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This module originally occurred on May 22, 2019
Duration: 4 hours


Module 3: Probabilistic Genotyping Software and Output

Over a dozen probabilistic genotyping software programs are commercially available or accessible as freeware. This module presents an instructive overview by software developers of three programs that are supported by published developmental validation. Instructors will impart the inner workings of the software with the goal of facilitating understanding of the computing approach and other features, parameter settings, mathematical and population genetic components of the likelihood ratio calculation, and the means of addressing allelic variance, artifacts, peak sharing, allele dropout and drop-in, DNA degradation, inhibition and other profile features. The programs will be demonstrated, and output files will be explained along with any diagnostic tools to aid in the analysis of output data.

Detailed Learning Objectives

  1. Describe the features and capabilities of different probabilistic genotyping software programs and their data input requirements.
  2. Evaluate output data in the context of a case scenario.
  3. Articulate difference in the programs that impact the likelihood ratio.
  4. Relay information about the functions, merits, and limitations of each program


  • Dr. Michael Coble | Center for Human Identification at the University of North Texas Health Science Center in Fort Worth, Texas
  • Dr. Mark Perlin | Chief Scientific and Executive Officer at Cybergenetics
  • Dr. Jo-Anne Bright | Senior Science Leader at the Institute of Environmental Science and Research Limited (ESR) New Zealand
  • Dr. Peter Gill | University of Oslo, Oslo, Norway

Recommended Reading

  • Alladio, E., Omedei, M., Cisana, S., D'Amico, G., Caneparo, D., Vincenti, M, and Garofano ,P. DNA mixtures interpretation - A proof-of-concept multi-software comparison highlighting different probabilistic methods' performances on challenging samples. Forensic Science International: Genetics, 2018. 37:143-150.
  • Bille, T.W., Weitz, S.M., Coble, M.D., Buckleton, J. and Bright, J.-A. Comparison of the performance of different models for the interpretation of low level mixed DNA profiles. Electrophoresis, 2014. 35:3125–3133.
  • You, Y. and Balding, D.J. A comparison of software for the evaluation of complex DNA profiles. Forensic Science International: Genetics, 2019. 40: 114-119.


  • Website:
  • An introduction to Euroformix at:
  • Resources
    • Cowell, R.G., et al., Analysis of forensic DNA mixtures with artifacts. Journal of the Royal Statistical Society, series C, 2015. 64:1-48.
    • Gill, P., Ø. Bleka, and T. Egeland, Does an English appeal court ruling increase the risks of miscarriages of justice when complex DNA profiles are searched against the national DNA database? Forensic Science International: Genetics, 2014. 13:167-175.
    • Bleka, O., et al., A comparative study of qualitative and quantitative models used to interpret complex STR DNA profiles. Forensic Sci Int Genet, 2016. 25:85-96.
    • Bleka, Ø., et al., dnamatch2: An open source software to carry out large scale database searches of mixtures using qualitative and quantitative models. Forensic Science International: Genetics Supplement Series, 2017. 6:e404-e406.
    • Bleka, Ø., et al., Open source software EuroForMix can be used to analyze complex SNP mixtures. Forensic Science International: Genetics, 2017. 31:105-110.


  • Website:
  • STRmix overview video at:
  • Resources
    • Bright, J.-A., Taylor, D.A., McGovern, C.E., Cooper, S.J., Russell, L.J., Abarno D.V. and Buckleton, J.S. Developmental validation of STRmix™, expert software for the interpretation of forensic DNA profiles. Forensic Science International: Genetics, 2016. 23:226-239.
    • Moretti, T.R., Just, R.S., Kehl, S.C, Willis, L.E. Buckleton, J.S., Bright, J.-A. and Taylor, D.A. Internal validation of STRmix™ for the interpretation of single source and mixed DNA profiles. Forensic Science International: Genetics, 2017. 29:126-144.
    • Buckleton J.S., Bright, J.-A., Gittelson, S., Moretti, T.R., Onorato, A.J., Bieber, F.R., et al. The Probabilistic Genotyping Software STRmix: Utility and Evidence for its Validity. Journal of the Forensic Sciences, 2019. 64(2): 393-405.
    • Taylor, D.A. Using continuous DNA interpretation methods to revisit likelihood ratio behavior. Forensic Science International: Genetics, 2014. 11: 144-153.
    • Bright, J.-A. et al. Internal validation of STRmix; A multi-laboratory response to PCAST. Forensic Science International: Genetics, 2018. 34:11-24.


  • Website:
  • TrueAllele® process overview at:
  • Resources
    • Greenspoon, S.A., Schiermeier-Wood, L. and Jenkins, B.C. Establishing the Limits of TrueAllele® Casework: A Validation Study. J Forensic Sci. 2015. 60:1263-1276.
    • Perlin, M.W., Belrose, J.L. and Duceman, B.W. New York State TrueAllele ® casework validation study. J Forensic Sci. 2013. 58:1458-1466.
    • Perlin, M.W., Dormer, K., Hornyak, J., Schiermeier-Wood, L. and Greenspoon, S. TrueAllele® casework on Virginia DNA mixture evidence: computer and manual interpretation in 72 reported criminal cases. PLoS One. 2014. 9:e92837.
    • Perlin, M.W., Hornyak, J.M., Sugimoto, G., Miller, K.W. TrueAllele® Genotype identification on DNA mixtures containing up to five unknown contributors. J Forensic Sci. 2015. 60:857-68.
    • Perlin, M.W., Legler, M.M., Spencer, C.E., Smith, J.L., Allan, W.P., Belrose, J.L. and Duceman, B.W. Validating TrueAllele® DNA mixture interpretation. J Forensic Sci. 2011. 56:1430-1447.
    • Perlin, M.W. and Sinelnikov, A. An information gap in DNA evidence interpretation. PLoS One, 2009. 4:e8327.

Funding for this Forensic Technology Center of Excellence webinar has been provided by the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice.

The opinions, findings, and conclusions or recommendations expressed in this webinar are those of the presenter(s) and do not necessarily reflect those of the U.S. Department of Justice.

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