Browsing by Author "D'Amato, María Eugenia"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Ethical publication of research on genetics and genomics of biological material: guidelines and recommendations
    (Elsevier, 2020) D'Amato, María Eugenia; Bodner, Martin; Butler, John M.
    Forensic Science International: Genetics and Forensic Science International: Reports communicate research on a variety of biological materials using genetics and genomic methods. Numerous guidelines have been produced to secure standardization and quality of results of scientific investigations. Yet, no specific guidelines have been produced for the ethical acquisition of such data. These guidelines summarize universally adopted principles for conducting ethical research on biological materials, and provide details of the general procedures for conducting ethical research on materials of human, animal, plant and environmental origin. Finally, the minimal ethics requirements for submission of research material are presented.
  • Thumbnail Image
    Item
    Predicting haplogroups using a versatile machine learning program (PredYMaLe) on a new mutationally balanced 32 Y-STR multiplex (CombYplex): Unlocking the full potential of the human STR mutation rate spectrum to estimate forensic parameters
    (Elsevier, 2020) D'Amato, María Eugenia; Delehelle, Franklin; Bouakaze, Caroline
    We developed a new mutationally well-balanced 32 Y-STR multiplex (CombYplex) together with a machine learning (ML) program PredYMaLe to assess the impact of STR mutability on haplogourp prediction, while respecting forensic community criteria (high DC/HD). We designed CombYplex around two sub-panels M1 and M2 characterized by average and high-mutation STR panels. Using these two sub-panels, we tested how our program PredYmale reacts to mutability when considering basal branches and, moving down, terminal branches. We tested first the discrimination capacity of CombYplex on 996 human samples using various forensic and statistical parameters and showed that its resolution is sufficient to separate haplogroup classes. In parallel, PredYMaLe was designed and used to test whether a ML approach can predict haplogroup classes from Y-STR profiles. Applied to our kit, SVM and Random Forest classifiers perform very well (average 97 %), better than Neural Network (average 91 %) and Bayesian methods (< 90 %).