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Continuous probabilistic genotyping (PG) systems are becoming the default method for calculating likelihood ratios (LRs) for competing propositions about DNA mixtures. Calculation of the LR relies on numerical methods and simultaneous probabilistic simulations of multiple variables rather than on analytical solutions alone. Some also require modelling of individual laboratory processes that give rise to electropherogram artefacts and peak height variance. For these reasons, it has been argued that any LR produced by continuous PG is unique and cannot be compared with another. We challenge this assumption and demonstrate that there are a set of conditions defining specific DNA mixtures which can produce an aspirational LR and thereby provide a measure of reproducibility for DNA profiling systems incorporating PG. Such DNA mixtures could serve as the basis for inter-laboratory comparisons, even when different STR amplification kits are employed. We propose a procedure for an inter-laboratory comparison consistent with these conditions.

Current analysis of forensic DNA stains relies on the probabilistic interpretation of bulk-processed samples that represent mixed profiles consisting of an unknown number of potentially partial representations of each contributor. Single-cell methods, in contrast, offer a solution to the forensic DNA mixture problem by incorporating a step that separates cells before extraction. A forensically relevant single-cell pipeline relies on efficient direct-to-PCR extractions that are compatible with standard down- stream forensic reagents. Here we demonstrate the feasibility of implementing single-cell pipelines into the forensic process by exploring four metrics of electropherogram (EPG) signal quality—i.e., allele detection rates, peak heights, peak height ratios, and peak height balance across low- to high-molecular-weight short tandem repeat (STR) markers—obtained with four direct-to-PCR extraction treatments and a common post-PCR laboratory procedure. Each treatment was used to extract DNA from 102 single buccal cells, whereupon the amplification reagents were immediately added to the tube and the DNA was amplified/injected using post-PCR conditions known to elicit a limit of detection (LoD) of one DNA molecule. The results show that most cells, regardless of extraction treatment, rendered EPGs with at least a 50% true positive allele detection rate and that allele drop-out was not cell independent. Statistical tests demonstrated that extraction treatments significantly impacted all metrics of EPG quality, where the Arcturus® PicoPure™ extraction method resulted in the lowest median allele drop-out rate, highest median average peak height, highest median average peak height ratio, and least negative median values of EPG sloping for GlobalFiler™ STR loci amplified at half volume. We, therefore, conclude the feasibility of implementing single-cell pipelines for casework purposes and demonstrate that inferential systems assuming cell independence will not be appropriate in the probabilistic interpretation of a collection of single-cell EPGs.

Background: Monoclonal protein (M-protein) concentrations are measured by serum protein electrophoresis (SPE). Two methods are used for demarcating the M-protein area in the electropherogram: perpendicular drop (PD) and tangent skimming (TS). The aim of this study was to compare both methods and to establish which is the most accurate and precise. Methods: We studied 24 sera containing M-protein (5-44 g/L). The systematic error (SE) was evaluated in a dilution series of 12 sera. Within-day, between-day, and interobserver variability were assessed. SPE was performed by capillary and agarose gel electrophoresis. M-protein concentrations were measured using both cutoff methods. Results: The PD method shows a constant SE ranged 1.00-2.27 g/L, while constant SE for TS is ranged - 0.30-- 0.57 g/L. None of the cutoff methods or electrophoretic methods showed a proportional SE, with the exception of the TS method in capillary electrophoresis for beta-migrating M-protein. The PD method was more precise than the TS method in all three estimates of imprecision. An increased CV for concentrations < 10 g/L in between-day imprecision was observed with the TS method. Interobserver imprecision was greater for M-protein concentrations < 17 g/L for both cutoff methods (14.85%, 26.42% respectively). Conclusions: Despite being less precise, the TS method provides a more accurate measurement of M-protein concentration.

