) values and corrected allele numbers used in previously developed models for stutter ratios using sequence information (i.e., LUS model and multi-seq model), we propose the weighted average of LUS values and corrected allele numbers based on the number of observations in sequence-based population data. Back stutter ratios demonstrated a positive correlation with allele numbers (allele model) in eight loci, LUS values (LUS model) in eight loci, and corrected allele numbers (multi-seq model) in five loci. The forward stutter ratios (FSRs) of D22S1045 followed the LUS model. FSRs other than D22S1045 and double-back stutter ratios followed the LUS model by considering multiple loci together. Minus 2-nt stutter ratios observed in SE33 and D1S1656 did not increase with the increase in the allele numbers. The adopted models for each stutter ratio can be implemented in software programs for DNA evidence interpretation and enable a reliable interpretation of crime stain profiles in forensic caseworks.