The NFI has developed a software expert system, DNAxs, for case data management, within case matching of profiles and complex DNA-profile interpretation using LoCIM inference and computation of consensus and composite profiles. The software links to other software tools such as SmartRank, Bonaparte and FDStools and CODIS. Within the software the DNAStatistX module has the continuous maximum likelihood ratio model from EuroForMix integrated  to calculate the evidential value with probabilistic DNA-statistics effortlessly. The platform aids the DNA-scientists in casework by giving overview and  managing the increasingly complex data interpretation and decision making process. In addition DNAxs has increased consistency and accountability, while reducing errors and interpretation variability.

The free version (2.0) of DNAxs (including DNAStatistX) is available for you. Request a copy through DNAxs@NFI.nl. You will need to sign a licence agreement before the software will be made available to you. For the standalone version of DNAStatistX also contact DNAxs@NFI.nl for a free copy.

For more information on DNAxs read our flyer and the Frequently Asked Questions document.

Workshops announcement

  • Online webinar September 2021. For more information see the flyer and registrate here at our registration page.
  • Announcements of new DNAxs workshops will be done through this website.

LRmix Studio

With the development of  DNAxs, no further development is performed on the semi-continuous model LRmix Studio (with the exception of bug fixes). LRmix Studio is no longer available from lrmixstudio.org, but has been migrated to GitHub from which the software can be downloaded.

The DNAxs software suite: A three-year retrospective study on the development, architecture, testing and implementation in forensic casework

The DNA eXpert System, DNAxs, aids in the data management and efficient, with low risk for error, interpretation and comparison of DNA profiling data. This study describes how the expert system was realized and how it evolved during the first three years after the initial implementation. Insight is given in the software architecture, its modular design enabling to communicate with various software systems and how this system was implemented in a casework setting. The importance of quality aspects are highlighted, such as (automated) software testing at various levels. The code coverage is presented as well as the numbers of software bugs that were discovered. The usefulness of the overall software suite and automated steps in DNA profile interpretation were evaluated based on its usage in forensic DNA at the developing laboratory. Because of automated profile comparisons, cases with larger numbers of profiles can be handled, are less prone to error and are extremely less time consuming. The implementation of DNAStatistX into DNAxs enabled a more efficient workflow. Extensive automated software tests and an audit trail serve as quality aspects for the usage of DNAxs. In times of the pandemic this software was found even more valuable than ever thought as it enabled working from home and it proved robust when used with many simultaneous users. The DNAxs software is regarded future proof and many new features and applications are envisioned.

Multi-laboratory validation of DNAxs and the statistical library DNAStatistX

DNAxs and DNAStatistX were used in a multi-laboratory validation study in which four laboratories participated. I.e. the Netherlands Forensic Institute in the Netherlands, Institute of Legal Medicine in Austria, National Forensic Laboratory in Slovenia, Institute of Legal Medicine (Cologne) in Germany, and Institut National de Police Scientifique (Ecully) in France. The study was partly funded by the European Union’s Internal Security Fund — Police (Proposal Number: 820838, Proposal Acronym: DNAxs2.0).

In this study, the software was modified to read multiple data formats. First, participants performed an exercise to explore all main functionalities of DNAxs and gave feedback on user-friendliness, installation and general performance. Next, every laboratory performed likelihood ratio (LR) calculations using their own dataset and a dataset provided by the organising laboratory (NFI). The organising laboratory performed LR calculations using all datasets. The datasets were generated with different STR typing kits or analysis systems and consisted of samples varying in DNA amounts, mixture ratios, number of contributors and drop-out level. Hypothesis sets had the correct, under- and over-assigned number of contributors and true and false donors as person of interest.

When comparing the results between laboratories, the LRs were foremost within the pre-set range of variation. The few LR results that deviated more had differences for the parameters estimated by the optimizer within DNAStatistX. Some of these were indicated by failed iteration results, others by a failed model validation, since unrealistic hypotheses were included. When these results that do not meet the quality criteria were excluded, as is in accordance with interpretation guidelines, none of the analyses in the different laboratories yielded a different statement in the casework report. Nonetheless, changes in software parameters were sought that minimized differences in outcomes, which made the DNAStatistX module more robust.

Overall, the software was found intuitive, user-friendly and valid for use in multiple laboratories. The dataset of the organising laboratory is provided to aid the implementation of the DNAxs/DNAStatistX software within other laboratories. This test dataset can be downloaded here.


  • C.C.G. Benschop, J. Hoogenboom, P. Hovers, M. Slagter, D. Kruise, R. Parag, K. Steensma, K. Slooten, J.H.A. Nagel, P. Dieltjes, V. van Marion, H. van Paassen, J. de Jong, C. Creeten, T. Sijen, A.L.J. Kneppers. DNAxs/DNAStatistX: Development and validation of a software suite for the data management and probabilistic interpretation of DNA profiles. Forensic Sci. Int. Genet. 42 (2019) 81-89.
  • C.C.G. Benschop, J. van der Linden, J. Hoogenboom, R. Ypma, H. Haned. Automated estimation of the number of contributors in autosomal short tandem repeat profiles using a machine learning approach. Forensic Sci. Int. Genet. 43 (2019) 102150.
  •  C.C.G. Benschop, A. Nijveld, F.E. Duijs, T. Sijen, An assessment of the performance of the probabilistic genotyping software EuroForMix: trends in likelihood ratios and analysis of Type I & II errors, Forensic Sci. Int. Genet. 42 (2019) 31–38.
    Download the PowerPlex Fusion 6C 2p-5p mixtures dataset as used in this study.
  • M. Slagter, D. Kruise, L. van Ommen, J. Hoogenboom, K. Steensma, J. de Jong, P. Hovers, R. Parag, J. van der Linden, A.L.J. Kneppers, C.C.G. Benschop. The DNAxs software suite: A three-year retrospective study on the development, architecture, testing and implementation in forensic casework. Forensic Sci. Int. Reports 3 (2021) 100212.’