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Forensic DNA typing typically relies on the length-based (LB) separation of PCR products containing short tandem repeat loci (STRs). Massively parallel sequencing (MPS) elucidates an additional level of STR motif and flanking region variation. Also, MPS enables simultaneous analysis of different marker-types - autosomal STRs, SNPs for lineage and identification purposes, reducing both the amount of sample used and the turn-around-time of analysis. Therefore, MPS methodologies are being considered as an additional tool in forensic genetic casework. The PowerSeq™ Auto/Y System (Promega Corp), a multiplex forensic kit for MPS, enables analysis of the 22 autosomal STR markers (plus Amelogenin) from the PowerPlex® Fusion 6C kit and 23 Y-STR markers from the PowerPlex® Y23 kit. Population data were generated from 140 individuals from an admixed sample from Rio de Janeiro, Brazil. All samples were processed according to the manufacturers' recommended protocols. Raw data (FastQ) were generated for each indexed sample and analyzed using STRait Razor v2s and PowerSeqv2.config file. The subsequent population data showed the largest increase in expected heterozygosity (23%), from LB to sequence-based (SB) analyses at the D5S818 locus. Unreported allele was found at the D21S11 locus. The random match probability across all loci decreased from 5.9 × 10 -28 to 7.6 × 10 -33 . Sensitivity studies using 1, 0.25, 0.062 and 0.016 ng of DNA input were analyzed in triplicate. Full Y-STR profiles were detected in all samples, and no autosomal allele drop-out was observed with 62 pg of input DNA. For mixture studies, 1 ng of genomic DNA from a male and female sample at 1:1, 1:4, 1:9, 1:19 and 1:49 proportions were analyzed in triplicate. Clearly resolvable alleles (i.e., no stacking or shared alleles) were obtained at a 1:19 male to female contributor ratio. The minus one stutter (-1) increased with the longest uninterrupted stretch (LUS) allele size reads and according to simple or compound/complex repeats. The haplotype-specific stutter rates add more information for mixed samples interpretation. These data support the use of the PowerSeq TM Auto/Y systems prototype kit (22 autosomal STR loci, 23 Y-STR loci and Amelogenin) for forensic genetics applications., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Unique molecular identifiers (UMIs) are a promising approach to contend with errors generated during PCR and massively parallel sequencing (MPS). With UMI technology, random molecular barcodes are ligated to template DNA molecules prior to PCR, allowing PCR and sequencing error to be tracked and corrected bioinformatically. UMIs have the potential to be particularly informative for the interpretation of short tandem repeats (STRs). Traditional MPS approaches may simply lead to the observation of alleles that are consistent with the hypotheses of stutter, while with UMIs stutter products bioinformatically may be re-associated with their parental alleles and subsequently removed. Herein, a bioinformatics pipeline named strumi is described that is designed for the analysis of STRs that are tagged with UMIs. Unlike other tools, strumi is an alignment-free machine learning driven algorithm that clusters individual MPS reads into UMI families, infers consensus super-reads that represent each family and provides an estimate the resulting haplotype's accuracy. Super-reads, in turn, approximate independent measurements not of the PCR products, but of the original template molecules, both in terms of quantity and sequence identity. Provisional assessments show that naïve threshold-based approaches generate super-reads that are accurate (∼97 % haplotype accuracy, compared to ∼78 % when UMIs are not used), and the application of a more nuanced machine learning approach increases the accuracy to ∼99.5 % depending on the level of certainty desired. With these features, UMIs may greatly simplify probabilistic genotyping systems and reduce uncertainty. However, the ability to interpret alleles at trace levels also permits the interpretation, characterization and quantification of contamination as well as somatic variation (including somatic stutter), which may present newfound challenges., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Compound repeats provide a testing ground for evaluating hypotheses on the causes and consequences of stutter. As compound repeats have two repeating motifs, each motif may produce stutter variants; thus as the repeat within the locus is compound, so is the resultant stutter. Further, the rates of stutter formation between these motifs may not be independent. This lack of independence may complicate modeling strategies, thus contributing to the challenges of mixture interpretation that rely on nucleotide sequence. This study evaluates compound stutter in two STR loci: D2S1338 and D12S391. The effects of flanking variation, as well as possible interactions between the two different uninterrupted stretches (US) and their respective stutter variants are assessed. Multivariate multiple linear regression (MMLR) was used to show that, as with simple repeats, the rate of stutter product formation of a particular repeating motif is not solely a function of the US of that repeat. The nucleotides adjacent to the repeating motif also appear to influence the rate of stutter formation of that motif, with those nucleotides sometimes including the other motif. MMLR was used to estimate the size of these effects and to construct an example of a two-dimensional (thus, a compound) stutter prediction. This example may merit further investigation in the application of massively parallel sequencing data to mixture interpretation and probabilistic genotyping., (Copyright © 2018. Published by Elsevier B.V.)