The accurate assessment of antibody glycosylation during bioprocessing requires the high-throughput generation of large amounts of glycomics data. This allows bioprocess engineers to identify critical process parameters that control the glycosylation critical quality attributes. The advances made in protocols for capillary electrophoresis-laser-induced fluorescence (CE-LIF) measurements of antibody N-glycans have increased the potential for generating large datasets of N-glycosylation values for assessment. With large cohorts of CE-LIF data, peak picking and peak area calculations still remain a problem for fast and accurate quantitation, despite the presence of internal and external standards to reduce misalignment for the qualitative analysis. The peak picking and area calculation problems are often due to fluctuations introduced by varying process conditions resulting in heterogeneous peak shapes. Additionally, peaks with co-eluting glycans can produce peaks of a non-Gaussian nature in some process conditions and not in others. Here, we describe an approach to quantitatively and qualitatively curate large cohort CE-LIF glycomics data. For glycan identification, a previously reported method based on internal triple standards is used. For determining the glycan relative quantities our method uses a clustering algorithm to ‘divide and conquer’ highly heterogeneous electropherograms into similar groups, making it easier to define peaks manually. Open-source software is then used to determine peak areas of the manually defined peaks. We successfully applied this semi-automated method to a dataset (containing 391 glycoprofiles) of monoclonal antibody biosimilars from a bioreactor optimization study. The key advantage of this computational approach is that all runs can be analyzed simultaneously with high accuracy in glycan identification and quantitation and there is no theoretical limit to the scale of this method.

Fluorescent microspheres (FMs) are widely employed in diagnostics and life sciences research; here, we investigated the effect of capillary coating, polymer concentration, electric field strength, and sample concentration on the separation performance of 1.0 μm FMs in hydroxyethyl cellulose (HEC) by capillary electrophoresis (CE). Results showed that (1) capillary coating could enhance the fluorescence signal. (2) For HEC with the same molecular weight, the higher HEC concentration is, the later the first peak appears in the electropherogram. (3) When FMs are diluted, increasing the electric field strength can enhance the migration speed and reduce the aggregation of FMs. (4) The number of FMs calculated is close to the theoretical value when it is diluted 10,000 times. The optimum conditions for CE were as follows: 6 cm/8 cm of effective length and total length of the coated capillary, 0.3% HEC (1300 k), and 300 V/cm of electric field strength. Such a study is helpful for the development of a FM counting system. Graphical abstract.

Human identification using forensic DNA profiling has made enormous advancement over the past two-and-half decades. Forensic DNA profiling provides enormous genetic data from a variety of biological materials and individuals to help solve many important criminal and civil cases that confront society. Under certain environmental conditions, the total deterioration of soft-tissue leaves skeletal remains as the only available sample for DNA testing to identify missing persons, victims of natural disasters, or exonerate suspect(s) in a criminal case. We report the findings of a case involving the human remains of a missing person submitted to the Forensic Science Laboratory of the Ghana Police Service for forensic DNA profiling in comparison to an alleged living relative of the deceased. DNA from the femur bone and buccal swabs of alleged relative of the deceased were extracted, quantified, and short tandem repeat (STR) profiled using Qiagen's Investigator kit, Applied Biosystem's Quantifiler trio, and GlobalFiler kits. Full STR profiles were generated for both the femur bone from the salty environment and the buccal swabs from the alleged relative. The femur bone was genetically identified to be that of the missing person. The remains were thus handed over to the relatives for final funeral rites and burial to bring closure to the long search for the missing person.

A simple and robust ultra-small four-color-fluorescence detection system was developed by integrating its components, namely, a four-capillary array, an injection-molded-plastic four-lens array, a four-dichroic-mirror array, and a CMOS sensor, as one device. The developed system was applied to a high-dynamic-range capillary-array electrophoresis (HiDy CE) to quantify a rare EGFR mutant (MT) of exon 19 deletion in a large excess of EGFR wild type (WT). Samples with serially diluted MT and constant-concentration WT were co-amplified by competitive PCR and subjected to HiDy CE. The MT peak in each electropherogram was then compared to the WT peak. As a result, MT was quantified with high-sensitivity (LOD of 0.004% MT/WT) and four-orders-of-magnitude dynamic range (0.01-100% MT/WT) by HiDy CE. Moreover, compared with existing methods, HiDy CE achieves higher speed, higher sample throughput, and lower consumable cost per sample. It has therefore great potential to be used in clinical practice.

