Statistics Of Sequence Comparison

序列比较统计

• 批准号: 6681316
• 负责人: STEPHEN F ALTSCHUL
• 金额: --
• 依托单位: NATIONAL LIBRARY OF MEDICINE
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/6681316
• 关键词: computer assisted sequence analysis; nucleic acid sequence; protein sequence; statistics /biometry

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) Investigation of the statistics of a block-based scoring system - within certain parameter ranges, the distribution of optimal block-based scores was found to be reasonably well modelled by an extreme value distribution. Whether such scores are more sensitive in recognizing distant biological relationships than protein "profiles" or position-specific score matrices remains to be determined; b) Initial investigation of the statistics of the "hybrid" local alignment scoring system - this method was found to produce scores that follow an extreme value distribution with predictible scale parameter lambda.

这个项目是一个持续的研究问题，关于当两个蛋白质或DNA序列比较时，什么相似性可以期望纯粹偶然地发生。一个附属的和相关的问题涉及到评分系统的定义，这个评分系统是区分生物学上有意义的模式和偶然的相似性的最佳选择。今年的工作包括：a)研究基于分块的评分系统的统计数据——在一定的参数范围内，发现基于分块的最优分数的分布可以很好地用极值分布来建模。在识别远距离生物学关系方面，这种评分是否比蛋白质“谱”或位置特异性评分矩阵更敏感仍有待确定；b)对“混合”局部对齐评分系统的统计数据进行初步调查-发现该方法产生的分数遵循可预测尺度参数lambda的极值分布。