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The effect of natural selection on the performance of maximum parsimony

机译:自然选择对最大简约表现的影响

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Background Maximum parsimony is one of the most commonly used and extensively studied phylogeny reconstruction methods. While current evaluation methodologies such as computer simulations provide insight into how well maximum parsimony reconstructs phylogenies, they tell us little about how well maximum parsimony performs on taxa drawn from populations of organisms that evolved subject to natural selection in addition to the random factors of drift and mutation. It is clear that natural selection has a significant impact on Among Site Rate Variation (ASRV) and the rate of accepted substitutions; that is, accepted mutations do not occur with uniform probability along the genome and some substitutions are more likely to occur than other substitutions. However, little is know about how ASRV and non-uniform character substitutions impact the performance of reconstruction methods such as maximum parsimony. To gain insight into these issues, we study how well maximum parsimony performs with data generated by Avida, a digital life platform where populations of digital organisms evolve subject to natural selective pressures. Results We first identify conditions where natural selection does affect maximum parsimony's reconstruction accuracy. In general, as we increase the probability that a significant adaptation will occur in an intermediate ancestor, the performance of maximum parsimony improves. In fact, maximum parsimony can correctly reconstruct small 4 taxa trees on data that have received surprisingly many mutations if the intermediate ancestor has received a significant adaptation. We demonstrate that this improved performance of maximum parsimony is attributable more to ASRV than to non-uniform character substitutions. Conclusion Maximum parsimony, as well as most other phylogeny reconstruction methods, may perform significantly better on actual biological data than is currently suggested by computer simulation studies because of natural selection. This is largely due to specific sites becoming fixed in the genome that perform functions associated with an improved fitness.
机译:背景技术最大简约性是最常用和研究最广泛的系统发育重建方法之一。虽然当前的评估方法(例如计算机模拟)提供了有关最大简约如何重建系统发育的见解,但除了漂移和随机因素之外,他们几乎没有告诉我们最大简约对从自然选择进化的生物种群中提取的分类单元的表现如何。突变。显然,自然选择对站点间变异率(ASRV)和可接受的替代率具有重大影响;也就是说,可接受的突变不会沿着基因组以统一的概率发生,并且某些替代比其他替代更有可能发生。但是,对于ASRV和非均匀字符替换如何影响诸如最大简约性之类的重建方法的性能知之甚少。为了深入了解这些问题,我们研究了由数字生活平台Avida生成的数据的最大简约表现如何,数字生活平台在该平台上数字生物种群在自然选择压力的作用下进化。结果我们首先确定自然选择确实会影响最大简约性重建精度的条件。通常,随着我们增加在中间祖先中发生重大适应的可能性,最大简约性的性能会提高。实际上,如果中间祖先获得了显着的适应性,则最大简约性可以正确地重建4个分类单元树,这些数据已经获得了令人惊讶的许多突变。我们证明,最大简约性的这种改进的性能更多地归因于ASRV,而不是不均匀的字符替换。结论由于自然选择,最大简约度以及大多数其他系统发育重建方法在实际生物学数据上的表现可能要比计算机模拟研究目前建议的明显好。这主要是由于特定位点在基因组中变得固定,这些位点执行了与适应性提高相关的功能。

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