Family of skewed distributions associated with the gene expression and proteome evolution
Family of skewed distributions associated with the gene expression and proteome evolution
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DOI：
10.1016/s0165-1684(02)00481-4
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发表时间：
2003-04-01
影响因子：
4.4
通讯作者：
Kuznetsov, VA
中科院分区：
文献类型：
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作者：
Kuznetsov, VA
我们研究了各种基因组相关现象中出现的统计分布，包括真核细胞转录组中转录本拷贝数的分布和物种蛋白质组中含有蛋白质结构域的蛋白质数量的分布。我们发现，所有研究的数据集的经验分布很好地拟合了一个家庭的帕累托分布函数，其形状取决于在一个可预测的方式对样本量。这样的分布是作为马尔可夫随机过程中的极限分布生成的，其中出生和死亡强度是事件的线性函数。我们还提出了一个新的模型，逐步进化的人口在系统中的不同组件和它们的链接的数量的增加，我们研究这些链接的概率分布的演变。估计该模型的两个未知参数使我们能够描述70个完全测序的基因组生物的蛋白质组中不同蛋白质结构域集合的数量和包含给定蛋白质结构域的蛋白质的数量的渐进进化。该模型还预测了蛋白质组复杂性演化的趋势。出版社：Elsevier Science B.V.
We study statistical distributions appearing in various genome-related phenomena, including the distribution of the transcript copy number in the transcriptome of eukaryotic cells and the distribution of the number of proteins containing a protein domain in proteomes of species. We found that the empirical distributions for all studied data sets are well fitted by a family of Pareto-like distribution functions whose shape depends in a predictable manner on the sample size. Such distributions are generated as limiting distributions in a Markov random process where the birth and death intensities are linear functions of events. We also propose a novel model of progressive evolution of a population in terms of the increase of the numbers of distinct components and their links in the system and we study evolution of the probability distribution of these links. Estimating two unknown parameters of this model allows us to describe the progressive evolution of the number of distinct protein domain sets and the number of proteins containing a given protein domain in the proteomes of 70 fully sequenced genome organisms. This model also predicts trends in proteome complexity evolution. Published by Elsevier Science B.V.