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One of the most challenging issues still present in forensic DNA analysis is identifying individuals in samples containing DNA from multiple contributors. The introduction of novel identification markers may be a useful tool in the deconvolution of such DNA mixtures. In this study, we investigated the potential of alleles from the human leukocyte antigen system (HLA) to aid in identifying individuals in complex, multiple-donor DNA samples. The most advantageous characteristic of the HLA complex is its polymorphism in the human genome. A 22-loci multiplex with HLA markers was designed and applied to two-, three-, and four-person DNA mixtures. The results of the conducted experiments demonstrated that the identification of individuals in multiple contributor samples with the help of HLA markers is possible; however, it is clear that the reliability of the method is heavily dependent on the number of unique alleles for each individual in the analysed mixture. In order to compare this novel approach against the already established process, the same group of reference and multiple-contributor samples was analysed with a commonly used set of STR markers. This proof-of-concept research shows the importance of examining alternative solutions to the current deconvolution challenge in forensic DNA profiling.

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BACKGROUND: Small amounts of DNA from a perpetrator collected during crime-scene investigations can be masked by large amounts of DNA from the victim. These samples can provide important information for the perpetrator's conviction. Short tandem repeat (STR) detection system is not sensitive enough to detect trace amounts of minor components in unbalanced mixed DNA. We developed a system using droplet digital polymerase chain reaction (ddPCR) capable of discovering trace components and accurately determining the ratio of mixed DNA in extremely unbalanced mixtures. METHODS: The non-recombining regions of the X chromosome and Y chromosome were quantified in the DNA of male and female mixtures using duplex ddPCR. Absolute quantification of low-abundance portions of trace samples and unbalanced mixtures was done using different mixing ratios. RESULTS: The ddPCR system could be used to detect low-abundance samples with < 5 copies of DNA components in an extremely unbalanced mixture at a mixing ratio of 10000:1. The high sensitivity and specificity of the system could identify the mixing ratio of mixed DNA accurately. CONCLUSIONS: A ddPCR system was developed for evaluation of mixed samples of male DNA and female DNA. Our system could detect DNA quantities as low as 5 copies in extremely unbalanced mixed samples with good specificity and applicability. This method could assist forensic investigators in avoiding the omission of important physical evidence, and evaluating the ratio of mixed male/female trace samples.

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The validity of a probabilistic genotyping (PG) system is typically demonstrated by following international guidelines for the developmental and internal validation of PG software. These guidelines mainly focus on discriminatory power. Very few studies have reported with metrics that depend on calibration of likelihood ratio (LR) systems. In this study, discriminatory power as well as various calibration metrics, such as Empirical Cross-Entropy (ECE) plots, pool adjacent violator (PAV) plots, log likelihood ratio cost (Cllr and Cllrcal), fiducial calibration discrepancy plots, and Turing' expectation were examined using the publicly-available PROVEDIt dataset. The aim was to gain deeper insight into the performance of a variety of PG software in the 'lower' LR ranges (∼LR 1-10,000), with focus on DNAStatistX and EuroForMix which use maximum likelihood estimation (MLE). This may be a driving force for the end users to reconsider current LR thresholds for reporting. In previous studies, overstated 'low' LRs were observed for these PG software. However, applying (arbitrarily) high LR thresholds for reporting wastes relevant evidential value. This study demonstrates, based on calibration performance, that previously reported LR thresholds can be lowered or even discarded. Considering LRs >1, there was no evidence for miscalibration performance above LR ∼1000 when using Fst 0.01. Below this LR value, miscalibration was observed. Calibration performance generally improved with the use of Fst 0.03, but the extent of this was dependent on the dataset: results ranged from miscalibration up to LR ∼100 to no evidence of miscalibration alike PG software using different methods to model peak height, HMC and STRmix. This study demonstrates that practitioners using MLE-based models should be careful when low LR ranges are reported, though applying arbitrarily high LR thresholds is discouraged. This study also highlights various calibration metrics that are useful in understanding the performance of a PG system.

