Development and validation of a fast and automated DNA identification line.

The importance of DNA evidence for gaining investigative leads demands a fast workflow for forensic DNA profiling performed in large volumes. Therefore, we developed software solutions for automated DNA profile analysis, contamination check, major donor inference, DNA database (DDB) comparison and reporting of the conclusions. This represents the Fast DNA IDentification Line (FIDL) and this study describes its development, validation and implementation in criminal casework at the authors' institute. This first implementation regards single donor profiles and major contributors to mixtures. The validation included testing of the software components on their own and examination of the performance of different DDB search strategies. Furthermore, end-to-end testing was performed under three conditions: (1) testing of scenarios that can occur in DNA casework practice, (2) tests using three months of previous casework data, and (3) testing in a casework production environment in parallel to standard casework practices. The same DNA database candidates were retrieved by this automated line as by the manual workflow. The data flow was correct, results were reproducible and robust, results requiring manual analysis were correctly flagged, and reported results were as expected. Overall, we found FIDL valid for use in casework practice in our institute. The results from FIDL are automatically reported within three working days from receiving the trace sample. This includes the time needed for registration of the case, DNA extraction, quantification, polymerase chain reaction and capillary electrophoresis. FIDL itself takes less than two hours from intake of the raw CE data to reporting. Reported conclusions are one of five options: (1) candidate retrieved from DDB, (2) no candidate retrieved from DDB, (3) high evidential value with regards to reference within the case, (4) results require examination of expert, or (5) insufficient amount of DNA obtained to generate a DNA profile. In our current process, the automated report is sent within three working days and a complete report, with confirmation of the FIDL results, and signed by a reporting officer is sent at a later time. The signed report may include additional analyses regarding e.g. minor contributors. The automated report with first case results is quickly available to the police enabling them to act upon the DNA results prior to receiving the full DNA report. This line enables a uniform and efficient manner of handling large numbers of traces and cases and provides high value investigative leads in the early stages of the investigation.

Explainable artificial intelligence in forensics: Realistic explanations for number of contributor predictions of DNA profiles.

Machine learning obtains good accuracy in determining the number of contributors (NOC) in short tandem repeat (STR) mixture DNA profiles. However, the models used so far are not understandable to users as they only output a prediction without any reasoning for that conclusion. Therefore, we leverage techniques from the field of explainable artificial intelligence (XAI) to help users understand why specific predictions are made. Where previous attempts at explainability for NOC estimation have relied upon using simpler, more understandable models that achieve lower accuracy, we use techniques that can be applied to any machine learning model. Our explanations incorporate SHAP values and counterfactual examples for each prediction into a single visualization. Existing methods for generating counterfactuals focus on uncorrelated features. This makes them inappropriate for the highly correlated features derived from STR data for NOC estimation, as these techniques simulate combinations of features that could not have resulted from an STR profile. For this reason, we have constructed a new counterfactual method, Realistic Counterfactuals (ReCo), which generates realistic counterfactual explanations for correlated data. We show that ReCo outperforms state-of-the-art methods on traditional metrics, as well as on a novel realism score. A user evaluation of the visualization shows positive opinions of end-users, which is ultimately the most appropriate metric in assessing explanations for real-world settings.

Application of a probabilistic genotyping software to MPS mixture STR data is supported by similar trends in LRs compared with CE data.