Current approaches to mixture deconvolution of complex biological samples at times do not have the capability to resolve component contributors in DNA evidence. Additional short tandem repeat (STR) loci were sought that may improve the forensic genetic analysis of mixtures. This study presents exploratory data of a multiplex comprised of 73 highly polymorphic STRs (referred herein as the 73Plex) that were selected because of their high diversity due to sequence variation. These STRs (or a subset of them) may be considered as candidates that may augment current core markers capabilities for DNA mixture deconvolution. Population genetics analyses were performed for each locus using DNA samples from 451 individuals comprising three U.S. populations. Sequence-based heterozygosities ranged from 72% to 98%, where only two loci (D10A97 and D6A7) fell below 80%. Mixture deconvolution capabilities for two-person mixtures were assessed based on complete allele resolution per locus (i.e., four alleles observed) of pairwise mixtures using in silico methods. A subset of 20 highly informative loci (referred herein as the 20Plex) from the 73Plex was compared to the 20 CODIS core loci on all population samples with full DNA profiles for both panels (i.e., no locus dropout; n = 443). Based on proportion of loci displaying four alleles, the 20Plex outperformed the CODIS core loci with increases of 82.6% and 89.3% using length-based and sequence-based alleles, respectively. A combination of 17 STR from the 20Plex and 3 CODIS loci gave the highest capacity for resolving allelic components per locus. These data illustrate the increased value of utilizing sequenced-based alleles of additional STR loci. Furthermore, there are a number of candidate STR loci that could notably augment the current core STR loci and enhance mixture interpretation capabilities., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Due to their polymorphic nature, short tandem repeats (STRs) are well-studied and routinely used genetic markers for forensic DNA typing. However, even the largest STR multiplexes are limited in their ability to parse out individuals in a DNA mixture sample, due to alleles shared by size detected by capillary electrophoresis and challenges in resolving minor alleles from stutter, and inherent heterozygote imbalance. In this study, STRs were explored in public datasets that displayed sequence variation and may have limited allele length spread. STRs were first selected using fundamental criteria of high heterozygosity, tetra-, penta-, or hexanucleotide repeat length, and overall relative narrow allele spread (based on length). All candidates were further scrutinized for chemistry compatibility. The resulting STRs were multiplexed and sequenced by massively parallel sequencing in a limited sample population set. Each candidate STR was evaluated for analytical performance and desired biological properties. The findings presented describe a refined set of 53 potential highly polymorphic STR markers (high sequence diversity and heterozygosity; reduced allele spread) that may be suitable to supplement the current core marker set(s) for possible enhanced characterization of complex DNA profiles., (Copyright © 2018 Elsevier B.V. All rights reserved.)

The short tandem repeat allele identification tool (STRait Razor), a program used to characterize the haplotypes of short tandem repeats (STRs) in massively parallel sequencing (MPS) data, was redesigned. STRait Razor v3.0 performs ∼660× faster allele identification than its previous version (v2s), a speedup that is largely due to a novel indexing strategy used to perform "fuzzy" (approximate) string matching of anchor sequences. Written in a portable compiled language, C++, STRait Razor v3.0 functions on all major operating systems including Microsoft Windows, and it has cross-platform multithreading support. In silico estimates of precision and accuracy of STRait Razor v3.0 were 100% in this evaluation and results were highly concordant with those of Strait Razor v2s. STRait Razor v3.0 adds several key features that simplify the haplotype reporting process, including simple filters to remove low frequency haplotypes as well as merging haplotypes within a locus encoded on opposite strands of the DNA molecule., (Copyright © 2017 Elsevier B.V. All rights reserved.)

