Systematic evaluation of STRmix™ performance on degraded DNA profile data

Duke, Kyle; Myers, Steven

doi:10.1016/j.fsigen.2019.102174

Cited by 9 publications

(5 citation statements)

References 18 publications

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“…Reference Ability of STRmix™ to deconvolute profiles and assign LRs that comport to manual interpretation and human expectation [15] Ability of STRmix™ to discriminate between donors and non-donors in database searches [190] Behaviour of STRmix™ to assign LRs when dealing with multiple replicates, different number of contributors, and assumed contributors [163] Sensitivity of LR produced by STRmix™ to different factors of uncertainty such as theta, relatedness of alternate DNA source and length of MCMC analysis [171] Tests to be performed when validating probabilistic genotyping, using STRmix™ as an example [112] Ability of individuals from different laboratories to standardise evaluations when using STRmix™ [33,53] Ability of STRmix™ to reliably use peak height information in very low intensity profiles [56,132,210] Ability of STRmix™ to discriminate between donors and non-donors in large-scale Hd true tests, or using importance sampling [59,60,190,200,21 2,213] Sensitivity of STRmix™ model parameters to laboratory factors [196,198] Ability of STRmix™ to utilise information from profiles produced under different laboratory conditions within a single analysis [155] Effect of mixture complexity, allele sharing and contributor proportions on the ability STRmix™ to distinguish contributors from non-contributors [54] The ability of STRmix™ to identify common DNA donors in mixed samples [25,159] The sensitivity of LRs produced in STRmix™ to the choice of the number of contributors [71,72,97] Ability to use STRmix™ to resolve major components of mixtures [72] Testing the assumption of additivity of peak heights in STRmix™ models [159,160] Performance of the degradation model within STRmix™ [214] The effect of relatedness of contributors to the STRmix™ analysis [203,215] Testing the calibration of LRs produced in STRmix™ [58] Validation overviews of STRmix™ [205,216] Comparison of STRmix™ ...…”

Section: Focus Of Validationmentioning

confidence: 99%

A Review of Probabilistic Genotyping Systems: EuroForMix, DNAStatistX and STRmix™

Gill

Benschop

Buckleton

et al. 2021

Genes

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Probabilistic genotyping has become widespread. EuroForMix and DNAStatistX are both based upon maximum likelihood estimation using a γ model, whereas STRmix™ is a Bayesian approach that specifies prior distributions on the unknown model parameters. A general overview is provided of the historical development of probabilistic genotyping. Some general principles of interpretation are described, including: the application to investigative vs. evaluative reporting; detection of contamination events; inter and intra laboratory studies; numbers of contributors; proposition setting and validation of software and its performance. This is followed by details of the evolution, utility, practice and adoption of the software discussed.

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Section: Focus Of Validationmentioning

confidence: 99%

A Review of Probabilistic Genotyping Systems: EuroForMix, DNAStatistX and STRmix™

Gill

Benschop

Buckleton

et al. 2021

Genes

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“…These restrictions are removed and the full potential of the DNA typing data is realized by statistical software packages that integrate probabilistic interpretation models [73,74]. Fully continuous DNA mixture interpretation software, such as STRmixTM, has been extensively tested and used in forensic DNA labs to analyze STR data [75]. STRmixTM determines the probability of the profile given all potential genotype combinations by utilizing quantitative data from an EPG, such as peak heights.…”

Section: Discussionmentioning

confidence: 99%

“…The likelihood of the EPG given each potential genotype combination at a locus is given a relative weight by STRmixTM [70]. Therefore, using probabilistic genotyping software facilitates the control of imported trace samples, particularly those with damaged DNA [75,76]. It also enables the comparison of the presence or absence of alleles in the reference profile and the trace sample directly.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence and Forensic Genetics: Current Applications and Future Perspectives

Sessa,

Esposito,

Cocimano

et al. 2024

Applied Sciences

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The term artificial intelligence (AI) was coined in the 1950s and it has successfully made its way into different fields of medicine. Forensic sciences and AI are increasingly intersecting fields that hold tremendous potential for solving complex criminal investigations. Considering the great evolution in the technologies applied to forensic genetics, this literature review aims to explore the existing body of research that investigates the application of AI in the field of forensic genetics. Scopus and Web of Science were searched: after an accurate evaluation, 12 articles were included in the present systematic review. The application of AI in the field of forensic genetics has predominantly focused on two aspects. Firstly, several studies have investigated the use of AI in haplogroup analysis to enhance and expedite the classification process of DNA samples. Secondly, other research groups have utilized AI to analyze short tandem repeat (STR) profiles, thereby minimizing the risk of misinterpretation. While AI has proven to be highly useful in forensic genetics, further improvements are needed before using these applications in real cases. The main challenge lies in the communication gap between forensic experts: as AI continues to advance, the collaboration between forensic sciences and AI presents immense potential for transforming investigative practices, enabling quicker and more precise case resolutions.

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“…To simulate single-source degraded samples, two randomly extracted DNA samples were diluted to a concentration of 5 ng/μL and treated with DNase I (Thermo Fisher Scientific, Waltham, MC, USA), respectively [ 38 ]. Subsequently, 45 μL of intact DNA (5 ng/μL) was mixed with 3.75 μL of 10× MgCl 2 buffer (Thermo Fisher Scientific, Waltham, MC, USA).…”

Section: Methodsmentioning

confidence: 99%

An MPS-Based 50plex Microhaplotype Assay for Forensic DNA Analysis

Zhang

Xue

Tan

et al. 2023

Genes

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Microhaplotypes (MHs) are widely accepted as powerful markers in forensic studies. They have the advantage of both short tandem repeats (STRs) and single nucleotide polymorphisms (SNPs), with no stutter and amplification bias, short fragments and amplicons, low mutation and recombination rates, and high polymorphisms. In this study, we constructed a panel of 50 MHs that are distributed on 21 chromosomes and analyzed them using the Multiseq multiple polymerase chain reaction (multi-PCR) targeted capture sequencing protocol based on the massively parallel sequencing (MPS) platform. The sizes of markers and amplicons ranged between 11–81 bp and 123–198 bp, respectively. The sensitivity was 0.25 ng, and the calling results were consistent with Sanger sequencing and the Integrative Genomics Viewer (IGV). It showed measurable polymorphism among sequenced 137 Southwest Chinese Han individuals. No significant deviations in the Hardy–Weinberg equilibrium (HWE) and linkage disequilibrium (LD) were found at all MHs after Bonferroni correction. Furthermore, the specificity was 1:40 for simulated two-person mixtures, and the detection rates of highly degraded single samples and mixtures were 100% and 93–100%, respectively. Moreover, animal DNA testing was incomplete and low depth. Overall, our MPS-based 50-plex MH panel is a powerful forensic tool that provides a strong supplement and enhancement for some existing panels.

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Systematic evaluation of STRmix™ performance on degraded DNA profile data

Cited by 9 publications

References 18 publications

A Review of Probabilistic Genotyping Systems: EuroForMix, DNAStatistX and STRmix™

A Review of Probabilistic Genotyping Systems: EuroForMix, DNAStatistX and STRmix™

Artificial Intelligence and Forensic Genetics: Current Applications and Future Perspectives

An MPS-Based 50plex Microhaplotype Assay for Forensic DNA Analysis

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