A series of developmental validation tests for Number of Contributors platforms: Exemplars using NOCIt and a neural network.

Complex DNA mixtures are challenging to interpret and require computational tools that aid in that interpretation. Recently, several computational methods that estimate the number of contributors (NOC) to a sample have been developed. Unlike analogous tools that interpret profiles and report LRs, NOC tools vary widely in their operational principle where some are Bayesian and others are machine learning tools. Conjunctionally, NOC tools may return a single n estimate, or a distribution on n. This vast array of constructs, coupled with a gap in standardized methods by which to validate NOC systems, warrants an exploration into the measures by which differing NOC systems might be tested for operations. In the current paper, we use two exemplar NOC systems: a probabilistic system named NOCIt, which renders an a posteriori probability (APP) distribution on the number of contributors given an electropherogram and an artificial neural network (ANN). NOCIt is a continuous Bayesian inference system incorporating models of peak height, degradation, differential degradation, forward and reverse stutter, noise and allelic drop-out while considering allele frequencies in a reference population. The ANN is also a continuous method, taking all the same features (barring degradation) into account. Unlike its Bayesian counterpart, it demands substantively more data to parameterize, requiring synthetic data. We explore each system's performance by conducting tests on 214 PROVEDIt mixtures where the limit of detection was 1-copy of DNA. We found that after a lengthy training period of approximately 24 h, the ANN's evaluation process was very fast and perfectly repeatable. In contrast, NOCIt only took a few minutes to train but took tens of minutes to complete each sample and was less repeatable. In addition, it rendered a probability distribution that was more sensitive and specific, affording a reasonable method by which to report all reasonable n that explain the evidence for a given sample. Whatever the method, by acknowledging the inherent differences between NOC systems, we demonstrate that validation constructs will necessarily be guided by the needs of the forensic domain and be dependent upon whether the laboratory seeks to assign a single n or range of n.

Probabilistic characterisation of baseline noise in STR profiles.

There are three dominant contributing factors that distort short tandem repeat profile measurements, two of which, stutter and variations in the allelic peak heights, have been described extensively. Here we characterise the remaining component, baseline noise. A probabilistic characterisation of the non-allelic noise peaks is not only inherently useful for statistical inference but is also significant for establishing a detection threshold. We do this by analysing the data from 643 single person profiles for the Identifiler Plus kit and 303 for the PowerPlex 16 HS kit. This investigation reveals that although the dye colour is a significant factor, it is not sufficient to have a per-dye colour description of the noise. Furthermore, we show that at a per-locus basis, out of the Gaussian, log-normal, and gamma distribution classes, baseline noise is best described by log-normal distributions and provide a methodology for setting an analytical threshold based on that deduction. In the PowerPlex 16 HS kit, we observe evidence of significant stutter at two repeat units shorter than the allelic peak, which has implications for the definition of baseline noise and signal interpretation. In general, the DNA input mass has an influence on the noise distribution. Thus, it is advisable to study noise and, consequently, to infer quantities like the analytical threshold from data with a DNA input mass comparable to the DNA input mass of the samples to be analysed.