STATISTICS OF SEQUENCE COMPARISON

这个项目是一个持续的研究问题，当两个蛋白质或DNA序列进行比较时，什么样的相似性可以预期纯粹是偶然发生的。 一个附属的和相关的问题涉及的评分系统，是最佳的区分生物学意义的模式从机会相似性的定义。今年的工作包括：a）在BLAST和PSI-BLAST数据库检索程序的背景下，实施更准确的方法来评估统计学显著性。在BLAST程序家族中E值的分配依赖于数据库序列的标准组合物的使用。 这可能导致涉及具有类似偏向组成的序列的比对接收不适当的低E值。 一种新的方法重新估计了每对序列的相关统计参数，这些参数产生了看似重要的比对。 新参数导致统计显著性的修订估计。 这可能对PSI-BLAST的输出产生重大影响，其中在一次迭代期间包含假阳性可能会破坏所有进一步的结果。新方法已经实施并测试了BLAST和PSI-BLAST，现在可以在NCBI网站上找到。假阳性结果的数量明显减少。B）用于提取局部比对得分的统计参数的最大似然估计的快速且准确的方法的实现。基于Waterman和Vingron提出的思想，并由Olsen，Bundschuh和Hwa进一步发展，我们已经开发了一种新的岛方法来估计局部比对分数分布的统计参数。与以前最常用的直接方法相比，新方法具有以下几个优点：i）它明确了参数估计偏差和随机误差之间的权衡，并允许这种权衡易于控制;ii）它允许获得任意长度序列比较的参数估计，包括无限长度限制;iii）它准确估计小长度比较的分数分布的尾部行为。

This project is a continuing study of questions concerning what similarities can be expected to occur purely by chance when two protein or DNA sequences are compared. A subsidiary and related question concerns the definition of scoring systems that are optimal for distinguishing biologically meaningful patterns from chance similarities. Work this year includes: a) The implementation of a more accurate method to assess statistical significance in the context of the BLAST and PSI-BLAST database search programs. The assignment of E-values in the BLAST family of programs has depended upon the use of a standard composition for database sequences. This can result in alignments involving sequences with similarly biased compositions receiving inappropriately low E-values. A new approach re-estimates the relevant statistical parameters for each pair of sequences that yield a seemingly significant alignment. The new parameters lead to a revised estimate of statistical significance. This can have a major effect on the output of PSI-BLAST, where the inclusion of a false positive during one iteration can corrupt all further results. The new approach has been implemented and tested for both BLAST and PSI-BLAST, and is now available on the NCBI web site. A substantial decrease in the number of false positive results is apparent. b) The implementation of a fast and accurate method for extracting maximum-likelihood estimates of statistical parameters for local alignment scores. Based upon ideas introduced by Waterman & Vingron, and further developed by Olsen, Bundschuh & Hwa, we have developed a new island method for estimating statistical parameters for local alignment score distributions has been described and implemented. In contrast to the direct method previously in most common use, the new method has several advantages:i) It renders explicit the tradeoff between parameter estimate bias and stochastic error, and allows this tradeoff to be easily controlled;ii) It allows parameter estimates to be obtained for arbitrary length sequence comparisons, including the infinite-length limit;iii) It estimates accurately the tail behavior of score distributions for small-length comparisons.