Combining artificial neural network classification with fully continuous probabilistic genotyping to remove the need for an analytical threshold and electropherogram reading. (January 2023)

• Record Type: Journal Article
• Authors: Taylor, Duncan; Buckleton, John
• Is Part Of: Forensic science international. Volume 62(2023)
• Publication Date: 2023-01
• Subjects: Artificial neural network -- Continuous DNA interpretation -- STRmix -- Modelling artefacts -- DNA mixtures; Forensic genetics -- Periodicals; Génétique légale -- Périodiques; Forensic genetics; Electronic journals; Periodicals; 614.1
• DOI: 10.1016/j.fsigen.2022.102787
• Languages: English
• ISSNs: 1872-4973

Abstract

Abstract: 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. Highlights: EPGs peaks are assigned probabilistically as being artefactual or not using neural networks. Models in STRmix are extended to incorporate these peak label probabilities. FaSTR processed mixtures without an analytical threshold, AT, or human intervention. These mixtures, with peak label probabilities, were analysed in STRmix. Performance exceeded a 'standard' analysis using an AT and human reading.