Understanding relationship between sequence and functional evolution in yeast proteins
Understanding relationship between sequence and functional evolution in yeast proteins
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DOI：
10.1007/s10709-006-9125-2
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发表时间：
2007-10-01
期刊：
影响因子：
1.5
通讯作者：
Yi, Soojin V.
中科院分区：
文献类型：
--
作者：
Kim, Seong-Ho;Yi, Soojin V.
功能变量和序列进化速率之间的潜在关系通常通过偏相关分析来评估。然而，这一策略受到阻碍的困难进行有意义的统计分析，使用嘈杂的生物数据。最近的一项研究表明，偏相关分析是误导时，数据是嘈杂的，主成分回归分析是一个更好的工具来分析生物数据。在本文中，我们评估这两种统计工具（偏相关和主成分回归）在数据有噪声时的表现。与前面的结论相反，我们发现这两个工具在大多数情况下执行重复操作。此外，当有一个以上的“真实”自变量时，偏相关分析可以更好地表示数据。采用这两种工具可以提供真实的数据的更完整和互补的表示。在这种情况下，并与新的分析，我们认为，蛋白质的长度和基因的可分配性在酵母蛋白质的进化中发挥重要的，独立的作用。
The underlying relationship between functional variables and sequence evolutionary rates is often assessed by partial correlation analysis. However, this strategy is impeded by the difficulty of conducting meaningful statistical analysis using noisy biological data. A recent study suggested that the partial correlation analysis is misleading when data is noisy and that the principal component regression analysis is a better tool to analyze biological data. In this paper, we evaluate how these two statistical tools (partial correlation and principal component regression) perform when data are noisy. Contrary to the earlier conclusion, we found that these two tools perform comparably in most cases. Furthermore, when there is more than one 'true' independent variable, partial correlation analysis delivers a better representation of the data. Employing both tools may provide a more complete and complementary representation of the real data. In this light, and with new analyses, we suggest that protein length and gene dispensability play significant, independent roles in yeast protein evolution.