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.
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.
Keywords
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Article info
Publication history
Published online: October 09, 2022
Accepted:
October 5,
2022
Received in revised form:
September 22,
2022
Received:
July 10,
2022
Identification
Copyright
© 2022 Elsevier B.V. All rights reserved.
