A signal model for forensic DNA mixtures

Ullrich J. Mönich, Catherine Grgicak, Viveck Cadambe, Jason Yonglin Wu, Genevieve Wellner, Ken Duffy, Muriel Medard

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Scopus citations

    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.

    Original languageEnglish (US)
    Title of host publicationConference Record of the 48th Asilomar Conference on Signals, Systems and Computers
    EditorsMichael B. Matthews
    PublisherIEEE Computer Society
    Pages429-433
    Number of pages5
    ISBN (Electronic)9781479982974
    DOIs
    StatePublished - Apr 24 2015
    Event48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States
    Duration: Nov 2 2014Nov 5 2014

    Publication series

    NameConference Record - Asilomar Conference on Signals, Systems and Computers
    Volume2015-April
    ISSN (Print)1058-6393

    Other

    Other48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
    Country/TerritoryUnited States
    CityPacific Grove
    Period11/2/1411/5/14

    All Science Journal Classification (ASJC) codes

    • Signal Processing
    • Computer Networks and Communications

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