The significant demands for single nucleotide polymorphism detection and genotyping assays have grown. Most common assays are based on the recognition of the target sequence by the hybridization with its specific probe having the complementary sequence of the target. Herein, a simple, label-free, and economical non-hybridization assay was developed for single nucleotide polymorphism detection and genotyping, based on the direct discrimination of single base mutation by simple capillary electrophoresis separation for single-stranded DNA in an acidic electrophoretic buffer solution containing urea. Capillary electrophoresis separation of single-base sequential isomers of DNA was achieved due to charge differences resulting from the different protonation properties of the DNA bases. Single nucleotide polymorphism detection and genotyping were achieved by discriminating the electropherogram pattern change, that is, peak number in the electropherogram, obtained by the proposed method. The successful practical application of the proposed method was demonstrated through single nucleotide polymorphism detection and genotyping on a known gene region of 84-mer, in which guanine to adenine single-base mutation is commonly observed, using a human hair sample in combination with genomic DNA extraction, polymerase chain reaction amplification, DNA purification from polymerase chain reaction products, and capillary electrophoresis separation.

An electrokinetically pumped sheath-flow nanoelectrospray interface provides an efficient means of transferring ions from a capillary electrophoresis system into a mass spectrometer. To characterize its performance, we analyzed angiotensin solutions prepared in a background of 0.25 mg/mL of a BSA tryptic digest. Calibration curves were prepared from 10 zeptomole (10(−21) mol) to 10 femtomole (10(−14) mol) of angiotensin injected into the capillary. A parallel reaction monitoring approach was used; MS1 was set to m/z = 523.8 ± 2, and fragment ion signals at 263.1389 (y(2+)) and 784.4095 (b(6+)) were used to generate selected ion electropherograms. Calibration curves based on peak areas were linear (log-log slope of 0.94 for the y(2+) fragment and 0.98 for the b(6+) fragment). We then injected 1-zmol (600 copies) of angiotensin in the BSA background using a 10-μm ID separation capillary. Triplicate analyses consistently produced co-migrating peaks for the two fragment ions. The b(6+) electropherogram showed no background signal, whereas the y(2+) electropherogram showed a few noise spikes that were smaller than the peak maximum. Bienayme-Tchebycheff inequality was used to estimate detection limits of 230 ymol (140 ions) injected into the separation capillary. To the best of our knowledge, these electropherograms present the smallest number of molecules detected using mass spectrometry coupled with a separation.

DNA profiles are generated in forensic biology laboratories around the world. It is possible that these profiles are assessed by two independent people in order for the profiles to be ‘read’. Recent work has been carried out to develop a neural network model to classify fluorescence in a DNA profile electropherogram and potentially replace one, or both human readers. The ability to use neural networks for this function has been programmed into the software FaSTR™ DNA, which has been validated for use in at least one laboratory in Australia. The work that previously developed a neural network system had a number of limitations, specifically it was computer intensive, did not make the best use of available data, and consequently the performance of this model was sub-optimal in some conditions (particularly for low-intensity peaks). In the current work a new neural network model is developed that makes various improvements on the old model, by using convolutional layers, a multi-head architecture and data augmentation. Results indicate that an improved performance can be expected for low-intensity profiles.

This study shows that the upper limit of binding (stability) constants determined by mobility shift affinity capillary electrophoresis can be increased from 104 to 106 -109 L/mol if the lowest possible analyte concentration in samples is used (for example, the concentration that gives electrophoretic peaks with a signal-to-noise ratio of 10) and the effective electrophoretic mobility of the analyte is calculated via the parameter a1 of the Haarhoff-Van der Linde function. The equation to calculate the boundary values of binding constants for 1:1 complexes was derived for the case when the constants cannot be calculated in the usual way. These values are obtained from the inequality: the difference between the ionic mobility of the analyte-ligand complex and the effective electrophoretic mobility of the analyte determined at the lowest ligand concentration in the background electrolyte at which the analyte appears as an undistorted peak in electropherograms is less than or equal to the absolute error in mobility measurements. The application of the equation was illustrated by the example of electrophoretic data for a complex between betulin 3,28-diphthalate and (2-hydroxypropyl)-γ-cyclodextrin. An algorithm to determine the binding constants for strong complexation by mobility shift affinity capillary electrophoresis was suggested.

