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Master of Science in Forensic Science Directed Research Projects

Support vector machine modeling (SVM)

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Full-Text Articles in Social and Behavioral Sciences

A Quantifiler Trio-Based Hrm Mixture Screening Assay For The Quantstudio 6 Flex Qpcr Platform, Dayanara A. Torres Jan 2022

A Quantifiler Trio-Based Hrm Mixture Screening Assay For The Quantstudio 6 Flex Qpcr Platform, Dayanara A. Torres

Master of Science in Forensic Science Directed Research Projects

At present, the forensic DNA workflow is not capable of providing information about the contributor status (single source vs. mixtures) of evidentiary samples prior to end-point analysis. This shortcoming can exacerbate the challenges inherent to mixtures and low-template DNA samples. Provided additional sample information earlier in the workflow, protocols could be implemented to mitigate these challenges. High-resolution melt (HRM) curve analysis is a technique used to detect genetic variation in DNA fragments and in the last decade has been evaluated for use in differentiation of samples by genotype and/or contributor status. To this end, a proof-of-concept HRM assay using the …


Expanded Testing Of An Integrated Qpcr Mixture Screening Assay Using High Resolution Melt Curve Analysis And Support Vector Machine Modeling, Andrea L. Williams Jan 2020

Expanded Testing Of An Integrated Qpcr Mixture Screening Assay Using High Resolution Melt Curve Analysis And Support Vector Machine Modeling, Andrea L. Williams

Master of Science in Forensic Science Directed Research Projects

In the conventional forensic DNA workflow, the number of contributors in a sample is unknown until the final step of STR analysis. We propose a high-resolution melt curve (HRM) mixture screening assay, which uses support vector machine (SVM) modeling of melt morphologies of D5S818 and D18S51 amplicons integrated into a common qPCR-based human DNA quantification kit, to differentiate between single-source samples (and their genotypes) and mixtures at an earlier stage in the DNA workflow (quantification). Previously, using data generated from whole melt curves, 87.5% of single-source samples and 100% of 1:1 mixture samples (2 contributors) classified accurately.

In this study, …