Investigation of the STR loci noise distributions of PowerSeq™ Auto System

Using unique molecular identifiers to improve allele calling in low-template mixtures

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.

Evaluation of ArmedXpert software tools, MixtureAce and Mixture Interpretation, to analyze MPS-STR data

Massively parallel sequencing (MPS) technologies have revolutionized studies of genomic variations and transformed DNA analysis in multiple fields. Assays based on MPS must be capable of discriminating variations introduced by the method, i.e. artifacts from true polymorphisms. In PCR-MPS methods targeting microsatellite markers, artifacts can arise from PCR mis-incorporation, PCR strand slippage (stutter), and sequencing error. Reliable detection of artifacts in mixed DNA samples is a significant challenge that must be addressed in forensic DNA analysis. The ArmedXpert (NicheVision) software tools, MixtureAce™ and Mixture Interpretation, can analyze MPS data by categorizing sequence reads in alleles, stutter, and non-stutter artifacts and analyzing autosomal STR loci of mixed samples. In this study, we evaluated the ArmedXpert tools for the analysis of STR profiles of single-sourced and mixed samples generated by the ForenSeq™ DNA Signature Prep kit (Verogen). Data from eight experimental runs (240 samples) were analyzed: one benchmark run, two runs testing sensitivity with down to 50 pg DNA input, one run testing artificially degraded samples and DNA derived from bones, blood cards and teeth, as well as four runs with mixed DNA samples of varying ratios, sex, and different number of contributors (two to six). The MixtureAce stutter thresholds were initially set following the recommendations from Verogen, plus a non-stutter artifact threshold was set at 5% of allele read counts. A benchmark run, of 30 samples, plus two controls, containing 2310 total alleles, revealed over 5000 artifacts, above an analytical threshold of 10. A total of 4869 artifacts were correctly classified, while 435 were mis-classified as alleles due to exceedance of initial threshold settings. False positives must be resolved by an analyst, which can be time consuming. Stutter thresholds were adjusted based on the benchmark data and the samples were re-tested, resulting in only 57 false positive allele calls. The revised settings were then used in the analysis of the remaining seven experimental runs. Results show that MixtureAce can accurately classify artifacts and alleles when laboratory-specific threshold settings are used. The Mixture Interpretation tool was applied on two- and three-person mixtures. This tool utilized the analyzed data from MixtureAce to calculate, based on the number of alleles at a locus and their read counts, possible deconvolution outcomes with their respective ratios.

Evaluation of Promega PowerSeq™ Auto/Y systems prototype on an admixed sample of Rio de Janeiro, Brazil: Population data, sensitivity, stutter and mixture studies

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.

Reducing noise and stutter in short tandem repeat loci with unique molecular identifiers

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.

Massively parallel sequencing of STRs using a 29-plex panel reveals stutter sequence characteristics

Massively parallel sequencing of forensic STRs simultaneously provides length-based genotypes and core repeat sequences as well as flanking sequence variations. Here, we report primer sequences and concentrations of a next-generation sequencing (NGS)-based in-house panel covering 28 autosomal STR loci (CSF1PO, D1GATA113, D1S1627, D1S1656, D1S1677, D2S441, D2S1776, D3S3053, D5S818, D6S474, D6S1017, D6S1043, D8S1179, D9S2157, D10S1435, D11S4463, D13S317, D14S1434, D16S539, D18S51, D18S853, D20S482, D20S1082, D22S1045, FGA, TH01, TPOX, and vWA) and the sex determinant locus Amelogenin. Preliminary evaluation experiments showed that the panel yielded intralocus- and interlocus-balanced sequencing data with a sensitivity as low as 62.5 pg input DNA. A total of 203 individuals from Yunnan Bai population were sequenced with this panel. Comparative forensic genetic analyses showed that sequence-based matching probability of this 29-plex panel reached 2.37 × 10^-29 , which was 23 times lower than the length-based data. Compound stutter sequences of eight STRs were compared with parental alleles. For seven loci, repeat motif insertions or deletions occurred in the longest uninterrupted repeat sequences (LUS). However, LUS and non-LUS stutters co-existed in the locus D6S474 with different sequencing depth ratios. These results supplemented our current knowledge of forensic STR stutters, and provided a sound basis for DNA mixture deconvolution.