Polyacrylamide gel electrophoresis is widely used for studying proteins and protein-containing objects. However, it is employed most frequently as a qualitative method rather than a quantitative one. This paper shows the feasibility of routine digital image acquisition and mathematical processing of electropherograms for protein quantification when using vertical gel electrophoresis and Chrom &amp, Spec software. Both the well-studied model protein molecules (bovine serum albumin) and more complex real-world protein-based products (casein-containing isolate for sports nutrition), which were subjected to mechanical activation in a planetary ball mill to obtain samples characterized by different protein denaturation degrees, were used as study objects. Protein quantification in the mechanically activated samples was carried out. The degree of destruction of individual protein was shown to be higher compared to that of the protein-containing mixture after mechanical treatment for an identical amount of time. The methodological approach used in this study can serve as guidance for other researchers who would like to use electrophoresis for protein quantification both in individual form and in protein mixtures. The findings prove that photographic imaging of gels followed by mathematical data processing can be applied for analyzing the electrophoretic data as an affordable, convenient and quick tool.

Although insect exuviae-based genetics is challenging, it can be a valuable method for obtaining reliable DNA resources by non-invasive sampling. This approach is especially effective when the target species is endangered/endemic or when sampling the adult is difficult. One example is cicadas, which during molt leave their exoskeletons on tree trunks, making them easily collectable. While cicada exuviae-derived DNA has previously been employed for mitochondrial DNA sequencing, this study aimed to develop a reliable method for the PCR amplification of nuclear microsatellite loci from cicada exuviae derived DNA for application in molecular ecology, conservation and population genetics. Five different PCR amplification protocols were performed, and the fragment patterns compared with those obtained using DNA extracted from adult individuals. Moreover, the relationship between the freshness of the exuviae and genotyping success was evaluated. TaKaRa LA Taq provided the best performance in the PCR amplification of DNA isolated from cicada exuviae and the electropherogram showed a clear fragment pattern that was equivalent to that obtained from the DNA extracted from the adult individual. This result suggests that cicada exuviae-derived DNA can be amplified by PCR and that multiple independent loci of nuclear DNA microsatellite markers can be easily genotyped. This study demonstrates that fresh cicada exuviae provide high quality DNA, which can be used for microsatellite genotyping. The methods developed in this study are applicable not only for cicada but other insect species for which exuviae are available. Thus, this study can make a significant contribution to insect sciences.

The analysis of forensic DNA profiles is complicated by the presence of undesirable artefactual peaks such as stutter, pull-up, and dye blobs. We describe the developmental validation of FaSTR™ DNA analysis software, which can assist with the streamlined analysis of forensic DNA profiles. A total of 3403 single-source and mixed DNA profiles generated using seven different STR profiling kits were analysed in FaSTR™ DNA. Results were compared with outputs from GeneMapper™ ID-X analysis software. Over 99.95% of peak designations were concordant, corresponding to over 232,000 peaks. There were no significant differences found in peak size however differences in peak height were observed between the software programmes. Peaks greater than 1000 rfu were generally taller in GeneMapper™ ID-X than in FaSTR™ DNA. At lower peak heights, peaks were often taller in FaSTR™ DNA, due to differences in how each software applies baseline correction to the electropherogram. Allele-specific stutter filters in FaSTR™ DNA correctly designated 99.3% of stutter peaks analysed (5174 peaks comprising four stutter types) in 265 single-source GlobalFiler™ profiles. FaSTR™ DNA was shown to be effective for its intended use as analysis software for the designation of alleles and the identification of artefacts such as stutter, pull-up, and spikes.

CE–MS is increasingly gaining momentum as an analytical tool in metabolomics, due to its ability to obtain information about the most polar elements in biological samples. This has been helped by improvements of robustness in peak identification by means of mobility‐scale representations of the electropherograms (mobilograms). As a necessary step toward facilitating the use of CE–MS for untargeted metabolomics data, the authors previously developed and introduced ROMANCE, a software automating mobilogram generation for large untargeted datasets through a simple and self‐contained user interface. Herein, we introduce a new version of ROMANCE including new features such as compatibility with other types of data (targeted MS data and 2D UV‐Vis absorption‐like electropherograms), and the much needed additional flexibility in the transformation parameters (including field ramping and the use of secondary markers), more measurement conditions (depending on detection and integration modes), and most importantly tackling the issue of quantitative peak conversion. First, we present a review of the current theoretical framework with regard to peak characterization, and we develop new formulas for multiple marker peak area corrections, for anticipating peak position precision, and for assessing peak shape distortion. Then, the new version of the software is presented and validated experimentally. We contrast the multiple marker mobility transformations with previous results, finding increased peak position precision, and finally we showcase an application to actual untargeted metabolomics data.