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This study evaluates the performance of analysing surface DNA samples using massively parallel sequencing (MPS) compared to traditional capillary electrophoresis (CE). A total of 30 samples were collected from various surfaces in an office environment and were analysed with CE and MPS. These were compared against 60 reference samples (office inhabitants). To identify contributors, likelihood ratios (LRs) were calculated for MPS and CE data using the probabilistic genotyping software MPSproto and EuroForMix respectively. Although a higher number of sequences/peaks were observed per DNA profile in MPS compared to CE, LR values were found to be lower for MPS data formats. This might be the result of the increased complexity of MPS data, along with a possible elevation of unknown alleles and/or artefacts. The study highlights avenues for improving MPS data quality and analysis to facilitate more robust interpretation of challenging casework-like samples.

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Sexual assault sample processing, despite recent funding and research efforts, remains time-consuming, labourious, and inefficient. These limitations, combined with the prevalence of sexual assaults, have prompted the need to develop a cheaper, quicker, and more robust method for separating victim and perpetrator contributions within sexual assault evidence so that analysts can keep pace with submissions and cases can be resolved in a timely manner. Thus, this study examined the use of a combined enzymatic and alkaline approach for differential cell lysis-with the goal of developing a quick, cheap, and more efficient DNA isolation method. Quantification results for this assay revealed that (72.0 ± 18.3)%, (15.8 ± 14.2)%, and (29.5 ± 23.7)% of total DNA were retained in sperm fractions for neat semen, neat vaginal, and semen-vaginal mixture eluates, respectively. Short tandem repeat (STR) analysis of mixture samples processed with this technique exhibited sperm fraction DNA profiles with mean male-to-female ratios of 1.74:1, which was a 3.01 ± 2.30-fold improvement in male-to-female ratios and led to the recovery of 5.90 ± 7.80 unshared male contributor alleles in sperm fractions that were otherwise undetected in unseparated controls. Overall, this study presented a modified differential lysis approach using prepGEM™ and sodium hydroxide treatments that can accomplish cell elution and fractional lysis within 25 min. Future studies should investigate alternative "non-sperm" cell lysis methods to enhance lysis efficiency and minimize the potential for inhibition, as well as the optimization and automation of this technique. Key points: Traditional sexual assault sample processing methods are time-consuming and inefficient.This modified differential lysis method produces lysates with sufficient DNA yield and quality.A combined technique using enzymatic and alkaline lysis can accomplish fractional separation.Lysis with prepGEM and NaOH absent purification is compatible with downstream processes.

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DNA mixture deconvolution in the forensic DNA community has been addressed in a variety of ways. "Front-end" methods that separate the cellular components of mixtures can provide a significant benefit over computational methods as there is no need to rely on models with inherent uncertainty to generate conclusions. Historically, cell separation methods have been investigated but have been largely ineffective due to high cost, unreliability, and the lack of proper instrumentation. However, the last decade has given rise to more innovative technology that can target and recover cells more effectively. This study focuses on the development and optimization of a method to selectively label and recover male cells in a mixture of male and female epithelial cells using a Y-chromosome labeling kit with DEPArray™ technology, whereby male cells are labeled and recovered into a single extraction-ready tube. Labeling efficiency was tested using freshly collected and aged buccal swabs where 70%-75% and 38% of male cells were labeled, respectively, with less than 1% false positives. DEPArray™ detection was assessed using single buccal epithelial cells where approximately 80% of labeled cells were identified as male. Mixtures (1:1, 1:10, male to female) yielded profiles that were predominantly single source male or those in which the male component was more easily interpreted. The male-specific labeling method was demonstrated to be both robust and reliable when used on freshly collected cells. While the DEPArray™ meditated detection and recovery had notable limitations, it still improved the interpretation of the male component in same-cell mixtures in more recently collected samples.

