Complex Principal Component and Correlation Structure of 16 Yeast Genomic Variables
Complex Principal Component and Correlation Structure of 16 Yeast Genomic Variables
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DOI：
10.1093/molbev/msr077
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发表时间：
2011-09-01
影响因子：
10.7
通讯作者：
Frishman, Dmitrij
中科院分区：
文献类型：
--
作者：
Theis, Fabian J.;Latif, Nadia;Frishman, Dmitrij
越来越多的反映基因功能和进化各个方面的特征可以通过实验测量或从 DNA 和蛋白质序列计算得到。对此类定量基因组变量之间的成对相关性的研究以及通过多维方法对其相互关系进行集体分析为分子进化过程提供了重要的见解。在这里，我们对酿酒酵母的 16 个基因组变量进行了主成分分析 (PCA)，这是迄今为止分析的最大数据集。由于许多缺失值和潜在的异常值阻碍了主成分的直接计算，因此我们引入贝叶斯PCA的应用。我们证实了一些先前建立的相关性，例如进化速率与蛋白质表达的关系，并揭示了新的相关性，例如翻译效率、磷酸化密度和蛋白质年龄之间的相关性。虽然第一个主成分主要对比基因组变化和蛋白质表达，但第二个主成分分离与基因存在和表达的蛋白质功能相关的变量。对影响变量相关性的基因进行富集分析揭示了影响基因的类别。例如，虽然核糖体和核转运基因对蛋白质等电点和分子量之间的相关性做出了重要贡献，但蛋白质合成和氨基酸代谢基因有助于导致基因丢失倾向和蛋白质年龄之间缺乏显着相关性。我们提出了新颖的 Quagmire 数据库（定量基因组学资源），它允许探索三种模式生物——大肠杆菌、酿酒酵母和智人——中更多基因组变量之间的关系 (http://webclu.bio.wzw.tum.de:18080/quagmire)。
A quickly growing number of characteristics reflecting various aspects of gene function and evolution can be either measured experimentally or computed from DNA and protein sequences. The study of pairwise correlations between such quantitative genomic variables as well as collective analysis of their interrelations by multidimensional methods have delivered crucial insights into the processes of molecular evolution. Here, we present a principal component analysis (PCA) of 16 genomic variables from Saccharomyces cerevisiae, the largest data set analyzed so far. Because many missing values and potential outliers hinder the direct calculation of principal components, we introduce the application of Bayesian PCA. We confirm some of the previously established correlations, such as evolutionary rate versus protein expression, and reveal new correlations such as those between translational efficiency, phosphorylation density, and protein age. Although the first principal component primarily contrasts genomic change and protein expression, the second component separates variables related to gene existence and expressed protein functions. Enrichment analysis on genes affecting variable correlations unveils classes of influential genes. For example, although ribosomal and nuclear transport genes make important contributions to the correlation between protein isoelectric point and molecular weight, protein synthesis and amino acid metabolism genes help cause the lack of significant correlation between propensity for gene loss and protein age. We present the novel Quagmire database (Quantitative Genomics Resource) which allows exploring relationships between more genomic variables in three model organisms-Escherichia coli, S. cerevisiae, and Homo sapiens (http://webclu.bio.wzw.tum.de:18080/quagmire).