The conventional capillary electrophoresis (CE) genotyping workflow used in forensic DNA laboratories is composed of two processes: measurement and interpretation. The outcome of the measurement process is an electropherogram (EPG). The outcome of the interpretation process is a strength of evidence statement often reported in the form of a likelihood ratio (LR) which typically requires probabilistic genotyping software (PGS). An LR system is defined as the entire pipeline of the measurement and interpretation processes where PGS is a piece of the whole LR system. To gain understanding on how two LR systems perform, a total of 154 two-person mixture, 147 three-person mixture, and 127 four-person mixture profiles of varying DNA quality, DNA quantity, and mixture ratios were obtained from the filtered (.CSV) files of the GlobalFiler 29 cycles 15s PROVEDIt dataset and deconvolved in two independently developed fully continuous programs, STRmix v2.6 and EuroForMix v2.1.0. Various parameters were set in each software and LR computations obtained from the two software were based on same/fixed EPG features, same pair of propositions, number of contributors, theta, and population allele frequencies. The ability of each LR system to discriminate between contributor (H1-true) and non-contributor (H2-true) scenarios was evaluated qualitatively and quantitatively. Differences in the numeric LR values and their corresponding verbal classifications between the two LR systems were compared. The magnitude of the differences in the assigned LRs and the potential explanations for the observed differences greater than or equal to 3 on the log10 scale were described. Cases of LR < 1 for H1-true tests and LR > 1 for H2-true tests were also discussed. Our intent is to demonstrate the value of using a publicly available ground truth known mixture dataset to assess discrimination performance of any LR system and show the steps used to investigate and understand similarities and differences between different LR systems. We share our observations with the forensic community and describe how examining more than one PGS with similar discrimination power can be beneficial, help analysts compare interpretation especially with low-template profiles or minor contributor cases, and be a potential additional diagnostic check even if software in use does contain certain diagnostic statistics as part of the output.HighlightsThe use of two different Likelihood Ratio (LR) systems to assign LRs is discussed.H1-true and H2-true tests are performed using STRmix and EuroForMix and a large set of PROVEDIt mixture profiles.Assessment of discrimination performance of two LR systems using ROC plots, scatter plots, and relative frequency histograms.The ability of the two LR systems to discriminate between contributors and non-contributors are statistically indistinguishable for the data that we considered.Potential reasons for the differences in LR values between the two LR systems that are ≥ 3 on the log10 scale are investigated and discussed.Contributors with LRs < 1 and non-contributors with LRs > 1 generated from each LR system are discussed.

The holdup assay is an automated high-throughput comparative chromatographic retention approach that allows to measure quantitative binding intensities (BI) for a large number of domain-motif pairs and deduce equilibrium binding affinity constants. We routinely apply this approach to obtain quantitative binding specificity profiles of particular PDZ-binding motifs (PBMs) toward the full library of known human PDZ domains (the PDZome). The quality of the electropherograms extracted from the capillary electrophoresis instrument at the final step of the holdup assay may vary, influencing the accuracy and reproducibility of the measurement. By using bioinformatic tools, we can solve these issues to extract more reliable BIs by means of a better superimposition of the electropherograms. The protocol presented in this chapter describes the main principles and strategies of our curated method to process holdup data and new ways to plot and compare the BIs for the PBM-PDZ interactions. For this particular protocol, all the necessary computing commands are freely available in open Python packages.