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Inferring the number of contributors (NoC) is a crucial step in interpreting DNA mixtures, as it directly affects the accuracy of the likelihood ratio calculation and the assessment of evidence strength. However, obtaining the correct NoC in complex DNA mixtures remains challenging due to the high degree of allele sharing and dropout. This study aimed to analyze the impact of allele sharing and dropout on NoC inference in complex DNA mixtures when using microhaplotypes (MH). The effectiveness and value of highly polymorphic MH for NoC inference in complex DNA mixtures were evaluated through comparing the performance of three NoC inference methods, including maximum allele count (MAC) method, maximum likelihood estimation (MLE) method, and random forest classification (RFC) algorithm. In this study, we selected the top 100 most polymorphic MH from the Southern Han Chinese (CHS) population, and simulated over 40 million complex DNA mixture profiles with the NoC ranging from 2 to 8. These profiles involve unrelated individuals (RM type) and related pairs of individuals, including parent-offspring pairs (PO type), full-sibling pairs (FS type), and second-degree kinship pairs (SE type). Our results indicated that how the number of detected alleles in DNA mixture profiles varied with the markers' polymorphism, kinship's involvement, NoC, and dropout settings. Across different types of DNA mixtures, the MAC and MLE methods performed best in the RM type, followed by SE, FS, and PO types, while RFC models showed the best performance in the PO type, followed by RM, SE, and FS types. The recall of all three methods for NoC inference were decreased as the NoC and dropout levels increased. Furthermore, the MLE method performed better at low NoC, whereas RFC models excelled at high NoC and/or high dropout levels, regardless of the availability of a priori information about related pairs of individuals in DNA mixtures. However, the RFC models which considered the aforementioned priori information and were trained specifically on each type of DNA mixture profiles, outperformed RFC_ALL model that did not consider such information. Finally, we provided recommendations for model building when applying machine learning algorithms to NoC inference.

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The de facto genetic markers of forensics are short tandem repeats (STRs). There are many analytical tools designed to work with STRs, including techniques for analyzing and assessing DNA mixtures. In contrast, the nascent field of forensic genetic genealogy often relies on biallelic single nucleotide polymorphisms (SNPs). Tools designed for the forensic assessment of SNPs are somewhat lacking, especially for DNA mixtures. In this paper we introduce Demixtify, a program that detects DNA mixtures using biallelic SNPs. Demixtify is quite powerful; highly imbalanced mixtures can be detected (≤1:99, considering in silico and in vitro mixtures) when coverage is ample. Demixtify can also detect mixtures in low coverage (∼1×) samples (when the mixture is relatively balanced). Demixtify includes an empirical estimator of sequence error that is specific to the markers assayed, making it especially relevant to the forensic community. Orthogonal techniques are also developed to characterize in vitro mixtures, as well as samples thought to be single source, and the results of these approaches serve to validate the techniques presented.

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The need to identify a missing person (MP) through kinship analysis of DNA samples found at a crime scene has become increasingly prevalent. DNA samples from MPs can be severely degraded, contain little DNA and mixed with other contributors, which often makes it difficult to apply conventional methods in practice. This study developed a massively parallel sequencing-based panel that contains 1661 single-nucleotide polymorphisms (SNPs) with low minor allele frequencies (MAFs) (averaged at 0.0613) in the Chinese Han population, and the strategy for relationship inference from DNA mixtures comprising different numbers of contributors (NOCs) and of varying allele dropout probabilities. Based on the simulated dataset and genotyping results of 42 artificial DNA mixtures (NOC = 2-4), it was observed that the present SNP panel was sufficient for balanced mixtures when referenced to the closest relatives (parents/offspring and full siblings). When the mixture profiles suffered from dropout, incorrect assignments were markedly associated with relatedness, NOC and the dropout level. We, therefore, indicate that SNPs with low MAFs could be reliably interpreted for MP identification through the kinship analysis of complex DNA mixtures. Further studies should be extended to more possible scenarios to test the feasibility of this present approach.

