FINAL by: OSAC Program Office; Version 2, January 13, 2020 1 

OSAC RESEARCH NEEDS ASSESSMENT FORM 

Title of research need: Software solutions for low template and high order DNA mixture interpretation

in sequence and fragment‐based methods

Describe

the need:

DNA mixture interpretation remains one of the most significant challenges in forensic DNA

analysis. Probabilistic genotyping, probabilistic number of contributors, and artifact

identification systems have been developed. However, it is incumbent upon the forensic

DNA community to continue the pursuit of high quality and reliable software as

chemistries, methods, targets, and platforms may change. This need addresses both the

evolution of current techniques and the innovation of new techniques. The focus of this

research and development need includes the development of new, and comparison of

current, computational and statistical approaches used in the analysis of standard nuclear

STR systems for both fragment and sequence analysis, Y‐STRs, phenotyping, ancestry and

mitochondrial DNA, SNPs, and microhaplotypes.  

Keyword(s): probabilistic genotyping, DNA mixture, number of contributors, artifacts,  NGS, MPS

Submitting subcommittee(s): Human Biology Date Approved: 10/05/2021

(If SAC review identifies additional subcommittees, add them to the box above.)

Background Information:

1. Does this research need address a gap(s) in a current or planned standard? (ex.: Field identification system 

for on scene opioid detection and confirmation)

Yes. 

2. Are you aware of any ongoing research that may address this research need that has not yet been published 

(e.g., research presented in conference proceedings, studies that you or a colleague have participated in but 

have yet to be published)?

Yes. 

3. Key bibliographic references relating to this research need: 

1) Alladio, E. et al. DNA mixtures interpretation – A proof-of-concept multi-software comparison highlighting 

different probabilistic methods’ performances on challenging samples. Forensic Sci. Int. Genet. 37, 143–150 

(2018). 

2) Bright, J.-A. et al. Developmental validation of STRmix TM , expert software for the interpretation of forensic 

DNA profiles. (2016). doi:10.1016/j.fsigen.2016.05.007 

3) Moretti, T. R. et al. Internal validation of STRmixTM for the interpretation of single source and mixed DNA 

profiles. Forensic Sci. Int. Genet. (2017). doi:10.1016/j.fsigen.2017.04.004 
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4) Bille, T. W., Weitz, S. M., Coble, M. D., Buckleton, J. & Bright, J. A. Comparison of the performance of different 

models for the interpretation of low level mixed DNA profiles. Electrophoresis 35, 3125–3133 (2014). 

5) Inman, K. et al. Lab Retriever: a software tool for calculating likelihood ratios incorporating a probability of 

drop-out for forensic DNA profiles. BMC Bioinformatics 16, 298 (2015). 

6) Bleka, Ø., Storvik, G. & Gill, P. EuroForMix: An open source software based on a continuous model to evaluate 

STR DNA profiles from a mixture of contributors with artefacts. Forensic Sci. Int. Genet. 21, 35–44 (2016). 

7) Marciano, M. A. & Adelman, J. D. PACE: Probabilistic Assessment for Contributor Estimation- A machine 

learning-based assessment of the number of contributors in DNA mixtures. Forensic Sci. Int. Genet. 27, 82–

91 (2017). 

8) Marciano, M. A. & Adelman, J. D. Developmental validation of PACETM: Automated artifact identification and 

contributor estimation for use with GlobalFilerTM and PowerPlex® fusion 6c generated data. Forensic Sci. 

Int. Genet. 43, 102140 (2019). 

9) Alfonse, L. E., Tejada, G., Swaminathan, H., Lun, D. S. & Grgicak, C. M. Inferring the Number of Contributors to 

Complex DNA Mixtures Using Three Methods: Exploring the Limits of Low-Template DNA Interpretation. J. 

Forensic Sci. 62, 308–316 (2017). 

10) Goor, R. M., Forman Neall, L., Hoffman, D. & Sherry, S. T. A Mathematical Approach to the Analysis of 

Multiplex DNA Profiles. Bull. Math. Biol. 73, 1909–1931 (2011). 

11) Buckleton, J. S. et al. Implementation and validation of an improved allele specific stutter filtering method for 

epg interpretation. Forensic Sci. Int. Genet. (2017). doi:10.1016/j.fsigen.2018.03.016 

12)Adelman, J. D., Zhao, A., Eberst, D. S. & Marciano, M. A. Automated detection and removal of capillary 

electrophoresis artifacts due to spectral overlap. Electrophoresis elps.201900060 (2019). 

doi:10.1002/elps.201900060 

13) Swaminathan, H., Grgicak, C. M., Medard, M. & Lun, D. S. NOCIt: A computational method to infer the number 

of contributors to DNA samples analyzed by STR genotyping. Forensic Sci. Int. Genet. 16, 172–180 (2015). 

14) Coble, M. D. et al. DNA Commission of the International Society for Forensic Genetics: Recommendations on 

the validation of software programs performing biostatistical calculations for forensic genetics applications. 

Forensic Sci. Int. Genet. 25, 191–197 (2016). 

15) Gill, P., Kirkham, A. & Curran, J. LoComatioN: A software tool for the analysis of low copy number DNA 

profiles. Forensic Sci. Int. 166, 128–138 (2007). 

16) Gill, P. et al. DNA commission of the International Society of Forensic Genetics: Recommendations on the 

evaluation of STR typing results that may include drop-out and/or drop-in using probabilistic methods. 