The interpretation of short tandem repeat (STR) profiles can be challenging when, for example, alleles are masked due to allele sharing among contributors and/or when they are subject to drop-out, for instance from sample degradation. Mixture interpretation can be improved by increasing the number of STRs and/or loci with a higher discriminatory power. Both capillary electrophoresis (CE, 6-dye) and massively parallel sequencing (MPS) provide a platform for analysing relatively large numbers of autosomal STRs. In addition, MPS enables distinguishing between sequence variants, resulting in enlarged discriminatory power. Also, MPS allows for small amplicon sizes for all loci as spacing is not an issue, which is beneficial with degraded DNA. Altogether, MPS has the potential to increase the weights of evidence for true contributors to (complex) DNA profiles. In this study, likelihood ratio (LR) calculations were performed using STR profiles obtained with two different MPS systems and analysed using different settings: 1) MPS PowerSeq™ Auto System profiles analysed using FDSTools equipped with optimized settings such as noise correction, 2) ForenSeq™ DNA Signature Prep Kit profiles analysed using the default settings in the Universal Analysis Software (UAS), and 3) ForenSeq™ DNA Signature Prep Kit profiles analysed using FDSTools empirically adapted to cope with one-directional reads and provisional, basic settings. The LR calculations used genotyping data for two- to four-person mixtures varying for mixture proportion, level of drop-out and allele sharing and were generated with the continuous model EuroForMix. The LR results for the over 2000 sets of propositions were affected by the variation for the number of markers and analysis settings used in the three approaches. Nevertheless, trends for true and non-contributors, effects of replicates, assigned number of contributors, and model validation results were comparable for the three MPS approaches and alike the trends known for CE data. Based on this analogy, we regard the probabilistic interpretation of MPS STR data fit for forensic DNA casework. In addition, guidelines were derived on when to apply LR calculations to MPS autosomal STR data and report the corresponding results.

Multi-laboratory validation of DNAxs including the statistical library DNAStatistX.

This study describes a multi-laboratory validation of DNAxs, a DNA eXpert System for the data management and probabilistic interpretation of DNA profiles [1], and its statistical library DNAStatistX to which, besides the organising laboratory, four laboratories participated. The software was modified to read multiple data formats and the study was performed prior to the release of the software to the forensic community. The first exercise explored all main functionalities of DNAxs with feedback on user-friendliness, installation and general performance. Next, every laboratory performed likelihood ratio (LR) calculations using their own dataset and a dataset provided by the organising laboratory. The organising laboratory performed LR calculations using all datasets. The datasets were generated with different STR typing kits or analysis systems and consisted of samples varying in DNA amounts, mixture ratios, number of contributors and drop-out level. Hypothesis sets had the correct, under- and over-assigned number of contributors and true and false donors as person of interest. When comparing the results between laboratories, the LRs were foremost within one unit on log10 scale. The few LR results that deviated more had differences for the parameters estimated by the optimizer within DNAStatistX. Some of these were indicated by failed iteration results, others by a failed model validation, since unrealistic hypotheses were included. When these results that do not meet the quality criteria were excluded, as is in accordance with interpretation guidelines, none of the analyses in the different laboratories yielded a different statement in the casework report. Nonetheless, changes in software parameters were sought that minimized differences in outcomes, which made the DNAStatistX module more robust. Overall, the software was found intuitive, user-friendly and valid for use in multiple laboratories.

Automated estimation of the number of contributors in autosomal short tandem repeat profiles using a machine learning approach.

The number of contributors (NOC) to (complex) autosomal STR profiles cannot be determined with absolute certainty due to complicating factors such as allele sharing and allelic drop-out. The precision of NOC estimations can be improved by increasing the number of (highly polymorphic) markers, the use of massively parallel sequencing instead of capillary electrophoresis, and/or using more profile information than only the allele counts. In this study, we focussed on machine learning approaches in order to make maximum use of the profile information. To this end, a set of 590 PowerPlex® Fusion 6C profiles with one up to five contributors were generated from a total of 1174 different donors. This set varied for the template amount of DNA, mixture proportion, levels of allele sharing, allelic drop-out and degradation. The dataset contained labels with known NOC and was split into a training, test and hold-out set. The training set was used to optimize ten different algorithms with selection of profile characteristics. Per profile, over 250 characteristics, denoted 'features', were calculated. These features were based on allele counts, peak heights and allele frequencies. The features that were most related to the NOC were selected based on partial correlation using the training set. Next, the performance of each model (=combination of features plus algorithm) was examined using the test set. A random forest classifier with 19 features, denoted the 'RFC19-model' showed best performance and was selected for further validation. Results showed improved accuracy compared to the conventional maximum allele count approach and an in-house nC-tool based on the total allele count. The method is extremely fast and regarded useful for application in forensic casework.

