National Institute of Justice and Syracuse University
When DNA is recovered from a crime scene, victim, or suspect, it may be a mixture of genetic information from multiple individuals. To successfully interpret a mixture of DNA, analysts must first determine the number of contributors present in the sample. This is a critical but challenging first step towards “deconvolution” of the DNA data. Standard computer programs designed to assist with DNA mixture analysis require analysts to determine the number of contributors beforehand. To do so, users must identify, categorize, and remove irrelevant data called artifacts. However, in cases when the DNA is in limited quantities, degraded, or both—individual contributors are difficult to separate, some even artifacts. Generally, as the number of contributors increases and complexity of the DNA profile increases, requiring greater amounts of user time and experiential judgment to determine the number of contributors. To address these challenges, Dr. Michael Marciano and Jonathan Adelman of Syracuse University developed the Probabilistic Assessment for Contributor Estimation (PACETM) a machine learning method to improve mixture interpretation. PACETM is unique in the field of forensic DNA analyses and is leading the way toward a future filled with myriad new tools and interpretation methods that better utilize complex, challenging samples. The development of PACETM (patent pending), which is exclusively licensed to NicheVision, was supported through the National Institute of Justice (NIJ) forensic science R&D program with the goal of eventually seeing operational use in casework.
“This is something that every DNA crime lab worldwide should investigate. We now have a tool that with the press of one button does the entire suite of services and provides you with the end result. Having the simple decisions made for you enables focus on addressing the issues of most importance.”
- Vic Meles | Marketing & Business Director, NicheVision
- PACE: Probabilistic Assessment for Contributor Estimation— A machine learning-based assessment of the number of contributors in DNA mixtures. Forensic Science International: Genetics, 2018.
- A hybrid approach to increase the informedness of CE-based data using locus-specific thresholding and machine learning, Forensic Science International: Genetics, 2018.
Funding for this Forensic Technology Center of Excellence success story was provided by the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice.
The opinions, findings, and conclusions or recommendations expressed in this success story are those of the author(s) and do not necessarily reflect those of the U.S. Department of Justice.