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This study investigated the transfer and persistence of salivary DNA under fingernails. This was performed to address a common alternate hypothesis presented to scientists in court, asserting that a relatively large quantity of DNA detected beneath the fingernails, typically from a victim of crime, originates from innocuous transfer of saliva in a casual setting. It was determined through these studies that contact with liquid saliva was an effective way to transfer foreign DNA beneath fingernails. However, when saliva was dried, DNA did not readily transfer through casual contact. When liquid saliva was placed directly beneath fingernails the amount of DNA detected from the saliva donor twenty-four hours later was several hundred-fold lower than the amount detected when sampling occurred immediately following deposition. Furthermore, when the recipients' hands were washed immediately following the deposition of liquid saliva beneath fingernails, the majority of foreign DNA was removed following one hand washing and all detectable foreign DNA was removed from most recipients' hands after three or six hand washings. This study demonstrates that casual contact with wet saliva can result in the transfer of substantial quantities of DNA beneath fingernails but that it does not typically persist for extended periods of time and is mostly removed if the hands are washed soon after deposition.

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DNAmix 2021 is a large-scale study conducted to evaluate the extent of consistency and variation among forensic laboratories in the interpretation of DNA mixtures, and to assess the effects of various potential sources of variability. This study utilized a multi-phasic approach designed to collect information about participating laboratories, laboratory policies, and their standard operating procedures (SOPs). It also characterizes the degree of variation in assessments of suitability and number of contributors as well as in comparisons and statistical analyses of DNA mixture profiles. This paper specifically details the study design and the data collected in the first two phases of the study: the Policies & Procedures (P&P) Questionnaire and the Casework Scenarios Questionnaire (CSQ). We report on the variation in policies and SOPs for 86 forensic laboratories-including information about their DNA workflows, systems, and type of statistics reported. We also provide details regarding various case-scenario specific decisions and the nature of mixture casework for 83 forensic laboratories. The data discussed in this article provide insight into the state of the field for forensic DNA mixture interpretation policies and SOPs at the time of the study (2021-2022).

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Advancements in environmental DNA (eDNA) approaches have allowed for rapid and efficient species detections in diverse environments. Although most eDNA research is focused on leveraging genetic diversity to identify taxa, some recent studies have explored the potential for these approaches to detect within-species genetic variation, allowing for population genetic assessments and abundance estimates from environmental samples. However, we currently lack a framework outlining the key considerations specific to generating, analysing and applying eDNA data for these two purposes. Here, we discuss how various genetic markers differ with regard to genetic information and detectability in environmental samples and how analysis of eDNA samples differs from common tissue-based analyses. We then outline how it may be possible to obtain species absolute abundance estimates from eDNA by detecting intraspecific genetic variation in mixtures of DNA under multiple scenarios. We also identify the major causes contributing to allele detection and frequency errors in eDNA data, discuss their consequences for population-level analyses and outline bioinformatic approaches to detect and remove erroneous sequences. This review summarizes the key advances required to harness the full potential of eDNA-based intraspecific genetic variation to inform population-level questions in ecology, evolutionary biology and conservation management.

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Molecular methods including metabarcoding and quantitative polymerase chain reaction have shown promise for estimating species abundance by quantifying the concentration of genetic material in field samples. However, the relationship between specimen abundance and detectable concentrations of genetic material is often variable in practice. DNA mixture analysis represents an alternative approach to quantify specimen abundance based on the presence of unique alleles in a sample. The DNA mixture approach provides novel opportunities to inform ecology and conservation by estimating the absolute abundance of target taxa through molecular methods; yet, the challenges associated with genotyping many highly variable markers in mixed-DNA samples have prevented its widespread use. To advance molecular approaches for abundance estimation, we explored the utility of microhaplotypes for DNA mixture analysis by applying a 125-marker panel to 1179 Chinook salmon (Oncorhynchus tshawytscha) smolts from the Sacramento-San Joaquin Delta, California, USA. We assessed the accuracy of DNA mixture analysis through a combination of mock mixtures containing DNA from up to 20 smolts and a trophic ecological application enumerating smolts in predator diets. Mock DNA mixtures of up to 10 smolts could reliably be resolved using microhaplotypes, and increasing the panel size would likely facilitate the identification of more individuals. However, while analysis of predator gastrointestinal tract contents indicated DNA mixture analysis could discern the presence of multiple prey items, poor and variable DNA quality prevented accurate genotyping and abundance estimation. Our results indicate that DNA mixture analysis can perform well with high-quality DNA, but methodological improvements in genotyping degraded DNA are necessary before this approach can be used on marginal-quality samples.