An assessment of the performance of the probabilistic genotyping software EuroForMix: Trends in likelihood ratios and analysis of Type I & II errors.

Continuous probabilistic genotyping software enables the interpretation of highly complex DNA profiles that are prone to stochastic effects and/or consist of multiple contributions. The process of introducing probabilistic genotyping into an accredited forensic laboratory requires testing, validation, documentation and training. Documents that include guidelines and/or requirements have been published in order to guide forensic laboratories through this extensive process and there has been encouragements to share the results obtained from internal laboratory studies. To this end, we present the results obtained from the quantitative probabilistic genotyping system EuroForMix applied to mixed DNA profiles with known contributions mixed in known proportions, levels of allele sharing and levels of allelic drop-out. The mixtures were profiled using the PowerPlex® Fusion 6C (PPF6C) kit. Using these mixtures, 427 Hp-true tests and 408 Hd-true tests were performed. In the Hd-true tests, non-contributors were selected deliberately to a have large overlap with the alleles within the mixture and worst-case scenarios were examined in which a simulated relative of one of the true donors was considered as the person of interest under the prosecution hypothesis. The effects of selecting different EuroForMix modelling options, the use of PCR replicates, allelic drop-out, and varying the assigned number of contributors were examined. Instances of Type I and Type II errors are discussed. In addition 330 likelihood ratio results from EuroForMix are compared to the semi-continuous model LRmix Studio. Results demonstrate the performance and trends of EuroForMix when applied to PPF6C profiles.

DNAxs/DNAStatistX: Development and validation of a software suite for the data management and probabilistic interpretation of DNA profiles.

The data management, interpretation and comparison of sets of DNA profiles can be complex, time-consuming and error-prone when performed manually. This, combined with the growing numbers of genetic markers in forensic identification systems calls for expert systems that can automatically compare genotyping results within (large) sets of DNA profiles and assist in profile interpretation. To that aim, we developed a user-friendly software program or DNA eXpert System that is denoted DNAxs. This software includes features to view, infer and match autosomal short tandem repeat profiles with connectivity to up and downstream software programs. Furthermore, DNAxs has imbedded the 'DNAStatistX' module, a statistical library that contains a probabilistic algorithm to calculate likelihood ratios (LRs). This algorithm is largely based on the source code of the quantitative probabilistic genotyping system EuroForMix [1]. The statistical library, DNAStatistX, supports parallel computing which can be delegated to a computer cluster and enables automated queuing of requested LR calculations. DNAStatistX is written in Java and is accessible separately or via DNAxs. Using true and non-contributors to DNA profiles with up to four contributors, the DNAStatistX accuracy and precision were assessed by comparing the DNAStatistX results to those of EuroForMix. Results were the same up to rare differences that could be attributed to the different optimizers used in both software programs. Implementation of dye specific detection thresholds resulted in larger likelihood values and thus a better explanation of the data used in this study. Furthermore, processing time, robustness of DNAStatistX results and the circumstances under which model validations failed were examined. Finally, guidelines for application of the software are shared as an example. The DNAxs software is future-proof as it applies a modular approach by which novel functionalities can be incorporated.

Low-template methods yield limited extra information for PowerPlex® Fusion 6C profiling.

Advances in autosomal DNA profiling systems enable analyzing increased numbers of short tandem repeat (STR) loci in one reaction. Increasing the number of STR loci increases the amount of information that may be obtained from a (crime scene) sample. In this study, we examined whether even more allelic information can be obtained by applying low-template methods. To this aim, the performance of the PowerPlex® Fusion 6C STR typing system was assessed when increasing the number of PCR cycles or enhancing the capillary electrophoresis (CE) injection settings. Results show that applying these low-template methods yields limited extra information and comes at cost of more background noise. In addition, the gain in detection of alleles was much smaller when compared to the gain when applying low-template methods to the 15-loci AmpFLSTR® NGM™ system. Consequently, the PowerPlex® Fusion 6C STR typing system was implemented using standard settings only; low-template methods were not implemented for our routine forensic casework.

