ValiDNA

ValiDNA combines ReSOLVIt, CleanIt, CALLIt, NOCIt and CEESIt capabilities into one package for efficient, comprehensive and cost-effective laboratory optimization and validation based on Systems Thinking

The Process of Optimization and Validation

Recently we described a systematic approach to forensic DNA optimization by coupling simulation and experimental data to evaluate signal to noise resolution in the single copy DNA level in,

  • Peters, K.C., Swaminathan H., Sheehan, J., Duffy K.R., Lun, D.S., (2017), Production of high-fidelity electropherograms results in improved and consistent DNA interpretation: Standardizing the forensic validation process. 2017 Sep 8;31:160-170. doi: 10.1016/j.fsigen.2017.09.005.

By simulating multifarious scenarios and systematically evaluating this signal, we can quickly produce high-fidelity forensic DNA data by optimizing the post-PCR laboratory conditions and signal detection thresholds, if necessary,

Optimizing the signal content contained in the evidentiary data, regardless of platform type, increases the match strength for true contributors, and decreases the probability that the match strength is large for true non-contributors. The resultant match-strength values are consistent between platforms, demonstrating that employing this validation scheme is the first step towards improving reproducibility across forensic and clinical laboratories.

ValiDNA will:

  • compute the signal-to-noise resolution and allele/noise error detection rates for multiple laboratory scenarios through simulation allowing laboratories to quickly and effectively choose optimal laboratory and analysis conditions;
  • automatically filter artifacts such as pull-up, complex pull-up and minus A from electropherogram data;
  • determine the probability of allele drop-in and drop-out for the optimized scenario;
  • determine stutter ratios and their confidence intervals; and
  • confirm that the match statistics obtained from a full continuous interpretation system and optimized signal are consistent with expectation.

Please contact c.grgicak@rutgers.edu for information or to provide suggestions for continued development.