STRmix™

With an ever growing range of functions underpinned by world leading statistical and biological modelling, STRmix™ enables forensic biologists to:

• Resolve mixed DNA profiles without reference to known contributors.

• Enter a contributor number range when performing a deconvolution.

• Assign an LR varying the number of contributors under the prosecution and defence propositions.

• Undertake quality checks for data.

• Set the number of major contributors to a mixed DNA profile you are interested in and obtain an LR only for these.

• Model any type of stutter observed within your STR profiling kit, now including improved dropout modelling allowing analysis of DNA profiles with a low, or even no analytical threshold.

• Compare reference DNA profiles to single source and mixed DNA profiles and provide a statistical weighting.

• Interpret DNA profiling data generated by any autosomal STR profiling kit, now including Amelogenin in the interpretation.

• Interpret DNA profiles from a range of starting template DNA concentrations.

• Use laboratory-specific settings to perform calculations suited exactly to that laboratory’s results.

• Set up multiple report templates and generate more than one report at the end of each calculation.

• Visualise a representation of the evidence input and ignore any peak during setup.

• Review the deconvolution and explore the accepted genotypes, their weights, and how a person of interest aligns.

• Search a deconvoluted DNA profile directly against a database without the need to interpret a single source component.

• Collate your deconvolution and likelihood ratio data using the in built tools.

• Calculate multiple LRs from multiple reference inputs to a previously run deconvolution (LR Batch tool).

• Perform a large number of in-silico specificity tests on a profile-by-profile basis (Hd True Tester tool).

• Batch multiple deconvolutions or other STRmix™ functions (such as Interpretation, LR from Previous, and Database Search) in a queue, allowing the user to run multiple deconvolutions and calculate LRs sequentially.

• Instantly set up interpretations (including replicate inputs) with flexible likelihood ratio propositions for multiple profiles using Batch Maker.

• Combine multiple amplifications of the same DNA extract into one interpretation, even when generated with different multiplexes.

• Accommodate data generated by protocols demonstrating increased stochastic variation and non-zero allelic drop-in rates, for example elevated PCR cycle number and enhanced CE injection methods.

• Include related individuals as alternate propositions in the LR.

• Carry out familial searches against a database, searching for close relatives of contributors to mixed DNA profiles.

• Optionally utilise peak labelling probabilities in STRmix™ deconvolutions (An advanced feature for FaSTR™ DNA users).

• Generate fully configurable (and if required, retrospective) reports including a CODIS report.

• Password protect default settings and kit settings.

STRmix™ In Use

STRmix™ has been used for routine casework interpretation at PHF Science and FSSA since August 2012 and is now the Australasian standard for DNA interpretation.

In one case, a mixed DNA profile was obtained from a semen stain on a carpet at the scene of an alleged sexual assault involving two male offenders. DNA present in approximately equal proportions from two individuals was detected (see EPG below). This profile was unsuitable for database searching using traditional DNA interpretation methods. Using STRmix™, the profile was interpreted assuming the profile originated from two contributors and compared against a database with over 145,000 profiles. The crime profile matched two individuals.

This case highlights how STRmix™'s database search function can provide investigative information. It can also be used to validate complex mixtures where contamination is suspected.

STRmix™ combines sophisticated biological modelling and standard mathematical processes to interpret a wide range of complex DNA profiles. 

Using well-established statistical methods, the software builds millions of conceptual DNA profiles.  It grades them against the evidential sample, finding the combinations that best explain the profile.  A range of Likelihood Ratio options are provided for subsequent comparisons to reference profiles.

Using a Markov Chain Monte Carlo engine, STRmix™ models any types of allelic and stutter peak heights as well as drop-in and drop out behaviour.  It does this rapidly, accessing evidential information previously out of reach with traditional methods.   

STRmix™ is supported by comprehensive empirical studies with its mathematics readily accessible to DNA analysts, so results are easily explained in court.

Validation

STRmix™ has been extensively validated and is in use for casework interpretation at PHF Science (formerly ESR) and multiple Australian, US, European, Canadian, Asian and UK laboratories (first implemented in August 2012).  STRmix™ has achieved Certificate of Networthiness (CoN) status on the United States Army Network.

Papers describing the biological model, mathematics, performance and validation of STRmix™:

[1]D.A. Taylor, J.-A. Bright, J. S. Buckleton, The interpretation of single source and mixed DNA profiles. Forensic Science International: Genetics. 2013; 7(5): 516-528.