Validation of SmartRank: A likelihood ratio software for searching national DNA databases with complex DNA profiles.

Searching a national DNA database with complex and incomplete profiles usually yields very large numbers of possible matches that can present many candidate suspects to be further investigated by the forensic scientist and/or police. Current practice in most forensic laboratories consists of ordering these 'hits' based on the number of matching alleles with the searched profile. Thus, candidate profiles that share the same number of matching alleles are not differentiated and due to the lack of other ranking criteria for the candidate list it may be difficult to discern a true match from the false positives or notice that all candidates are in fact false positives. SmartRank was developed to put forward only relevant candidates and rank them accordingly. The SmartRank software computes a likelihood ratio (LR) for the searched profile and each profile in the DNA database and ranks database entries above a defined LR threshold according to the calculated LR. In this study, we examined for mixed DNA profiles of variable complexity whether the true donors are retrieved, what the number of false positives above an LR threshold is and the ranking position of the true donors. Using 343 mixed DNA profiles over 750 SmartRank searches were performed. In addition, the performance of SmartRank and CODIS were compared regarding DNA database searches and SmartRank was found complementary to CODIS. We also describe the applicable domain of SmartRank and provide guidelines. The SmartRank software is open-source and freely available. Using the best practice guidelines, SmartRank enables obtaining investigative leads in criminal cases lacking a suspect.

Results of an inter and intra laboratory exercise on the assessment of complex autosomal DNA profiles.

The interpretation of complex DNA profiles may differ between laboratories and reporting officers, which can lead to discrepancies in the final reports. In this study, we assessed the intra and inter laboratory variation in DNA mixture interpretation for three European ISO17025-accredited laboratories. To this aim, 26 reporting officers analyzed five sets of DNA profiles. Three main aspects were considered: 1) whether the mixed DNA profiles met the criteria for comparison to a reference profile, 2) the actual result of the comparison between references and DNA profiling data and 3) whether the weight of the DNA evidence could be assessed. Similarity in answers depended mostly on the complexity of the tasks. This study showed less variation within laboratories than between laboratories which could be the result of differences between internal laboratory guidelines and methods and tools available. Results show the profile types for which the three laboratories report differently, which informs indirectly on the complexity threshold the laboratories employ. Largest differences between laboratories were caused by the methods available to assess the weight of the DNA evidence. This exercise aids in training forensic scientists, refining laboratory guidelines and explaining differences between laboratories in court. Undertaking more collaborative exercises in future may stimulate dialog and consensus regarding interpretation. For training purposes, DNA profiles of the mixed stains and questioned references are made available.

A comparative study of qualitative and quantitative models used to interpret complex STR DNA profiles.

The investigation of the performance of models to interpret complex DNA profiles is best undertaken using real DNA profiles. Here we used a data set to reflect the variety typically encountered in real casework. The "crime-stains" were constructed from known individuals and comprised a total of 59 diverse samples: pristine DNA/DNA extracted from blood, 2-3 person mixtures, degradation/no-degradation, differences in allele sharing, dropout/no dropout, etc. Two siblings were also included in the test-set in order to challenge the systems. Two kinds of analyses were performed, namely tests on whether a person of interest is a contributor based on weight-of-evidence (likelihood ratio) calculations, and deconvolution test to estimate the profile of unknown constituent parts. The weight-of-evidence analyses compared LRmix Studio with EuroForMix including exploration of the effect of applying an ad hoc stutter-filter. For the deconvolution analysis we compared EuroForMix with LoCIM-tool. When we classified persons of interests into being true contributors or non-contributors, we found that EuroForMix, overall, returned a higher true positive rate for the same false positive levels compared to LRmix. In particular, in cases with an unknown major component, EuroForMix was more discriminating for mixtures where the person of interest was a minor contributor. Comparing deconvolution of major contributors we found that EuroForMix overall performed better than LoCIM-tool.

Evaluation of samples comprising minute amounts of DNA.

