Probabilistic characterisation of baseline noise in STR profiles

Ullrich J. Mönich, Ken Duffy, Muriel Médard, Viveck Cadambe, Lauren E. Alfonse, Catherine Grgicak

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


There are three dominant contributing factors that distort short tandem repeat profile measurements, two of which, stutter and variations in the allelic peak heights, have been described extensively. Here we characterise the remaining component, baseline noise. A probabilistic characterisation of the non-allelic noise peaks is not only inherently useful for statistical inference but is also significant for establishing a detection threshold. We do this by analysing the data from 643 single person profiles for the Identifiler Plus kit and 303 for the PowerPlex 16 HS kit. This investigation reveals that although the dye colour is a significant factor, it is not sufficient to have a per-dye colour description of the noise. Furthermore, we show that at a per-locus basis, out of the Gaussian, log-normal, and gamma distribution classes, baseline noise is best described by log-normal distributions and provide a methodology for setting an analytical threshold based on that deduction. In the PowerPlex 16 HS kit, we observe evidence of significant stutter at two repeat units shorter than the allelic peak, which has implications for the definition of baseline noise and signal interpretation. In general, the DNA input mass has an influence on the noise distribution. Thus, it is advisable to study noise and, consequently, to infer quantities like the analytical threshold from data with a DNA input mass comparable to the DNA input mass of the samples to be analysed.

Original languageEnglish (US)
Pages (from-to)107-122
Number of pages16
JournalForensic Science International: Genetics
StatePublished - Jul 27 2015

All Science Journal Classification (ASJC) codes

  • Pathology and Forensic Medicine
  • Genetics


Dive into the research topics of 'Probabilistic characterisation of baseline noise in STR profiles'. Together they form a unique fingerprint.

Cite this