Exploring STR signal in the single- and multicopy number regimes: Deductions from an in silico model of the entire DNA laboratory process.

Short tandem repeat (STR) profiling from DNA samples has long been the bedrock of human identification. The laboratory process is composed of multiple procedures that include quantification, sample dilution, PCR, electrophoresis, and fragment analysis. The end product is a short tandem repeat electropherogram comprised of signal from allele, artifacts, and instrument noise. In order to optimize or alter laboratory protocols, a large number of validation samples must be created at significant expense. As a tool to support that process and to enable the exploration of complex scenarios without costly sample creation, a mechanistic stochastic model that incorporates each of the aforementioned processing features is described herein. The model allows rapid in silico simulation of electropherograms from multicontributor samples and enables detailed investigations of involved scenarios. An implementation of the model that is parameterized by extensive laboratory data is publically available. To illustrate its utility, the model was employed in order to evaluate the effects of sample dilutions, injection time, and cycle number on peak height, and the nature of stutter ratios at low template. We verify the model's findings by comparison with experimentally generated data.

Production of high-fidelity electropherograms results in improved and consistent DNA interpretation: Standardizing the forensic validation process.

Samples containing low-copy numbers of DNA are routinely encountered in casework. The signal acquired from these sample types can be difficult to interpret as they do not always contain all of the genotypic information from each contributor, where the loss of genetic information is associated with sampling and detection effects. The present work focuses on developing a validation scheme to aid in mitigating the effects of the latter. We establish a scheme designed to simultaneously improve signal resolution and detection rates without costly large-scale experimental validation studies by applying a combined simulation and experimental based approach. Specifically, we parameterize an in silico DNA pipeline with experimental data acquired from the laboratory and use this to evaluate multifarious scenarios in a cost-effective manner. Metrics such as signal1copy-to-noise resolution, false positive and false negative signal detection rates are used to select tenable laboratory parameters that result in high-fidelity signal in the single-copy regime. We demonstrate that the metrics acquired from simulation are consistent with experimental data obtained from two capillary electrophoresis platforms and various injection parameters. Once good resolution is obtained, analytical thresholds can be determined using detection error tradeoff analysis, if necessary. Decreasing the limit of detection of the forensic process to one copy of DNA is a powerful mechanism by which to increase the information content on minor components of a mixture, which is particularly important for probabilistic system inference. If the forensic pipeline is engineered such that high-fidelity electropherogram signal is obtained, then the likelihood ratio (LR) of a true contributor increases and the probability that the LR of a randomly chosen person is greater than one decreases. This is, potentially, the first step towards standardization of the analytical pipeline across operational laboratories.