Christophe Van Neste, Dieter Deforce, Filip Van Nieuwerburgh
Laboratory for Pharmaceutical Biotechnology, Ghent University, Ottergemsesteenweg 460, B-9000 Ghent, Belgium.
Laboratory for Pharmaceutical Biotechnology, Ghent University, Ottergemsesteenweg 460, B-9000 Ghent, Belgium.
Forensic Sci Int Genet. 2015 Nov;19:243-249. doi: 10.1016/j.fsigen.2015.08.001. Epub 2015 Aug 6.
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.
诸如大规模平行测序等技术进步，使得能够从越来越具挑战性的样本中获取越来越多的遗传信息。当然，对于低模板、降解以及多贡献者样本而言，仅仅通过分析更多位点，许多图谱中缺失等位基因的数量就会增加，这使得难以概率性地评估发生了多少缺失等位基因以及它们可能发生在哪些位点。此前我们开发了一种随机未排除个体（RMNE）方法，该方法能够考虑等位基因缺失情况，同时避免对缺失等位基因发生概率进行详细估计，也无需对这些缺失等位基因可能发生在哪些位点做出假设。无需确切知道已缺失等位基因的数量。在此我们报告一种通用的Python算法，用于计算任意给定数量位点的RMNE概率。允许的缺失等位基因数量可设定在0至所分析位点数的两倍之间。源代码已在https://github.com/fvnieuwe/rmne上提供。一个基于网络的RMNE计算工具已在http://forensic.ugent.be/rmne上提供。该工具可根据来自任意给定数量位点的观察到和未观察到的等位基因概率的自定义列表来计算这些RMNE概率。利用这个工具，我们探讨了允许等位基因缺失对具有不同位点数的随机法医图谱证据价值的影响。我们的结果深入了解了允许的缺失等位基因数量如何影响图谱的证据价值，以及在RMNE方法中如何处理缺失等位基因情况。