[2]J.-A. Bright, D.A. Taylor, J. M. Curran, J. S. Buckleton, Developing allelic and stutter peak height models for a continuous method of DNA interpretation. Forensic Science International: Genetics. 2013; 7(2): 296-304.

[3]J.-A. Bright, D.A. Taylor, J. Curran, J.S. Buckleton, Degradation of forensic DNA profiles, Australian Journal of Forensic Sciences. 2013 45(4): 445-449.

[4]J.-A. Bright, D.A. Taylor, J. M. Curran, J. S. Buckleton, Searching mixed DNA profiles directly against profile databases. Forensic Science International: Genetics. 2014; 9: 102-110.

[5]D.A. Taylor, Using continuous DNA interpretation methods to revisit likelihood ratio behaviour.Forensic Science International: Genetics. 2014; 11: 144-153.

[6]J.-A. Bright, J.M. Curran and J.S. Buckleton, The effect of the uncertainty in the number of contributors to mixed DNA profiles on profile interpretation. Forensic Science International: Genetics. 2014; 12: 208-214.

[7]J.-A. Bright, I.W. Evett, D.A. Taylor, J.M. Curran and J.S. Buckleton, A series of recommended tests when validating probabilistic DNA profile interpretation software. Forensic Science International: Genetics. 2015; 14: 125-131.

[8]J.-A. Bright, K.E. Stevenson, J.M. Curran and J.S. Buckleton, The variability in likelihood ratios due to different mechanisms. Forensic Science International: Genetics. 2015; 14:187-190.

[9]D.A. Taylor, J.-A. Bright and J.S. Buckleton, Considering relatives when assessing the evidential strength of mixed DNA profiles. Forensic Science International: Genetics. 2014; 13: 259-263.

[10]T.W. Bille, S.M. Weitz, M.D. Coble, J.S. Buckleton and J.-A. Bright, Comparison of the performance of different models for the interpretation of low level mixed DNA profiles. Electrophoresis. 2014; 35:3125-33.

[11]D.A. Taylor, J.-A. Bright and J.S. Buckleton, The ‘factor of two’ issue in mixed DNA profiles. Journal of Theoretical Biology. 2014; 363: p. 300-306.

[12]D.A. Taylor and J.S. Buckleton, Do low template DNA profiles have useful quantitative data? Forensic Science International: Genetics. 2015; 16:13-6.

[13]D.A. Taylor, J.S. Buckleton and I. Evett, Testing likelihood ratios produced from complex DNA profiles. Forensic Science International: Genetics. 2015; 16:165-171.

[14]S.J. Cooper, C.E. McGovern, J.-A. Bright, D.A. Taylor and J.S. Buckleton, Investigating a common approach to DNA profile interpretation using probabilistic software. Forensic Science International: Genetics. 2015; 16:121-131.

[15]J.-A. Bright, D.A. Taylor, C.E. McGovern, S.J. Cooper, L.J. Russell, D.V. Abarno and J.S. Buckleton, Developmental validation of STRmix™, expert software for the interpretation of forensic DNA profiles. Forensic Science International: Genetics. 2016; 23:226-239.

[16]D.A. Taylor, J.-A. Bright, C.E. McGovern, C. Hefford, T. Kalafut, J.S. Buckleton, Validating multiplexes for use in conjunction with modern interpretation strategies. Forensic Science International: Genetics. 2016; 20:6-19.

[17]D.A. Taylor, J.S. Buckleton, J.-A. Bright, Factors affecting peak height variability for short tandem repeat data. Forensic Science International: Genetics. 2016; 21:126-133.

[18]T.R. Moretti, R.S. Just, S.C. Kehl, L.E. Willis, J.S. Buckleton, J.-A. Bright, D.A. Taylor, Internal validation of STRmix™ for the interpretation of single source and mixed DNA profiles. Forensic Science International: Genetics. 2017; 29:126-144.

[19]D.A. Taylor, J.-A. Bright, H. Kelly, M.-H. Lin, J.S. Buckleton, A fully continuous system of DNA profile evidence evaluation that can utilise STR profile data produced under different conditions within a single analysis. Forensic Science International: Genetics. 2017; 31:149-154.

[20]D.A. Taylor, J.S. Buckleton, J.-A. Bright, Does the use of probabilistic genotyping change the way we should view sub-threshold data? Australian Journal of Forensic Sciences. 2017; 49(1):78-92.

[21]J.-A. Bright, et al., Internal validation of STRmix; A multi laboratory response to PCAST. Forensic Science International: Genetics. 2018; 34:11-24.

[22]J.S. Buckleton, et al., The Probabilistic Genotyping Software STRmix: Utility and Evidence for its Validity. Journal of Forensic Sciences. 2019; 64(2): 393-405.

