Wojtowicz Andrzej J, Miller Craig R, Joyce Paul
Stat Appl Genet Mol Biol. 2015 Feb;14(1):65-81. doi: 10.1515/sagmb-2014-0030.
Experimental evolution is an important research method that allows for the study of evolutionary processes occurring in microorganisms. Here we present a novel approach to experimental evolution that is based on application of next generation sequencing. Under this approach population level sequencing is applied to an evolving population in which multiple first-step beneficial mutations occur concurrently. As a result, frequencies of multiple beneficial mutations are observed in each replicate of an experiment. For this new type of data we develop methods of statistical inference. In particular, we propose a method for imputing selection coefficients of first-step beneficial mutations. The imputed selection coefficient are then used for testing the distribution of first-step beneficial mutations and for estimation of mean selection coefficient. In the case when selection coefficients are uniformly distributed, collected data may also be used to estimate the total number of available first-step beneficial mutations.
实验进化是一种重要的研究方法，可用于研究微生物中发生的进化过程。在此，我们提出一种基于下一代测序应用的新型实验进化方法。在这种方法中，群体水平测序应用于一个正在进化的群体，其中多个第一步有益突变同时发生。结果，在实验的每个重复中都观察到多个有益突变的频率。对于这种新型数据，我们开发了统计推断方法。特别是，我们提出了一种估算第一步有益突变选择系数的方法。然后，将估算出的选择系数用于测试第一步有益突变的分布以及估算平均选择系数。在选择系数均匀分布的情况下，收集到的数据也可用于估算可用的第一步有益突变的总数。
