Probabilistic Genotyping DNA Analytical Software

The vendor required to provide probabilistic genotyping DNA analytical software for utilizing advanced instrumentation and data analysis software, analysts can interpret samples and establish scientific connections between individuals and criminal activities.
- Software capable of:
•    Handling a wide range of DNA mixtures, from simple to highly complex, and degraded samples; 
•    Providing comprehensive statistical analysis, including likelihood ratios, and exclusion and inclusion data; 
•    Performing developmental and internal validations; 
•    Providing comprehensive onsite training for 14 agency personnel; and 
•    The ability to work with multiple contributors in mixtures.
- Mixture interpretation and analysis
1. DNA mixture processing:
•    The software must support the analysis of str (short tandem repeat) DNA mixtures ranging from simple profiles with few contributors to highly complex mixtures involving a minimum of 4 contributors. 
•    The software must incorporate models for artifacts like stutter, allelic drop-in, dropout, amplification efficiency, and degradation to enhance interpretation accuracy. 
•    To properly assess stutter peaks, the software must evaluate every peak as a potential allele, including those that might otherwise be filtered out as stutter. 
•    The software must be able to account for peak heights, mixture proportions, number of contributors, template amount, and multiple replicates. 
•    It should produce statistically robust results that enable forensic analysts to confidently differentiate individual contributors, ensuring reliable mixture interpretation for casework and legal proceedings.
2. Concurrent processing:
•    The software must support simultaneous processing of multiple DNA samples. 
•    This capability is critical to improve laboratory efficiency and manage high workloads effectively, ensuring timely analysis and reporting. 
•    The system shall maintain accuracy and performance while processing multiple complex samples in parallel, minimizing turnaround times and preventing workflow bottlenecks.
3. Sample processing:
•    The software must provide efficient processing speeds to support the laboratory’s operational demands. 
•    Simple DNA mixtures should be processed within minutes to enable rapid casework turnaround. 
•    More complex mixtures involving multiple contributors and challenging sample conditions, processing times should generally not exceed 6 hours. 
•    These performance benchmarks ensure that the software balances thorough analysis with timely results, supporting effective case management without compromising accuracy or reliability.
- Statistical features
1. Likelihood ratio (LR) and statistical analysis:
•    The software must be capable of generating statistically robust LRs to support DNA mixture interpretation. 
•    It should calculate LRS based on clearly defined propositions and provide point estimates along with appropriate confidence measures or intervals. 
•    The system must incorporate relevant variables, including the number of contributors, peak heights, stutter, drop-in, dropout, and mixture proportions.
2. Uncertainty and relatedness handling:
•    The software must address uncertainty in DNA interpretation by incorporating statistical techniques such as highest posterior density (HPD) to account for variability in allele frequencies and weighting. 
•    It should provide both a point estimate of the LR and the lower bound of the HPD interval to support transparent and reliable reporting. 
•    The system must apply a conservative population genetic model, such as the balding-Nicholls model, consistent with recommendation of the NRCII report, to account for population substructure. 
•    It should also incorporate appropriate theta values and, if enabled, account for relatedness within the population structure. 
•    The software should support the use of a unified statistic, treating alternate contributors as potentially related or unrelated individuals, to enhance interpretation accuracy.
3. Kinship and relatedness: 
•    The software must account for potential relatedness among contributors by adjusting LR calculations accordingly. 
•    This includes the ability to model kinship scenarios, where a contributor may be a close relative of a person of interest, and to apply conservative population genetic assumptions to ensure the accuracy and reliability of the analysis. 
•    The resulting statistical output should be transparent, reproducible, and suitable for presentation in court.
4. Population database and comparison: 
•    The software should allow comparison of results with relevant population databases such as institute for population genetic analysis.

- Contract Period/Term: 3 years
- Pre-Submittal Conference Date: December 05, 2025
- Questions/Inquires Deadline: December 10, 2025