Minute amounts of DNA representing only few diploid cells, may be interrogated using enhanced DNA profiling, which will be accompanied by stochastic amplification effects. Notwithstanding, a weight of evidence statistic may be calculated using current interpretation software. In this study, we profiled single donor, two- and three-person samples having only 3 pg to 12 pg of DNA per contributor using both standard and enhanced capillary electrophoresis (CE) injection settings. Likelihood ratios (LRs) were computed using LRmix Studio, compared for both types of profiles and examined in relation to the amount of DNA, drop-out level, number of detected alleles, peak heights and reproducibility of alleles. Especially for DNA profiles that were generated using enhanced CE, the obtained LRs could indicate strong evidence in favour of the prosecution (log10(LR)>6), also when the amount of DNA represented about half of a diploid cell equivalent in the amplification. These results illustrate that an assessment of the criminalistic relevance of a sample carrying minute amounts of DNA is essential prior to applying enhanced interrogation techniques and/or calculating a weight of evidence statistic.

The effect of varying the number of contributors on likelihood ratios for complex DNA mixtures.

Interpretation of DNA mixtures with three or more contributors, defined here as high order mixtures, is difficult because of the inevitability of allele sharing. Allele sharing complicates the estimation of the number of contributors, which is an important parameter to assess the probative value. Consequently, these mixtures may not be deemed suitable for interpretation and reporting. In this study, we generated three-, four- and five-person mixtures with little or no drop-out and with varying levels of allele sharing. For these DNA mixtures we computed likelihood ratios (LRs) using the LRmix model, and always using persons of interest that are true contributors. We assessed the influence of different scenarios on the LR, and used (1) the true or an incorrect number of contributors, (2) zero, one or two anchored individuals and (3) an equal number of contributors under Hp and Hd or an extra contributor under Hd. It was shown that the LR varied considerably when the hypotheses used an incorrect number of contributors, especially when individuals were anchored under the hypotheses. Overall, when analysing high order mixtures, there may occur a transition from LR greater than one to less than one if an incorrect number of contributors is conditioned. This is a result of allele sharing among the multiple contributors rather than allele drop-out, since this study only utilised samples with little or no drop-out.

Complex DNA mixture analysis in a forensic context: evaluating the probative value using a likelihood ratio model.

The interpretation of mixed DNA profiles obtained from low template DNA samples has proven to be a particularly difficult task in forensic casework. Newly developed likelihood ratio (LR) models that account for PCR-related stochastic effects, such as allelic drop-out, drop-in and stutters, have enabled the analysis of complex cases that would otherwise have been reported as inconclusive. In such samples, there are uncertainties about the number of contributors, and the correct sets of propositions to consider. Using experimental samples, where the genotypes of the donors are known, we evaluated the feasibility and the relevance of the interpretation of high order mixtures, of three, four and five donors. The relative risks of analyzing high order mixtures of three, four, and five donors, were established by comparison of a 'gold standard' LR, to the LR that would be obtained in casework. The 'gold standard' LR is the ideal LR: since the genotypes and number of contributors are known, it follows that the parameters needed to compute the LR can be determined per contributor. The 'casework LR' was calculated as used in standard practice, where unknown donors are assumed; the parameters were estimated from the available data. Both LRs were calculated using the basic standard model, also termed the drop-out/drop-in model, implemented in the LRmix module of the R package Forensim. We show how our results furthered the understanding of the relevance of analyzing high order mixtures in a forensic context. Limitations are highlighted, and it is illustrated how our study serves as a guide to implement likelihood ratio interpretation of complex DNA profiles in forensic casework.

LoCIM-tool: An expert's assistant for inferring the major contributor's alleles in mixed consensus DNA profiles.