Dear Editor A recent publication [1] has claimed to address criticism of probabilistic genotyping by the President’s Council of Advisors on Science and Technology (PCAST) report [2] and addendum [3]. PCAST considered probabilistic genotyping to have “foundational validity … under limited circumstances (specifically, a three-person mixture in which the minor contributor constitutes at least 20 percent of the DNA in the mixture), but that substantially more evidence is needed to establish foundational validity across broader settings” [2]. For more than three contributors and/or for mixtures with minor contributors constituting less than 20% of the DNA, PCAST recommends “the creation and dissemination … of large collections of hundreds of DNA profiles created from known mixtures” [2]. Bright et al. [1] draw upon guidelines for testing reliability and validity of probabilistic genotyping published by The Scientific Working Group on DNA Analysis Methods (SWGDAM) [4] and the International Society for Forensic Genetics (ISFG) [5] to produce an internal validation of continuous probabilistic genotyping using STRmix™ on electropherogram (epg) outputs provided by multiple laboratories. Their study extends an internal validation performed only in the FBI Laboratory at Quantico, Virginia (USA) [6]. We believe that the publication by Bright et al. [1] is a valuable first step in the direction of establishing a foundational validity for mixture interpretation using probabilistic software, as recommended by the PCAST report. We hope that our suggestions would help to better address this issue.

An ‘Autologous Blood Transfusion’ (ABT) is the reinjection of blood previously taken from an athlete to increase its oxygen transport capabilities. Despite the World Anti-Doping Agency's ban on such practices, ABT abuse continues. Autologous blood doping (ABD) is challenging to detect because of the similarities between an individual's doped and undoped blood. Recently, Harrison et al. reported that high-speed capillary electrophoresis may identify ABD. In their work, first order derivatives of the electropherograms were used to identify doping. However, this method suffered from false negatives due to the subjective nature of the analysis. Here, we provide an informatics analysis of the data from this study, contrasting the results of traditional statistical methods and less traditional mathematical techniques. First, three well-known multivariate statistical tools: cluster analysis, principal component analysis (PCA), and partial least squares (PLS) are applied to develop calibrations and/or group electropherograms of undoped (0%) and doped (5% and 10%) blood samples. (These doping levels were chosen due to the low physiological effect of doping below 5%, with 10% corresponding to the approximate ‘gain’ derived from the transfusion of a single unit of blood into an adult.) Different preprocessing and variable selection methods were considered. Due to variation in the electropherograms and the limited sample size, these methods were inadequate. We next considered four less commonly used mathematical/informatics tools: pattern recognition entropy (PRE), the Euclidean distance between vectors, a peak fitting/integration method, and the second moment (SM). Each of these techniques showed some ability to differentiate between the 0, 5, and 10% doped samples. We then evaluated the prediction capabilities of inverse least squares (ILS) models based on these summary statistics. An ILS calibration based on PRE, the Euclidean distance, and peak fitting/integration proved more successful than the PLS model at predicting levels of blood doping from the corresponding electropherograms; the ILS model distinguished between doped (5% and 10%) and undoped (0%) blood. This methodology may be applicable to other challenging informatics problems like determining risk factors for genetically linked diseases, robust pattern finding in peak-like data such as ChIP-seq, or other genomic sequencing for understanding the 3D genome.

CE application in aptamer selection (CE-SELEX) shows more advantages than other selection methods. In this study, an online reaction based single-step CE (ssCE) mode was employed for fast obtaining protein-ssDNA complex. Using human thrombin (H-Thr) and its aptamer Apt29 as models, we accomplished the procedures of mixing, reaction, separation, detection and complex collection in single step online process, which took about 10 min to obtain the H-Thr/Apt29 complex. Important factors, affecting the aptamer and H-Thr interaction (buffer, ratio of aptamer and H-Thr amount), and complex separation and collection (voltage and temperature) were discussed. Later, the online reaction of H-Thr with an 80 nt ssDNA library was realized under optimized conditions, and the H-Thr/ssDNA complex was collected and subjected to PCR. By analyzing the PCR product through capillary gel electrophoresis, the resulting approximative 80 nt DNA length validated the ssDNA sequence in complex. To confirm the availability of ssCE mode, two ssDNA libraries with different lengths (56 nt and 82 nt ssDNA) and three proteins (platelet derived growth factor, PDGF-BB; lactoferrin protein, LF; and single-strand DNA binding protein, SSB) were utilized. Their complex peaks were also observed in electropherograms as expected. Additionally, the online incubation of ssDNA and H-Thr was achieved by stopping the separation voltage for some time when ssDNA passed the H-Thr zone. Our results show the ssCE mode has apparent merits of saving time and sample cost for aptamer selection against protein targets.