Forensic Sci. Int. Genet. 6, 679–688 (2012). 

17) Perlin, M. W. et al. Validating TrueAllele® DNA Mixture Interpretation*,†. J. Forensic Sci. 56, 1430–1447 

(2011). 

18) Bauer, D. W., Butt, N., Hornyak, J. M. & Perlin, M. W. Validating TrueAllele ® Interpretation of DNA Mixtures 

Containing up to Ten Unknown Contributors. J. Forensic Sci. 65, 380–398 (2020). 

19) Perlin, M. W., Dormer, K., Hornyak, J., Schiermeier-Wood, L. & Greenspoon, S. TrueAllele casework on Virginia 

DNA mixture evidence: Computer and manual interpretation in 72 reported criminal cases. PLoS One 9, 

(2014). 

20) Slooten, K. & Meester, R. Probabilistic strategies for familial DNA searching. J. R. Stat. Soc. Ser. C Appl. Stat. 63, 

361–384 (2014). 

21) Gill, P. & Haned, H. A new methodological framework to interpret complex DNA profiles using likelihood 

ratios. Forensic Sci. Int. Genet. 7, 251–263 (2013). 

22) Bleka, Ø., Dørum, G., Haned, H. & Gill, P. Database extraction strategies for low-template evidence. Forensic 

Sci. Int. Genet. 9, 134–141 (2014). 
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23) Taylor, D., Bright, J. A. & Buckleton, J. The interpretation of single source and mixed DNA profiles. Forensic 

Sci. Int. Genet. 7, 516–528 (2013). 

24) Cowell, R. G., Lauritzen, S. L. & Mortera, J. MAIES: A Tool for DNA Mixture Analysis. 

25) Cowell, R. G., Lauritzen, S. L. & Mortera, J. Probabilistic expert systems for handling artifacts in complex DNA 

mixtures. Forensic Sci. Int. Genet. 5, 202–209 (2011). 

26) Swaminathan, H., Garg, A., Grgicak, C. M., Medard, M. & Lun, D. S. CEESIt: A computational tool for the 

interpretation of STR mixtures. Forensic Sci. Int. Genet. 22, 149–160 (2016). 

27)Holland, M. M., Pack, E. D. & Mcelhoe, J. A. Evaluation of GeneMarker 1 HTS for improved alignment of mtDNA 

MPS data, haplotype determination, and heteroplasmy assessment. (2017). doi:10.1016/j.fsigen.2017.01.016 

28)Adamowicz, M. et al. Validation of MaSTRTM software_ Extensive study of fully-continuous probabilistic 

mixture analysis using PowerPlex®Fusion 2 – 5 contributor mixtures. Forensic Sci. Int. Genet. Suppl. Ser. 7, 

1875–1768 (2020). 

29)Adamowicz, M. Assessing the Performance of the SoftGenetics® MaSTRTM Probabilistic Genotyping 

Software. 

30)ArmedXpertTM - NicheVision Forensics, LLC. 

Available at: https://nichevision.com/armedxpert/. (Accessed: 25th July 2021) 

31)Hansson, O. & Gill, P. Evaluation of GeneMapper 1 ID-X Mixture Analysis tool. 

doi:10.1016/j.fsigss.2011.08.005 

4. Review the annual operational/research needs published by the National Institute of Justice (NIJ) at 

https://nij.ojp.gov/topics/articles/forensic-science-research-and-development-technology-working-groupoperational#latest? Is your research need identified by NIJ?

Yes, “Improved methods for identifying the number of contributors and mixture interpretation algorithms

for all markers (STRs, sequence‐based STRs, Y‐STRs, mitochondrial, microhaplotypes, SNPs) to include

statistical considerations for combining marker types” and “Probabilistic haplotyping tool for mixture

interpretation of lineage markers (Y‐STRs, mitochondrial) and/or methods by which to statistically evaluate

mixture profiles (Y‐STRs, mitochondrial)”.

5. In what ways would the research results improve current laboratory capabilities? 

The development of new software tools will meet the needs of an evolving technological climate in forensics, 

where new chemistries, instrumentation and targets are being evaluated and implemented. New methods may 

be developed that improve upon existing software solutions, thus enabling higher confidence in interpretation 

and conclusions. Finally, assessments of currently used software solutions will enable laboratories to make more 

informed decisions regarding implementation. 

6. In what ways would the research results improve understanding of the scientific basis for the 

subcommittee(s)? 

The development and assessment of software methods for forensic DNA interpretation lead to a better 

understanding of the underlying biology, the process related components such as sample collection, DNA 

isolation and purification, amplification and detection. This will also prepare the subcommittee for the 

development of new standards that will address the use of new chemistries, instruments or targets. 
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7. In what ways would the research results improve services to the criminal justice system? 

The chemistries, methods, targets, and platforms used in forensic DNA analysis may change leading to a need to 

adapt or develop new methods for probabilistic methods to assess the resulting profiles. Ultimately this research 

need advocates for the continual development of software that offers quality improvements to the “toolbox” that 

forensic DNA analysts use to aid in judgement and decision making. 

8. Status assessment (I, II, III, or IV): IV

Major gap in 

current 

knowledge 

Minor gap in 

current 

knowledge 

 

 No or limited

current research is 

being conducted I III 

 Existing current 

research is being 

conducted II IV 

This research need has been identified by one or more subcommittees of OSAC and is being provided as an

informational resource to the community.