While DNA evidence has been a staple of criminal investigations since the early 1990s, nothing since then has revolutionized forensic DNA interpretation like probabilistic genotyping (PG) software... and now forensic laboratories can do even more. Since its introduction more than a decade ago, PG software has enabled forensic labs to use far more of the available DNA profile to consider the probability of the evidence under two competing scenarios: typically, one that aligns with the prosecution standpoint (H1) and one that aligns with the defense (H2). As a result, low-level, degraded, or mixed DNA samples that previously would have been discarded as too complex have produced usable, interpretable, and legally admissible DNA evidence in more than half a million criminal cases worldwide.

Work by the same team that produced STRmix™, one of the first and most successful PG software packages, has led to the introduction of two complementary software applications: FaSTR™ DNA, which rapidly analyzes raw DNA data and assigns a number of contributors (NoC) estimate; and DBLR™, which allows forensic laboratories to visualize the value of DNA mixture evidence, carry out superfast database searches, and undertake extensive kinship analysis. When used in in combination with STRmix™, these applications deliver an end-to-end analysis, interpretation, and intelligence solution.

To illustrate this fully integrated workflow, let’s examine a hypothetical case in which swabs are taken from the grip of a handgun found at the scene of a crime for processing by the forensic laboratory.

Who could have donated any DNA detected? What is the profile quality like? Do we want to search all detected contributors or is there some risk to searching all? These are questions that may be addressed through use of the STRmix™ suite of software. Using the FaSTR™ DNA software, the laboratory can analyze data output by capillary electrophoresis (CE) instruments – the primary methodology used for separating and detecting short tandem repeat (STR) alleles in forensic DNA analysis.

FaSTR™ DNA analyzes the data applying dynamic baselining. Using peak detection and sizing algorithms, it may apply a set of laboratory-customized analysis rules to distinguish artefactual peaks from allelic peaks. It can also carry out number of contributors (NoC) estimation and sample-to-sample or sample-to-database comparison checks. Optionally, laboratories may employ an artificial neural network (ANN) to assist with profile interpretation. The exports and reports from FaSTR™ DNA are fully configurable.

In our hypothetical case, the FaSTR™ DNA analysis displays a mixed DNA profile from the swab taken from the grip of the handgun (Figure 1) and the smart decision-tree based NoC estimate indicates the DNA could originate from four individuals.