A signal model for forensic DNA mixtures
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Medard_A signal.pdf
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Author(s)
Monich, Ullrich
Grgicak, Catherine
Cadambe, Viveck
Wellner, Genevieve
Duffy, Ken
Medard, Muriel
Wu, Yonglin
Date Issued
November 2014
Journal
Proceedings of the 2014 48th Asilomar Conference on Signals, Systems and Computers
Citation
Monich, Ullrich J., Catherine Grgicak, Viveck Cadambe, Jason Yonglin Wu, Genevieve Wellner, Ken Duffy, and Muriel Medard. “A Signal Model for Forensic DNA Mixtures.” 2014 48th Asilomar Conference on Signals, Systems and Computers (November 2014).
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Author's final manuscript
Abstract
For forensic purposes, short tandem repeat allele signals are used as DNA fingerprints. The interpretation of signals measured from samples has traditionally been conducted by applying thresholding. More quantitative approaches have recently been developed, but not for the purposes of identifying an appropriate signal model. By analyzing data from 643 single person samples, we develop such a signal model. Three standard classes of two-parameter distributions, one symmetric (normal) and two right-skewed (gamma and log-normal), were investigated for their ability to adequately describe the data. Our analysis suggests that additive noise is well modeled via the log-normal distribution class and that variability in peak heights is well described by the gamma distribution class. This is a crucial step towards the development of principled techniques for mixed sample signal deconvolution.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Research Laboratory of Electronics
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/ACSSC.2014.7094478
