Objective method for estimating asymptotic parameters, with an application to sequence alignment.
Objective method for estimating asymptotic parameters, with an application to sequence alignment.
复制标题
估计渐近参数的客观方法，并应用于序列比对。
DOI：
10.1103/physreve.84.031914
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发表时间：
2011-09
期刊：
影响因子：
--
通讯作者：
Spouge JL
中科院分区：
文献类型：
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作者：
Sheetlin S;Park Y;Spouge JL
序列比对是现代分子生物学中不可缺少的计算工具。生物学序列比对的基础模型引起了物理学家的兴趣，因为它近似于DNA和蛋白质退火的统计力学，同时与随机介质中的定向聚合物模型有着密切的关系。最近确定随机序列比对统计量的方法已经将计算时间减少到小于15秒，为生物搜索引擎的在线计算开辟了一些有趣的可能性。然而，在实施之前，这些方法需要一种客观的技术来计算与渐近状态相关的回归系数。通常，物理学家主观地估计与渐近状态相关的参数：他们观察他们的数据；以合理的精度估计回归模型所处的渐近区域；然后只在估计的渐近区域内回归数据。我们公开的计算机程序ARRP用客观的变化点检测方法取代了对渐近状态的主观评估，增加了对参数估计的科学客观性的信心。渐近回归在大多数物理学领域都有潜在的应用。
Sequence alignment is an indispensable computational tool in modern molecular biology. The model underlying biological sequence alignment is of interest to physicists because it approximates the statistical mechanics of DNA and protein annealing, while bearing an intimate relationship to models of directed polymers in random media. Recent methods for determining the statistics of random sequence alignments have reduced the computation time to less than 1 s, opening up some interesting possibilities for online computation with biological search engines. Before implementation, however, the methods required an objective technique for computing regression coefficients pertinent to an asymptotic regime. Typically, physicists estimate parameters pertinent to an asymptotic regime subjectively: They eyeball their data; estimate the asymptotic regime where the regression model holds with reasonable accuracy; and then regress data only within the estimated asymptotic regime. Our publicly available computer program ARRP replaces the subjective assessment of the asymptotic regime with an objective change-point detection method, increasing confidence in the scientific objectivity of the parameter estimates. Asymptotic regression has potential applications across most of physics.