Technological advances such as massively parallel sequencing enable increasing amounts of genetic information to be obtained from increasingly challenging samples. Certainly on low template, degraded and multi-contributor samples, drop-outs will increase in number for many profiles simply by analyzing more loci, making it difficult to probabilistically assess how many drop-outs have occurred and at which loci they might have occurred. Previously we developed a Random Man Not Excluded (RMNE) method that can take into account allelic drop-out while avoiding detailed estimations of the probability that drop-outs have occurred, nor making assumptions about at which loci these drop-outs might have occurred. The number of alleles that have dropped out, does not need to be exactly known. Here we report a generic Python algorithm to calculate the RMNE probabilities for any given number of loci. The number of allowed drop-outs can be set between 0 and twice the number of analyzed loci. The source code has been made available on https://github.com/fvnieuwe/rmne. An online web-based RMNE calculation tool has been made available on http://forensic.ugent.be/rmne. The tool can calculate these RMNE probabilities from a custom list of probabilities of the observed and non-observed alleles from any given number of loci. Using this tool, we explored the effect of allowing allelic drop-outs on the evidential value of random forensic profiles with a varying number of loci. Our results give insight into how the number of allowed drop-outs affects the evidential value of a profile and how drop-out can be managed in the RMNE approach