When dealing with mixed DNA profiles where contributors have donated DNA in unequal amounts, it is often useful to deduce the genotype of the major contributor. Inference of a major contributor's genotype empowers storage of the DNA profile in a DNA database (DDB), which is especially of interest in cases without a suspect. When a major contributor's genotype cannot be inferred straightforwardly, for instance because low level components are present, replicate analyses can be prepared and combined into a consensus profile. Here we describe an automated and freely available tool to deduce the major component's alleles in mixed consensus DNA profiles. In these consensus profiles, theoretical peak heights (PHs) are assigned to the alleles using the sum of the PHs in the individual amplifications. The LoCIM-tool (Locus Classification & Inference of the Major-tool) uses these PHs plus parameters on the stochastic threshold, heterozygote balance (HB) and major to minor(s) ratio to classify every locus as a type 1, type 2 or type 3 locus, which represent classes of increasing complexity. Based on the type of locus, the LoCIM-tool applies an inclusion percentage to deduce the alleles for the major contributor. Using the LoCIM-tool, 99.9% of all type 1 loci and 96.7% of all type 2 loci were inferred correctly from a large set of consensus DNA profiles that were generated from mixtures varying for the mixture ratio, amount of DNA per contributor, number of contributors, quality of DNA, and allele sharing among the contributors. For type 3 loci, we aimed at inferring the major contributor's alleles and possibly extra alleles, which occurred for 87.2% of all type 3 loci analysed using the LoCIM-tool. When compared to the overall results of manual inference by a group of forensic scientists, the LoCIM-tool obtains a higher percentage of correctly inferred loci. From our results, we conclude that the LoCIM-tool presents an objective, uniform and fast method to reliably deduce alleles of a major component.

Assessment of mock cases involving complex low template DNA mixtures: A descriptive study.

Complex DNA mixtures with low template (LT) components provide the most challenging cases to interpret and report. In this study, we designed such mixtures and we describe how reporting officers (ROs) at the Netherlands Forensic Institute (NFI) assess these when embedded in a mock case setting. DNA mixtures containing LT DNA from two to four contributors, sporadic contamination (mimicked by adding 6pg of DNA, which represents once cell equivalent) and/or DNA of relatives (brothers), were amplified four-fold using the AmpFlSTR(®) NGM™ PCR Amplification Kit. Consensus profiles were then generated which included the alleles detected in at least half of the replicates. Four mock cases were created by including reference profiles of a hypothetical victim and suspect. The mock cases were assessed by eight ROs following the stepwise interpretation approach currently in use at the NFI. With this approach, the results of the comparisons between the DNA profiles of the evidentiary trace and the reference profiles are classified into four categories of evidential value [1]. The interpretations by the ROs were compared to the likelihood ratios (LRs) obtained from a probabilistic model that allows a calculation of LRs to assist the interpretation of LT DNA evidence and both were compared to the true composition of the designed mixtures.

Vaginal microbial flora analysis by next generation sequencing and microarrays; can microbes indicate vaginal origin in a forensic context?

Forensic analysis of biological traces generally encompasses the investigation of both the person who contributed to the trace and the body site(s) from which the trace originates. For instance, for sexual assault cases, it can be beneficial to distinguish vaginal samples from skin or saliva samples. In this study, we explored the use of microbial flora to indicate vaginal origin. First, we explored the vaginal microbiome for a large set of clinical vaginal samples (n = 240) by next generation sequencing (n = 338,184 sequence reads) and found 1,619 different sequences. Next, we selected 389 candidate probes targeting genera or species and designed a microarray, with which we analysed a diverse set of samples; 43 DNA extracts from vaginal samples and 25 DNA extracts from samples from other body sites, including sites in close proximity of or in contact with the vagina. Finally, we used the microarray results and next generation sequencing dataset to assess the potential for a future approach that uses microbial markers to indicate vaginal origin. Since no candidate genera/species were found to positively identify all vaginal DNA extracts on their own, while excluding all non-vaginal DNA extracts, we deduce that a reliable statement about the cellular origin of a biological trace should be based on the detection of multiple species within various genera. Microarray analysis of a sample will then render a microbial flora pattern that is probably best analysed in a probabilistic approach.

Low template STR typing: effect of replicate number and consensus method on genotyping reliability and DNA database search results.

