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Modelling the evolution of protein coding sequences sampled from Measurably Evolving Populations

机译:模拟蛋白质编码序列的演变从可测量的群体中取样的序列

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Models of nucleotide or amino acid sequence evolution that implement homogeneous and stationary Markov processes of substitutions are mathematically convenient but are unlikely to represent the true complexity of evolution. With the large amounts of data that next generation sequencing promises, appropriate models of evolution are important, particularly when data are collected from ancient and sub-fossil remains, where changes in evolutionary parameters are the norm and not the exception. In this paper, we describe a new codon-based model of evolution that applies to Measurably Evolving Populations (MEPs). A MEP is defined as a population from which it is possible to detect a statistically significant accumulation of substitutions when sequences are obtained at different times. The new model of codon evolution permits changes to the substitution process, including changes to the intensity of selection and the proportions of sites undergoing different selective pressures. In our serial model of codon evolution, changes in the selective regime occur simultaneously across all lineages. Different regions of the protein may also evolve under distinct selective patterns. We illustrate the application of the new model to a dataset of HIV-1 seqnences obtained from an infected individual before and after the commencement of antiretroviral therapy.
机译:核苷酸或氨基酸序列演化的模型,实现均匀和静止的替代品的替代性的过程是数学方便的,但不太可能代表进化的真正复杂性。随着下一代测序承诺的大量数据,适当的进化模型很重要,特别是当从古代和子化石中收集数据仍然存在数据时,进化参数的变化是规范而不是异常。在本文中,我们描述了一种新的基于密码子的演化模型,适用于可测量的不断发展的人群(MEP)。 MEP定义为在不同时间获得序列时可以检测到统计上显着的取代积累的群体。新的密码子演进模型允许改变替代过程,包括改变选择的选择强度和所经历不同选择性压力的遗迹的比例。在我们的密码子演化的串行模型中,选择性制度的变化在所有谱系中同时发生。蛋白质的不同区域也可以在不同的选择性模式下发展。我们说明了新模型在抗逆转录病毒治疗开始前后从受感染的个体获得的HIV-1 SEQNENCE的数据集。

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