Forensic DNA signal is notoriously challenging to interpret and requires the implementation of computational tools that support its interpretation. While data from high-copy, low-contributor samples result in an electropherogram signal that is readily interpreted by probabilistic methods, electropherogram signals from forensic stains are often garnered from low-copy, high-contributor-number samples. These samples can be obfuscated by allele sharing, allele drop-out, stutter, and noise peaks. Since forensic DNA profiles are too complicated to quantitatively assess by manual methods, continuous probabilistic frameworks that draw inferences on the Number of Contributors (NOC) and compute the Likelihood Ratio (LR) given the prosecution and defense’s hypotheses have been developed.
In this webinar, the presenter will show validation results acquired from the newest version of the NOCIt inference platform, which determines an A Posteriori Probability (APP) distribution of the number of contributors given an electropherogram. NOCIt is a continuous inference system that incorporates models of peak height (including degradation and differential degradation), forward and reverse stutter, noise, and allelic drop-out while considering allele frequencies in a reference population.
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