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DNA mixture interpretation is one of the most challenging problems in forensics. Complex DNA mixtures are more difficult to analyze when there are more than two contributors or related contributors. Microhaplotypes (MHs) are polymorphic genetic markers recently discovered and employed in DNA mixture analysis. However, the evidentiary interpretation of the MH genotyping data needs more debate. The Random Man Not Excluded (RMNE) method analyzes DNA mixtures without using allelic peak height data or the number of contributors (NoC) assumptions. This study aimed to assess how well RMNE interpreted mixed MH genotyping data. We classified the MH loci from the 1000 Genomes Project database into groups based on their Ae values. Then we performed simulations of DNA mixtures with 2-10 unrelated contributors and DNA mixtures with a pair of sibling contributors. For each simulated DNA mixture, incorrectly included ratios were estimated for three types of non-contributors: random men, parents of contributors, and siblings of contributors. Meanwhile, RMNE probability was calculated for contributors and three types of non-contributors, allowing loci mismatch. The results showed that the MH number, the MH Ae values, and the NoC affected the RMNE probability of the mixture and the incorrectly included ratio of non-contributors. When there were more MHs, MHs with higher Ae values, and a mixture with less NoC, the RMNE probability, and the incorrectly included ratio decreased. The existence of kinship in mixtures complicated the mixture interpretation. Contributors' relatives as non-contributors and related contributors in the mixture increased the demands on the genetic markers to identify the contributors correctly. When 500 highly polymorphic MHs with Ae values higher than 5 were used, the four individual types could be distinguished according to the RMNE probabilities. This study reveals the promising potential of MH as a genetic marker for mixed DNA interpretation and the broadening of RMNE as a parameter indicating the relationship of a specific individual with a DNA mixture in the DNA database search.

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Forensically relevant single nucleotide polymorphisms (SNPs) can provide valuable supplemental information to short tandem repeats (STRs) for investigative leads, and genotyping can now be streamlined using massively parallel sequencing (MPS). Dust is an attractive evidence source, as it accumulates on undisturbed surfaces, often is overlooked by perpetrators, and contains sufficient human DNA for analysis. To assess whether SNPs genotyped from indoor dust using MPS could be used to detect known household occupants, 13 households were recruited and provided buccal samples from each occupant and dust from five predefined indoor locations. Thermo Fisher Scientific Precision ID Identity and Ancestry Panels were utilized for SNP genotyping, and sequencing was completed using Illumina® chemistry. FastID, a software developed to permit mixture analysis and identity searching, was used to assess whether known occupants could be detected from associated household dust samples. A modified "subtraction" method was also used in FastID to estimate the percentage of alleles in each dust sample contributed by known and unknown occupants. On average, 72% of autosomal SNPs were recovered from dust samples. When using FastID, (a) 93% of known occupants were detected in at least one indoor dust sample and could not be excluded as contributors to the mixture, and (b) non-contributor alleles were detected in 54% of dust samples (29 ± 11 alleles per dust sample). Overall, this study highlights the potential of analyzing human DNA present in indoor dust to detect known household occupants, which could be valuable for investigative leads.