[23] D.A. Taylor, J.-A. Bright, J.S. Buckleton, Interpreting forensic DNA profiling evidence without specifying the number of contributors. Forensic Science International: Genetics. 2014; 13:269-80.

[24] D.A.Taylor, J.-A. Bright, J.S. Buckleton, J. Curran, An illustration of the effect of various sources of uncertainty on DNA likelihood ratio calculations. Forensic Science International: Genetics. 2014; 11:56-63.

[25] J.-A. Bright, M. Jones Dukes, S.N. Pugh, I.W. Evett, J.S. Buckleton, Applying calibration to LRs produced by a DNA interpretation software. Australian Journal of Forensic Sciences. 2019; 1-7 https://doi.org/10.1080/00450618.2019.1682668(external link)

[26] J.-A. Bright, et al., STRmix™ collaborative exercise on DNA mixture interpretation. Forensic Science International: Genetics. 2019; 40:1-8.

[27] H. Kelly, et al, A sensitivity analysis to determine the robustness of STRmix™ with respect to laboratory calibration. Forensic Science International: Genetics. 2018; 35:113-122.

[28] S. Noel, J. Noel, D. Granger, J.-F. Lefebvre, D. Seguin, STRmix™ put to the test: 300 000 non-contributor profiles compared to four-contributor DNA mixtures and the impact of replicates. Forensic Science International: Genetics. 2019; 41:24-31.

[29] J.J.R.B. Rodriguez, J.-A. Bright, J.M. Salvador, R.P. Laude, M.C.A. De Ungria, Probabilistic approaches to interpreting two-person DNA mixtures from post-coital specimens. Forensic Science International. 2019; 300: 157-163.

[30] L. Russell, S. Cooper, R. Wivell, Z. Kerr, D. Taylor, J.S. Buckleton, J.-A. Bright, A guide to results and diagnostics within a STRmix™ report. Wiley Interdisciplinary Reviews: Forensic Science. 2019; 1:e1354.

[31] K.R. Dukes, S.P. Myers, Systematic evaluation of STRmix™ performance on degraded DNA profile data. Forensic Science International: Genetics. 2020; 44: Article 102174.

[32] M.-H. Lin, J.A-. Bright, S.N. Pugh, J.S. Buckleton, The interpretation of mixed DNA profiles from a mother, father, and child trio. Forensic Science International: Genetics. 2020; 44: Article 102175.

[33] D. Taylor, J.-A. Bright, L. Scandrett, D. Abarno, S.-I. Lee, R. Wivell, H.Kelly, J. Buckleton, Validation of a top-down DNA profile analysis for database searching using a fully continuous probabilistic genotyping model.Forensic Science International: Genetics. 2021; 2021/05/01/;52:102479.

[34] C. McGovern, K. Cheng, H. Kelly, A. Ciecko, D. Taylor, J.S. Buckleton, et al., Performance of a method for weighting a range in the number of contributors in probabilistic genotyping. Forensic Science International: Genetics. 2020; 48:102352.

[35] J. Buckleton, D. Taylor, J.-A. Bright, T. Hicks, J. Curran, When evaluating DNA evidence within a likelihood ratio framework, should the propositions be exhaustive? Forensic Science International: Genetics. 2021;50:102406.

[36] J.-A.Bright, J. Buckleton, D. Taylor, Probabilistic interpretation of the Amelogenin locus. Forensic Science International: Genetics. 2021;52:102462.

[37] T. Hicks, Z. Kerr, S. Pugh, J.-A. Bright, J. Curran, D. Taylor, et al., Comparing multiple POI to DNA mixtures. Forensic Science International: Genetics. 2021; 52:102481.

[38] C. McGovern, K. Cheng, H. Kelly, A. Ciecko, D. Taylor, J.S. Buckleton, J.-A. Bright, Performance of a method for weighting a range in the number of contributors in probabilistic genotyping. Forensic Science International: Genetics. Volume 48, September 2020;102352.

[39] T. Bille, M.D. Coble, J.-A. Bright, Exploring the advantages of amplifying the entire extract versus splitting the extract and interpreting replicates using a continuous model of interpretation. Australian Journal of Forensic Sciences. 2021; https://doi. org/10.1080/00450618.2021.1882568.

[40] Taylor, J. Buckleton. Combining artificial neural network classification with fully continuous probabilistic genotyping to remove the need for an analytical threshold and electropherogram reading. Forensic Science International: Genetics Volume 62, January 2023, 102787.

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