To analyze DNA samples with very low DNA concentrations, various methods have been developed that sensitize short tandem repeat (STR) typing. Sensitized DNA typing is accompanied by stochastic amplification effects, such as allele drop-outs and drop-ins. Therefore low template (LT) DNA profiles are interpreted with care. One can either try to infer the genotype by a consensus method that uses alleles confirmed in replicate analyses, or one can use a statistical model to evaluate the strength of the evidence in a direct comparison with a known DNA profile. In this study we focused on the first strategy and we show that the procedure by which the consensus profile is assembled will affect genotyping reliability. In order to gain insight in the roles of replicate number and requested level of reproducibility, we generated six independent amplifications of samples of known donors. The LT methods included both increased cycling and enhanced capillary electrophoresis (CE) injection [1]. Consensus profiles were assembled from two to six of the replications using four methods: composite (include all alleles), n-1 (include alleles detected in all but one replicate), n/2 (include alleles detected in at least half of the replicates) and 2× (include alleles detected twice). We compared the consensus DNA profiles with the DNA profile of the known donor, studied the stochastic amplification effects and examined the effect of the consensus procedure on DNA database search results. From all these analyses we conclude that the accuracy of LT DNA typing and the efficiency of database searching improve when the number of replicates is increased and the consensus method is n/2. The most functional number of replicates within this n/2 method is four (although a replicate number of three suffices for samples showing >25% of the alleles in standard STR typing). This approach was also the optimal strategy for the analysis of 2-person mixtures, although modified search strategies may be needed to retrieve the minor component in database searches. From the database searches follows the recommendation to specifically mark LT DNA profiles when entering them into the DNA database.

Post-coital vaginal sampling with nylon flocked swabs improves DNA typing.

In the examination of sexual assault cases, DNA typing of vaginal samples mostly occurs after differential DNA extraction. Notwithstanding the differential extraction method, the DNA profiles from the seminal fraction often show the male alleles at low-level in combination with female alleles. This unfavorable ratio male to female DNA is due to a limited amount of sperm cells and an overwhelming quantity of female cells. In this study, we compared standard cotton and nylon flocked swabs for post-coital vaginal sampling. Twelve couples donated 88 vaginal swabs - 44 cotton, 44 nylon flocked - which were taken with a time since intercourse (TSI) up to 84 h. These vaginal swabs were sorted into categories on the basis of the TSI and submitted to (1) microscopic examination for the presence of male cells, (2) presumptive tests for the detection of seminal fluid and (3) DNA typing. Cellular elution was found to be 6-fold more efficient from the nylon flocked swabs. This makes microscopic analysis less time consuming as the higher cell yield and better cell morphology simplify detection of male cells. Both swab types reveal similar results regarding presumptive tests and male DNA typing. Positive presumptive tests (RSID-semen and PSA) were obtained up to 60 h TSI and male autosomal profiles up to 72 h TSI. Interestingly, over 50% of the samples negative for both presumptive tests resulted in informative male STR profiles. After differential extraction, less DNA was left on the nylon flocked swabs and more male DNA was isolated. Our results imply that the use of nylon flocked swabs for vaginal sampling will improve microscopic analysis and DNA typing in the medical forensic investigation of sexual assault cases.

Higher capillary electrophoresis injection settings as an efficient approach to increase the sensitivity of STR typing.

Evidentiary traces may contain low quantities of DNA, and regularly incomplete short tandem repeat (STR) profiles are obtained. In this study, higher capillary electrophoresis injection settings were used to efficiently improve incomplete STR profiles generated from low-level DNA samples under standard polymerase chain reaction (PCR) conditions. The method involves capillary electrophoresis with higher injection voltage and extended injection time. STR peak heights increased six-fold. Inherent to the analysis of low-level DNA samples, we observed stochastic amplification artifacts, mainly in the form of allele dropout and heterozygous peak imbalance. Increased stutter ratios and allele drop-in were rarely seen. Upon STR typing of 10:1 admixed samples, the profile of the major component did not become overloaded when using higher injection settings as was observed upon elevated cycling. Thereby an improved profile of the minor component was obtained. For low-level DNA casework samples, we adhere to independent replication of the PCR amplification and boosted capillary electrophoresis.