Interlaboratory studies are a type of collaborative exercise in which many laboratories are presented with the same set of data to interpret, and the results they produce are examined to get a "big picture" view of the effectiveness and accuracy of analytical protocols used across participating laboratories. In 2005 and again in 2013, the Applied Genetics Group of the National Institute of Standards and Technology (NIST) conducted interlaboratory studies involving DNA mixture interpretation. In the 2005 NIST MIX05 study, 69 laboratories interpreted data in the form of electropherograms of two-person DNA mixtures representing four different mock sexual assault cases with different contributor ratios. In the 2013 NIST MIX13 study,108 laboratories interpreted electropherogram data for five different case scenarios involving two, three, or four contributors, with some of the contributors potentially related. This paper describes the design of these studies, the variations observed among laboratory results, and lessons learned.

Forensic DNA signal is notoriously challenging to assess, requiring computational tools to support its interpretation. Over-expressions of stutter, allele drop-out, allele drop-in, degradation, differential degradation, and the like, make forensic DNA profiles too complicated to evaluate by manual methods. In response, computational tools that make point estimates on the Number of Contributors (NOC) to a sample have been developed, as have Bayesian methods that evaluate an A Posteriori Probability (APP) distribution on the NOC. In cases where an overly narrow NOC range is assumed, the downstream strength of evidence may be incomplete insofar as the evidence is evaluated with an inadequate set of propositions. In the current paper, we extend previous work on NOCIt, a Bayesian method that determines an APP on the NOC given an electropherogram, by reporting on an implementation where the user can add assumed contributors. NOCIt is a continuous system that incorporates models of peak height (including degradation and differential degradation), forward and reverse stutter, noise, and allelic drop-out, while being cognizant of allele frequencies in a reference population. When conditioned on a known contributor, we found that the mode of the APP distribution can shift to one greater when compared with the circumstance where no known contributor is assumed, and that occurred most often when the assumed contributor was the minor constituent to the mixture. In a development of a result of Slooten and Caliebe (FSI:G, 2018) that, under suitable assumptions, establishes the NOC can be treated as a nuisance variable in the computation of a likelihood ratio between the prosecution and defense hypotheses, we show that this computation must not only use coincident models, but also coincident contextual information. The results reported here, therefore, illustrate the power of modern probabilistic systems to assess full weights-of-evidence, and to provide information on reasonable NOC ranges across multiple contexts.

Although forensic DNA testing is well established, some experts disagree with the interpretation and statistical significance of test results obtained from very small samples. This article discusses the problems regarding the use of the low copy number (LCN) technique as well as the value that can be derived from such an analysis. It focuses on the problematic results that can arise from using very small samples for forensic DNA identification. Since this kind of analysis is based on low amounts of DNA samples (between 100 picograms and 200 picograms in South Africa) that are amplified by using more than the normal 28 cycles to create larger samples for analysis, the reliability of the analysis has been questioned. The amplification process, known as the Polymerase Chain Reaction (PCR), is associated with risks such as stochastic effects and contamination that could make interpretation of the results difficult for the defence. While standard operating laboratory protocols could prevent contamination and although the electropherograms could aid the detection of contamination, it is highly problematic for the defence counsel to ascertain whether these procedures were indeed strictly followed. Drawing on foreign jurisprudence, this article considers the risks and key controversies and explains what lawyers need to know, in order to be able to recognise controversial results that could stem from using the LCN DNA technique for forensic DNA identification. The conclusions thus drawn may be of particular relevance to the South African context, as no reported case law exists in which the issues relating to the use of LCN DNA have yet come to the fore.

