Forensic laboratories frequently update workflows. However, studies conducted to validate these updates remain a demanding and resource-intensive process. Currently, the forensic community lacks a comprehensive tool to navigate the challenges of designing and interpreting validation experiments. As a result, practitioners often rely on conventional experimental designs and interpret data using traditional methods or costly proprietary services. To address this gap, we have launched the initial release of MixMaSTR: a standalone, open-source, and intuitive software tool that automates the experimental design process through the following key modules. First, the Mixture Genotype Combination Generator module automatically generates all unique donor combinations from user-provided single-source profiles, ensuring that every potential mixture genotype scenario is explored. Second, the Mixture Metric Computation module computes key metrics for these combinations—such as allele-sharing levels, homozygosity, rare-allele counts, and 1-bp resolution—allowing practitioners to purposefully select the most relevant samples rather than relying on random selection. Third, the Optimized Experimental Design module employs a statistical algorithm to identify unique mixture ratios that are uniformly distributed across the factor space. Based on user-defined parameters—including mixture metrics, total PCR reaction counts, DNA input, and DNA quality (pristine or degraded)—MixMaSTR automatically generates experimental plans by pairing selected genotypes with these optimized ratios. Finally, the Mixture Preparation module provides pipetting strategies to prepare each mixture at its respective ratio based on user-provided stock concentrations and target DNA masses. This presentation demonstrates how MixMaSTR simplifies validation design, enabling laboratories to comprehensively cover the relevant factor space of samples encountered in routine casework. While this version focuses on design optimization, additional modules for data processing and visualization are currently under development. (Contact: mixmastr@nist.gov)

Authors

• Sarah Riman (National Institute of Standards and Technology, United States of America)
• Hari Iyer (National Institute of Standards and Technology, United States of America)
• Sarra Chouder (National Institute of Standards and Technology, United States of America)
• Peter Vallone (National Institute of Standards and Technology, United States of America)