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Estimation of prokaryotic supergenome size and composition from gene frequency distributions

机译：从基因频率分布估算原核超基因组的大小和组成

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Background Because prokaryotic genomes experience a rapid flux of genes, selection may act at a higher level than an individual genome. We explore a quantitative model of the distributed genome whereby groups of genomes evolve by acquiring genes from a fixed reservoir which we denote as supergenome. Previous attempts to understand the nature of the supergenome treated genomes as random, independent collections of genes and assumed that the supergenome consists of a small number of homogeneous sub-reservoirs. Here we explore the consequences of relaxing both assumptions. Results We surveyed several methods for estimating the size and composition of the supergenome. The methods assumed that genomes were either random, independent samples of the supergenome or that they evolved from a common ancestor along a known tree via stochastic sampling from the reservoir. The reservoir was assumed to be either a collection of homogeneous sub-reservoirs or alternatively composed of genes with Gamma distributed gain probabilities. Empirical gene frequencies were used to either compute the likelihood of the data directly or first to reconstruct the history of gene gains and then compute the likelihood of the reconstructed numbers of gains. Conclusions Supergenome size estimates using the empirical gene frequencies directly are not robust with respect to the choice of the model. By contrast, using the gene frequencies and the phylogenetic tree to reconstruct multiple gene gains produces reliable estimates of the supergenome size and indicates that a homogeneous supergenome is more consistent with the data than a supergenome with Gamma distributed gain probabilities.
机译：背景技术由于原核生物基因组经历了快速的基因变化，因此选择可能比单个基因组具有更高的水平。我们探索了分布式基因组的定量模型，通过从固定储库中获取基因来发展基因组群，我们将其称为超基因组。先前了解超基因组性质的尝试将基因组视为随机，独立的基因集合，并假定超基因组由少量同质子贮库组成。在这里，我们探讨放宽这两个假设的后果。结果我们调查了几种估计超基因组大小和组成的方法。这些方法假定基因组是超基因组的随机，独立样本，或者它们是通过从水库中随机取样从共同祖先沿着已知树进化而来的。假定该储层是同质子储层的集合，或者由具有Gamma分布增益概率的基因组成。使用经验基因频率直接计算数据的可能性，或先重建基因增益的历史，然后计算重建的增益数的可能性。结论直接使用经验基因频率估算超基因组大小在选择模型方面并不可靠。相比之下，使用基因频率和系统树重建多个基因增益可产生可靠的超基因组大小估计值，并表明同质超基因组比具有Gamma分布增益概率的超基因组更符合数据。

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