Research focus

The intersection of genetic identity, DNA based forensic science and issues pertaining to national security. We leverage molecular biology and bioinformatics to address applied scientific questions relating to (1) the interpretation of low quality, low quantity DNA samples, (2) laboratory and computational (machine learning) approaches to facets of DNA mixture interpretation and (3)DNA based geolocation – the exploitation of biologicals to aid in tagging, tracking locating targets of interest.

Current Students:

  • Haley Crooks
  • Bridget Essing*
  • Jonathan Hogg*
  • Natalie Novotna*
  • Natalia Pedraza
  • Veronica Poole
  • Amber Vandepoele
  • Taylor Zekri

*We would like to congratulate Natalie (M.S. Forensic Science), Jon (M.S. Forensic Science) and Bridget (B.S. Biology, B.S. Forensics) on graduating and thank them for all of their hard work over the past several years, they will be missed! Good luck!

Announcements

  • Congratulations!:
    • Recent Student achievements
      • Haley Crooks – Internship at the San Diego Police Department Crime Lab (2022)
      • Natalia Pedraza – SOURCE grant 2022-2023
      • Amber Vandepoele – 2022 Norma Slepecky Undergraduate Research Prize
      • Bridget Essing (news article) and Amber Vandepoele presented at the 32nd International Symposium on Human Identification (2021)
      • Amber Vandepoele
        • International Symposium on Human Identification (ISHI) Student Ambassador – see the article.
        • Recipient of internship with the Defense Forensic Science Center as a research apprentice – Army Educational Outreach Program (AEOP)
      • Bridget Essing
        • Internship with the Department of Defense
      • Linzy Dineen – Honors grant 2020, 2021 Biology Department award for Outstanding Achievement in Academics and Research
      • Bridget Essing – Honors – SOURCE grant 2020
      • Amber Vandepoele – SOURCE grant 2019-2020
  • Congratulations on the hiring – Recent bioforensics research alumni!
    • Davis Watkins at the Seattle Forensics Laboratory as a Forensic Biologist!
    • Olivia D’Angelo  at the North Carolina State Laboratory – Western Regional Lab as a Forensic Biologist!
  •  Bioforensics data now publicly available through a collaboration with the Rutgers – POWERPLEX FUSION CELL SORTED HALF-REACTION DATA ADDED 06/20/2020.

In the News

 Recent Publications

  • D’Angelo O., Vandepoele A.C.W., Adelman, J. and Marciano, MA. (2022). Assessing non-LUS stutter in DNA sequence data. Forensic Science International: Genetics. 59,102706. https://doi.org/10.1016/j.fsigen.2022.102706.
  • Marciano, M.A. and Crill, J. (2021). Genetics and Biodiversity: Tools to Improve Geolocation and Source Attribution. Research Shorts. National Intelligence Press Publications. November 8, 2021. https://ni-u.edu/wp/wp-content/uploads/2022/01/NIUShort_11082021_DNI202104183.pdf
  • Watkins, D., Myers, D., Xavier, H., and Marciano, M. (2021). Revisiting Single Cell Analysis in Forensic Science. Nature Scientific Reports 11, 7054.10.1038/s41598-021-86271-6. [OPEN ACCESS]
  • Young, B., Marciano, M., Crenshaw, K., Duncan, G., Armogida, L., and McCord, B. (2021) Match Statistics for Sequence-Based Profiles from Forensic PCR-MPS Kits. Electrophoresis 0, 1-10. https://doi.org/10.1002/elps.202000087.
  • Malanio BP, Mehta, P, Kurimsky MT, Marciano, MA. (2020) Quantification of Cellular Material on Fired and Unfired Ammunition. AFTE Journal 52(4), 230-238.
  • Marciano, Michael & Adelman, Jonathan. (2019). Developmental Validation of PACE™: Automated Artifact Identification and Contributor Estimation for use with GlobalFiler™ and PowerPlex® Fusion 6c Generated Data. Forensic Science International: Genetics. 43. 102140. 10.1016/j.fsigen.2019.102140.
  • Adelman, J.D., Zhao A., Eberst, D.S. and Marciano, M.A. (2019). Automated detection and removal of capillary electrophoresis artifacts due to spectral overlap. Electrophoresis, 0, 1-9. https://doi.org/10.1002/elps.201900060
  • Marciano M.A., Williamson V.R. & Adelman J.D. (2018). A Hybrid Approach to Increase the Informedness of CE-based Data Using Locus-Specific Thresholding and Machine Learning. Forensic Science International: Genetics 35, 26-37  https://doi.org/10.1016/j.fsigen.2018.03.017.
  • Williamson, V.R, Laris T.M., Romano R. & Marciano, M.A.  (2018). Enhanced DNA Mixture Deconvolution of Sexual Offense Samples Using the DEPArray™ System.  Forensic Science International: Genetics 34, 265-276). DOI: https://doi.org/10.1016/j.fsigen.2018.03.001
  • Marciano M.A.,Panicker S.X., Liddil G.D., Lindgren D. & Sweder K.S. (2018). Development of a Method to Extract Opium Poppy (Papaver somniferum L.) DNA from Heroin.  Nature Scientific Reports 8, 2590. doi:10.1038/s41598-018-20996-9
  • Marciano, M. A. & Adelman, J. D. (2017). PACE: Probabilistic Assessment for Contributor Estimation—A machine learning-based assessment of the number of contributors in DNA mixtures. Forensic Science International: Genetics, 27, 82-91. DOI: http://dx.doi.org/10.1016/j.fsigen.2016.11.006 

A Hybrid Machine Learning Approach for DNA Mixture Interpretation

The Bioforensics group has developed a DNA mixture deconvolution software to explore the use of machine learning as a potential method for enhanced deconvolution. The software related to this initial proof of concept study can be downloaded here. Please contact us for any questions or support needs.

This project was supported by Award No. 2014-DN-BX-K029 awarded 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 publication/program/exhibition are those of the author(s) and do not necessarily reflect those of the Department of Justice.

Patent Pending: SYSTEM AND METHOD FOR INTER-SPECIES DNA MIXTURE INTERPRETATION 20160162636

Inventors: Michael A. Marciano and Jonathan D Adelman

Abstract: Methods and systems for characterizing two or more nucleic acids in a sample. The method can include the steps of providing a hybrid machine learning approach that enables rapid and automated deconvolution of DNA mixtures of multiple contributors. The input is analyzed by an expert system which is implemented in the form of a rule set. The rule set establishes requirements based on expectations on the biology and methods used. The methods and systems also include a machine learning algorithm that is either incorporated into the expert system, or utilizes the output of the expert system for analysis. The machine learning algorithm can be any of a variety of different algorithms or combinations of algorithms used to perform classification in a complex data environment.