Complex DNA mixtures are challenging to interpret and require computational tools that aid in that interpretation. Recently, several computational methods that estimate the number of contributors (NOC) to a sample have been developed. Unlike analogous tools that interpret profiles and report LRs, NOC tools vary widely in their operational principle where some are Bayesian and others are machine learning tools. Conjunctionally, NOC tools may return a single n estimate, or a distribution on n. This vast array of constructs, coupled with a gap in standardized methods by which to validate NOC systems, warrants an exploration into the measures by which differing NOC systems might be tested for operations. In the current paper, we use two exemplar NOC systems: a probabilistic system named NOCIt, which renders an a posteriori probability (APP) distribution on the number of contributors given an electropherogram and an artificial neural network (ANN). NOCIt is a continuous Bayesian inference system incorporating models of peak height, degradation, differential degradation, forward and reverse stutter, noise and allelic drop-out while considering allele frequencies in a reference population. The ANN is also a continuous method, taking all the same features (barring degradation) into account. Unlike its Bayesian counterpart, it demands substantively more data to parameterize, requiring synthetic data. We explore each system’s performance by conducting tests on 214 PROVEDIt mixtures where the limit of detection was 1-copy of DNA. We found that after a lengthy training period of approximately 24 h, the ANN’s evaluation process was very fast and perfectly repeatable. In contrast, NOCIt only took a few minutes to train but took tens of minutes to complete each sample and was less repeatable. In addition, it rendered a probability distribution that was more sensitive and specific, affording a reasonable method by which to report all reasonable n that explain the evidence for a given sample. Whatever the method, by acknowledging the inherent differences between NOC systems, we demonstrate that validation constructs will necessarily be guided by the needs of the forensic domain and be dependent upon whether the laboratory seeks to assign a single n or range of n.

Lab-on-a-chip technology is an emerging and convenient system to easily and quickly separate proteins of high molecular weight. The current study established a high-molecular-weight glutenin subunit (HMW-GS) identification system using Lab-on-a-chip for three, six, and three of the allelic variations at the Glu-A1, Glu-B1, and Glu-D1 loci, respectively, which are commonly used in wheat breeding programs. The molecular weight of 1Ax1 and 1Ax2* encoded by Glu-A1 locus were of 200 kDa and 192 kDa and positioned below 1Dx subunits. The HMW-GS encoded by Glu-B1 locus were electrophoresed in the following order below 1Ax1 and 1Ax2*: 1Bx13 &ge, 1Bx7 = 1Bx7OE &gt, 1Bx17 &gt, 1By16 &gt, 1By8 = 1By18 &gt, 1By9. 1Dx2 and Dx5 showed around 4-kDa difference in their molecular weights, with 1Dy10 and 1Dy12 having 11-kDa difference, and were clearly differentiated on Lab-on-a-chip. Additionally, some of the HMW-GS, including 1By8, 1By18, and 1Dy10, having different theoretical molecular weights showed similar electrophoretic mobility patterns on Lab-on-a-chip. The relative protein amount of 1Bx7OE was two-fold higher than that of 1Bx7 or 1Dx5 and, therefore, translated a significant increase in the protein amount in 1Bx7OE. Similarly, the relative protein amounts of 8 &amp, 10 and 10 &amp, 18 were higher than each subunit taken alone. Therefore, this study suggests the established HMW-GS identification system using Lab-on-a-chip as a reliable approach for evaluating HMW-GS for wheat breeding programs.

A novel method for Sanger DNA sequencing, as part of a compact and inexpensive DNA sequencer, using the RGB image sensor of a digital color camera was developed. Since the RGB image sensor can only detect up to three colors (wavelength bands), while Sanger DNA sequencing is required to detect four or more colors in order to quantify fluorescence of four different fluorophores in a mixed state during capillary electrophoresis, it is difficult to perform Sanger DNA sequencing using an RGB image sensor. Moreover, due to the spectral response of an RGB image sensor, fluorescence of the four fluorophores is detected only on the red and green channels, that is, not on the blue channel. To address the above-described issues, a two-color electropherogram was obtained by capillary electrophoresis of a sequencing sample and reproduced by best local fittings of two-color single peaks of the four fluorophores in chronological order. The fitted single peaks of the four fluorophores respectively give fluorescence intensities of the four fluorophores. That is, the two-color electropherogram is converted to a four-fluorophore electropherogram. The proposed Sanger-DNA-sequencing method was applied to a model sequencing sample, and the DNA sequence of 69-144 bases of the sample was accurately determined. This result is the first successful quantification of four or more fluorophores in a mixed state by using an RGB image sensor.