3 research outputs found
A Signal Model for Forensic DNA Mixtures
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
A Signal Model for Forensic DNA Mixtures
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
A Signal Model for Forensic DNA Mixtures
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