论文标题
DNA混合物使用具有多个种群的进化算法，爬山和引导突变的进化算法对卷积
DNA mixture deconvolution using an evolutionary algorithm with multiple populations, hill-climbing, and guided mutation
论文作者
论文摘要
DNA样品在法医遗传学中分析的犯罪病例经常包含来自多个贡献者的DNA。这些是作为DNA样品贡献者的DNA谱的卷积。因此，在一个或多个贡献者未知的情况下，感兴趣的目标是将这些未知概况的分离（通常称为反卷积）。为了获得未知DNA谱的反vl vol，我们引入了多个种群进化算法（MEA）。我们允许MEA的突变操作员利用适合度基于概率模型，并通过使用所观察到的每个元素的观测值和期望值之间的偏差来指导它。该引导的突变算子（GM）的设计使得突变概率越大。此外，GM的时间不均匀，随着迭代次数的增加而降低到指定的下限。我们以不同的混合物比例分析了102个两人的DNA混合物样品。使用两种不同的DNA准备对样品进行定量。套件：（1）Illumina Forenseq面板B（30个样本）和（2）应用生物系统精确ID GlobalFiler NGS STR面板（72个样品）。 DNA混合物被MEA解卷积，并与样品的真实DNA曲线进行比较。我们分析了我们假设的三种情况：（1）主要贡献者的DNA谱尚不清楚，（2）未知的DNA谱是未知的，（3）两个DNA谱均未知。此外，我们通过改变子群的大小，在ForenseQ面板上进行了一系列灵敏度实验，将完全随机的均匀突变操作员与导向操作员进行了比较，并与导向操作员进行了变化的突变衰减率，并允许爬山山丘。
DNA samples crime cases analysed in forensic genetics, frequently contain DNA from multiple contributors. These occur as convolutions of the DNA profiles of the individual contributors to the DNA sample. Thus, in cases where one or more of the contributors were unknown, an objective of interest would be the separation, often called deconvolution, of these unknown profiles. In order to obtain deconvolutions of the unknown DNA profiles, we introduced a multiple population evolutionary algorithm (MEA). We allowed the mutation operator of the MEA to utilise that the fitness is based on a probabilistic model and guide it by using the deviations between the observed and the expected value for every element of the encoded individual. This guided mutation operator (GM) was designed such that the larger the deviation the higher probability of mutation. Furthermore, the GM was inhomogeneous in time, decreasing to a specified lower bound as the number of iterations increased. We analysed 102 two-person DNA mixture samples in varying mixture proportions. The samples were quantified using two different DNA prep. kits: (1) Illumina ForenSeq Panel B (30 samples), and (2) Applied Biosystems Precision ID Globalfiler NGS STR panel (72 samples). The DNA mixtures were deconvoluted by the MEA and compared to the true DNA profiles of the sample. We analysed three scenarios where we assumed: (1) the DNA profile of the major contributor was unknown, (2) DNA profile of the minor was unknown, and (3) both DNA profiles were unknown. Furthermore, we conducted a series of sensitivity experiments on the ForenSeq panel by varying the sub-population size, comparing a completely random homogeneous mutation operator to the guided operator with varying mutation decay rates, and allowing for hill-climbing of the parent population.
