DETECTING SUBTLE SEQUENCE SIGNALS - A GIBBS SAMPLING STRATEGY FOR MULTIPLE ALIGNMENT
DETECTING SUBTLE SEQUENCE SIGNALS - A GIBBS SAMPLING STRATEGY FOR MULTIPLE ALIGNMENT
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DOI：
10.1126/science.8211139
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发表时间：
1993-10-08
期刊：
影响因子：
56.9
通讯作者：
WOOTTON, JC
中科院分区：
文献类型：
--
作者：
LAWRENCE, CE;ALTSCHUL, SF;WOOTTON, JC
基因组计划和其他测序工作正在产生大量的蛋白质和DNA序列数据。解读这些序列以及理解它们之间的关系的一个关键障碍是难以检测多个序列共有的细微局部残基模式。这种模式常常反映相似的分子结构和生物学特性。适用于完全计算机自动化的“局部多重比对”问题的一个数学定义已被用于开发一种基于迭代抽样统计方法的新型灵敏算法。该算法能在N线性时间内为N个序列找到一个优化的局部比对模型，在当前的工作站上只需几秒，并允许同时检测和优化多种模式以及模式重复。该方法在应用于螺旋 - 转角 - 螺旋蛋白质、脂质运载蛋白和异戊烯基转移酶时得到了阐释。
A wealth of protein and DNA sequence data is being generated by genome projects and other sequencing efforts. A crucial barrier to deciphering these sequences and understanding the relations among them is the difficulty of detecting subtle local residue patterns common to multiple sequences. Such patterns frequently reflect similar molecular structures and biological properties. A mathematical definition of this ''local multiple alignment'' problem suitable for full computer automation has been used to develop a new and sensitive algorithm, based on the statistical method of iterative sampling. This algorithm finds an optimized local alignment model for N sequences in N-linear time, requiring only seconds on current workstations, and allows the simultaneous detection and optimization of multiple patterns and pattern repeats. The method is illustrated as applied to helix-turn-helix proteins, lipocalins, and prenyltransferases.