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. • An algorithm is presented for mixture detection using biallelic SNPs. • Mixture and single-source hypotheses can be formally compared. • Highly imbalanced mixtures (∼1:199) and low coverage mixtures (∼1x) can be detected. [ABSTRACT FROM AUTHOR]

Background: Multi-locus genotype data are widely used in population genetics and disease studies. In evaluating the utility of multi-locus data, the independence of markers is commonly considered in many genomic assessments. Generally, pairwise non-random associations are tested by linkage disequilibrium; however, the dependence of one panel might be triplet, quartet, or other. Therefore, a compatible and user-friendly software is necessary for testing and assessing the global linkage disequilibrium among mixed genetic data. Results: This study describes a software package for testing the mutual independence of mixed genetic datasets. Mutual independence is defined as no non-random associations among all subsets of the tested panel. The new R package "mixIndependR" calculates basic genetic parameters like allele frequency, genotype frequency, heterozygosity, Hardy–Weinberg equilibrium, and linkage disequilibrium (LD) by mutual independence from population data, regardless of the type of markers, such as simple nucleotide polymorphisms, short tandem repeats, insertions and deletions, and any other genetic markers. A novel method of assessing the dependence of mixed genetic panels is developed in this study and functionally analyzed in the software package. By comparing the observed distribution of two common summary statistics (the number of heterozygous loci [K] and the number of share alleles [X]) with their expected distributions under the assumption of mutual independence, the overall independence is tested. Conclusion: The package "mixIndependR" is compatible to all categories of genetic markers and detects the overall non-random associations. Compared to pairwise disequilibrium, the approach described herein tends to have higher power, especially when number of markers is large. With this package, more multi-functional or stronger genetic panels can be developed, like mixed panels with different kinds of markers. In population genetics, the package "mixIndependR" makes it possible to discover more about admixture of populations, natural selection, genetic drift, and population demographics, as a more powerful method of detecting LD. Moreover, this new approach can optimize variants selection in disease studies and contribute to panel combination for treatments in multimorbidity. Application of this approach in real data is expected in the future, and this might bring a leap in the field of genetic technology. Availability: The R package mixIndependR, is available on the Comprehensive R Archive Network (CRAN) at: https://cran.r-project.org/web/packages/mixIndependR/index.html. [ABSTRACT FROM AUTHOR]

PCR artifacts are an ever-present challenge in sequencing applications. These artifacts can seriously limit the analysis and interpretation of low-template samples and mixtures, especially with respect to a minor contributor. In medicine, molecular barcoding techniques have been employed to decrease the impact of PCR error and to allow the examination of low-abundance somatic variation. In principle, it should be possible to apply the same techniques to the forensic analysis of mixtures. To that end, several short tandem repeat loci were selected for targeted sequencing, and a bioinformatic pipeline for analyzing the sequence data was developed. The pipeline notes the relevant unique molecular identifiers (UMIs) attached to each read and, using machine learning, filters the noise products out of the set of potential alleles. To evaluate this pipeline, DNA from pairs of individuals were mixed at different ratios (1−1, 1−9) and sequenced with different starting amounts of DNA (10, 1 and 0.1 ng). Naïvely using the information in the molecular barcodes led to increased performance, with the machine learning resulting in an additional benefit. In concrete terms, using the UMI data results in less noise for a given amount of drop out. For instance, if thresholds are selected that filter out a quarter of the true alleles, using read counts accepts 2381 noise alleles and using raw UMI counts accepts 1726 noise alleles, while the machine learning approach only accepts 307. • A machine learning pipeline for analyzing UMI barcoded reads was created. • On low-template data, single source allele calling was improved by 13% (PR AUC). • Allele calling for a 100 pg balanced mixture was improved by 10%. [ABSTRACT FROM AUTHOR]