Characterizing stutter variants in forensic STRs with massively parallel sequencing

Despite improvements in characterizing stutters of short tandem repeats (STRs), the relationships among the amounts of stutter variants and the relationships among motifs are not well understood yet. In the present study, 750 peripheral blood samples from human subjects were included to characterize the stutters of 58 STRs via the ForenSeq DNA Signature Prep Kit on a MiSeq FGx instrument. Alleles and corresponding stutter products were identified with a sequence simplification procedure. After screening, 26,921 alleles were included, that resulted in over 50 million reads, among which 8.69% were stutter products. Among these stutter products, 83.44% were N-1 stutters. Additionally, N-4, N-3, N-2, N0, N + 1, and N + 2 variants accounted for 0.11%, 0.77%, 6.45%, 3.01%, 5.95%, and 0.25% of the stutter products, respectively. For backward stutters, stutter products correlated best with the corresponding one-unit-longer stutter (or parental allele), which may represent a good predictor for backward stutters. For forward stutters, the N + 2 stutter correlated best with the N + 1 stutter, whereas the N + 1 stutter correlated best with the N-1 stutter rather than the expected parental allele, which indicated that the patterns were more complex for forward stutters. Additionally, some interesting findings were obtained for D21S11. For two adjacent contiguous motifs, co-stuttering patterns were observed where one motif tended to increase one repeat unit while the other motif decreased one repeat unit, whereas the inter-motif dependency was not significant for interrupted motifs. In conclusion, with massively parallel sequencing technology and our sequence simplification strategy, sequence variations within alleles and stutter products were identified, which was useful to determine the origin of stutters, identify more stutter variants, and explore the relationships among motifs. These findings may be helpful for allele designation, a deeper understanding of the mechanism of stutter, and improving resolution in forensic mixture analyses.

Compound stutter in D2S1338 and D12S391

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.

Massively parallel sequencing of 12 autosomal STRs in Cannabis sativa

Massively parallel sequencing (MPS) is an emerging technology in the field of forensic genetics that provides distinct advantages compared to capillary electrophoresis. This study offers a proof of concept that MPS technologies can be applied to genotype autosomal STRs in Cannabis sativa. A custom panel for MPS was designed to interrogate 12 cannabis-specific STR loci by sequence rather than size. A simple workflow was implemented to integrate the custom PCR multiplex into a workflow compatible with the Ion Plus Fragment Library Kit, Ion^™ Chef, and Ion^™ S5 System. For data sorting and sequence analysis, a custom configuration file was designed for STRait Razor v3 to parse and extract STR sequence data. This study represents a preliminary investigation of sequence variation for 12 autosomal STR loci in 16 cannabis samples. Full concordance was observed between the MPS and CE data. Results revealed intra-repeat variation in eight loci where the nominal or size-based allele was identical, but variances were discovered in the sequence of the flanking region. Although only a small number of cannabis samples were evaluated, this study demonstrates that more informative STR data can be obtained via MPS.

toaSTR: A web application for forensic STR genotyping by massively parallel sequencing

Massively parallel sequencing (MPS) is emerging within the forensic community as a promising technique for high-resolution short tandem repeat (STR) genotyping, discovering both length and sequence polymorphisms. Conversely, the application of MPS to routine casework poses new challenges to the DNA analyst in view of the complex sequence data that is generated with this technology. We developed the web application toaSTR to help forensic experts work with MPS data simply and efficiently. An intuitive graphical user interface guides through the STR genotyping workflow. This versatile software handles data from various popular MPS platforms and supports both commercial and in-house multiplex PCR kits. Users can define locus-specific stutter thresholds and create custom sets of STR markers to be analyzed. toaSTR's innovative sequence-based stutter model predicts and identifies common stutter variants. The algorithm automatically differentiates biological (iso-)alleles from stutter and other artefacts to assist the interpretation of mixed samples. toaSTR features a comprehensive data visualization with interactive diagrams and a dynamic tabular overview of sequence observations. The software provides an interface to biostatistics tools and enables PDF result export in compliance with the sequence notation recommended by the International Society for Forensic Genetics (ISFG). An initial compatibility and concordance study confirmed the software's independent functionality and precise allele calling with data of different MPS platforms, STR amplification kits, and library preparation chemistries. Discussion of genotyping results for single source and mixed samples demonstrates toaSTR's advantages and includes suggestions for future MPS software development. The beta version of toaSTR is freely accessible at www.toastr.online.

Flanking Variation Influences Rates of Stutter in Simple Repeats

It has been posited that the longest uninterrupted stretch (LUS) of tandem repeats, as defined by the number of exactly matching repeating motif units, is a better predictor of rates of stutter than the parental allele length (PAL). While there are cases where this hypothesis is likely correct, such as the 9.3 allele in the TH01 locus, there can be situations where it may not apply as well. For example, the PAL may capture flanking indel variations while remaining insensitive to polymorphisms in the repeat, and these haplotypic changes may impact the stutter rate. To address this, rates of stutter were contrasted against the LUS as well as the PAL on different flanking haplotypic backgrounds. This study shows that rates of stutter can vary substantially depending on the flanking haplotype, and while there are cases where the LUS is a better predictor of stutter than the PAL, examples to the contrary are apparent in commonly assayed forensic markers. Further, flanking variation that is 7 bp from the repeat region can impact rates of stutter. These findings suggest that non-proximal effects, such as DNA secondary structure, may be impacting the rates of stutter in common forensic short tandem repeat markers.