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Forensic "touch" DNA samples are low-quantity samples that are recovered from surfaces that have been touched by single or multiple individuals. These samples can include DNA from primary contributors who directly touched the surface, as well as secondary contributors whose DNA was transferred to the surface through an intermediary. It is difficult to determine the type of transfer, or how often and under what conditions DNA transfer occurs. In this paper, we present an innovative protocol that combines (1) a paired male and female transfer DNA experimental design in which the presence of male DNA indicates secondary transfer and (2) a cost-effective quantitative PCR (qPCR) assay of a sex-specific region in the Amelogenin gene to detect male and female DNA. We evaluate the ability of the Amelogenin qPCR assay to detect low concentrations of male and female DNA in mixed samples. We also test experimental DNA samples using our transfer DNA protocol to differentiate primary and secondary DNA transfer. Male DNA was detected in the majority of known mixed samples, even in samples with 4× more female DNA-this result demonstrates the ability to detect low concentrations of male DNA and the presence of secondary transfer DNA in our experimental design. Primary DNA transfer was detected in 100% of our experimental trials and secondary DNA transfer was detected in 37.5% of trials. Our innovative protocol mimics realistic case scenarios to establish rates of primary and secondary DNA transfer in an inexpensive and simplified manner.

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In an attempt to enhance forensic DNA mixture deconvolution several alternative DNA typing approaches have been developed. Among these, DIP-STR compound markers can resolve extremely unbalanced two-source DNA mixtures of same-or-opposite sex donors, up to a 1:1000 minor:major DNA ratio. A forensic set of 10 markers was validated for casework and a larger set of 23 DIP-STRs has proven suitable to biogeographic ancestry inference and for prenatal paternity testing. Yet, to promote the widespread use of this original approach, more markers and multiplex panels need to be developed. To this end, here we describe an extended set of forensic DIP-STRs identified using currently available whole-genome sequencing datasets. Complete lists of Indels and STRs were obtained from reported frequencies of genetic variants of 76,156 genomes. About 3000 identified DIP-STRs candidates were shorter than 200 bp and 500 showed high haplotype variability estimated using the genotypes of individuals homozygous for the DIP or the STR. Here, we present 23 additional DIP-STRs validated for sensitivity, specificity and Swiss population variability. Finally, a set of 30 markers comprising seven previously validated ones is proposed for the prospective development of a forensic DIP-STR multiplex panel.

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We provide an internal validation study of a recently published precise DNA mixture algorithm based on Hamiltonian Monte Carlo sampling (Susik et al., 2022). We provide results for all 428 mixtures analysed by Riman et al. (2021) and compare the results with two state-of-the-art software products: STRmix™  v2.6 and Euroformix v3.4.0. The comparison shows that the Hamiltonian Monte Carlo method provides reliable values of likelihood ratios (LRs) close to the other methods. We further propose a novel large-scale precision benchmark and quantify the precision of the Hamiltonian Monte Carlo method, indicating its improvements over existing solutions. Finally, we analyse the influence of the factors discussed by Buckleton et al. (2022).

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When evaluating support for the contribution of a person of interest (POI) to a mixed DNA sample, it is generally assumed that the mixture contributors are unrelated to the POI and to each other. In practice, there may be situations where this assumption is violated, for instance if two mixture contributors are siblings. The effect on the likelihood ratio of (in)correctly assuming relatedness between mixture contributors has previously been investigated using simulation studies based on simplified models ignoring peak heights. We revisit this problem using a simulation study that applies peak height models both in the simulation and mixture interpretation part of the study. Specifically, we sample sets of mixtures comprising both related and unrelated contributors and evaluate support for the contribution of the mixture donors as well as unrelated persons with and without incorporating an assumption of relatedness. The results show, consistent with earlier studies, that including a correct assumption of relatedness increases the capacity of the probabilistic genotyping system to distinguish between mixture donors and unrelated persons. Any effect of the relatedness is found to depend strongly on the mixture ratio. We further show that the results do not change materially when a sub-population correction is applied. Finally, we suggest and discuss a likelihood ratio approach that considers relatedness between mixture contributors using a prior probability.

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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.

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