Short tandem repeats of the nuclear genome have been the preferred markers for analyzing forensic DNA mixtures. However, when nuclear DNA in a sample is degraded or limited, mitochondrial DNA (mtDNA) markers provide a powerful alternative. Though historically considered challenging, the interpretation and analysis of mtDNA mixtures have recently seen renewed interest with the advent of massively parallel sequencing. However, there are only a few software tools available for mtDNA mixture interpretation. To address this gap, the Mitochondrial Mixture Deconvolution and Interpretation Tool (MMDIT) was developed. MMDIT is an interactive application complete with a graphical user interface that allows users to deconvolve mtDNA (whole or partial genomes) mixtures into constituent donor haplotypes and estimate random match probabilities on these resultant haplotypes. In cases where deconvolution might not be feasible, the software allows mixture analysis directly within a binary framework (i.e. qualitatively, only using data on allele presence/absence). This paper explains the functionality of MMDIT, using an example of an in vitro two-person mtDNA mixture with a ratio of 1:4. The uniqueness of MMDIT lies in its ability to resolve mixtures into complete donor haplotypes using a statistical phasing framework before mixture analysis and evaluating statistical weights employing a novel graph algorithm approach. MMDIT is the first available open-source software that can automate mtDNA mixture deconvolution and analysis. The MMDIT web application can be accessed online at https://www.unthsc.edu/mmdit/. The source code is available at https://github.com/SammedMandape/MMDIT%5fUI and archived on zenodo (https://doi.org/10.5281/zenodo.4770184). • Design, development, implementation, and functionality of Mitochondrial Mixture Deconvolution and Interpretation Tool (MMDIT) is presented. • mtDNA mixtures can be deconvolved or analyzed within a binary framework. • Computing statistical weights -> analyzing mixtures without deconvolution. [ABSTRACT FROM AUTHOR]

• STRait Razor Online is available as an online, standalone executable, or open-source software application. • Batch-processing of >700 samples in ∼40 min. • Software-driven allele calling was consistent with manual curation for 98.72 % of loci. Since 2013, STRait Razor has enabled analysis of massively parallel sequencing (MPS) data from various marker systems such as short tandem repeats, single nucleotide polymorphisms, insertion/deletions, and mitochondrial DNA. In this paper, STRait Razor Online (SRO), available at https://www.unthsc.edu/straitrazor , is introduced as an interactive, Shiny-based user interface for primary analysis of MPS data and secondary analysis of STRait Razor haplotype pileups. This software can be accessed from any common browser via desktop, tablet, or smartphone device. SRO is available also as a standalone application and open-source R script available at https://github.com/ExpectationsManaged/STRaitRazorOnline. The local application is capable of batch processing of both fastq files and primary analysis output. Processed batches generate individual report folders and summary reports at the locus- and haplotype-level in a matter of minutes. For example, the processing of data from ∼700 samples generated with the ForenSeq Signature Preparation Kit from allsequences.txt to a final table can be performed in ∼40 min whereas the Excel-based workbooks can take 35−60 h to compile a subset of the tables generated by SRO. To facilitate analysis of single-source, reference samples, a preliminary triaging system was implemented that calls potential alleles and flags loci suspected of severe heterozygote imbalance. When compared to published, manually curated data sets, 98.72 % of software-assigned allele calls without manual interpretation were consistent with curated data sets, 0.99 % loci were presented to the user for interpretation due to heterozygote imbalance, and the remaining 0.29 % of loci were inconsistent due to the analytical thresholds used across the studies. [ABSTRACT FROM AUTHOR]

The scale of genetic methods are presently being expanded: forensic genetic assays previously were limited to tens of loci, but now technologies allow for a transition to forensic genomic approaches that assess thousands to millions of loci. However, there are subtle distinctions between genetic assays and their genomic counterparts (especially in the context of forensics). For instance, forensic genetic approaches tend to describe a locus as a haplotype, be it a microhaplotype or a short tandem repeat with its accompanying flanking information. In contrast, genomic assays tend to provide not haplotypes but sequence variants or differences, variants which in turn describe how the alleles apparently differ from the reference sequence. By the given construction, mitochondrial genetic assays can be thought of as genomic as they often describe genetic differences in a similar way. The mitochondrial genetics literature makes clear that sequence differences, unlike the haplotypes they encode, are not comparable to each other. Different alignment algorithms and different variant calling conventions may cause the same haplotype to be encoded in multiple ways. This ambiguity can affect evidence and reference profile comparisons as well as how "match" statistics are computed. In this study, a graph algorithm is described (and implemented in the MMDIT (Mitochondrial Mixture Database and Interpretation Tool) R package) that permits the assessment of forensic match statistics on mitochondrial DNA mixtures in a way that is invariant to both the variant calling conventions followed and the alignment parameters considered. The algorithm described, given a few modest constraints, can be used to compute the "random man not excluded" statistic or the likelihood ratio. The performance of the approach is assessed in in silico mitochondrial DNA mixtures. [ABSTRACT FROM AUTHOR]