Methods and statistics for combining motif match scores
Methods and statistics for combining motif match scores
复制标题
DOI：
10.1089/cmb.1998.5.211
复制
发表时间：
1998-06-01
影响因子：
1.7
通讯作者：
Gribskov, M
中科院分区：
文献类型：
--
作者：
Bailey, TL;Gribskov, M
特定位置的评分矩阵对于蛋白质序列蛾的表示和搜索是有用的，一个序列家族通常可以用一组一个或多个基序来描述，一个有效的搜索必须将一个序列与组中每个基序的匹配分数结合起来，我们描述了三种组合匹配分数和估计组合分数的统计意义的方法，并对每种方法的搜索质量(分类精度)和统计意义估计的准确性进行了评估。这三种方法是：1)分数总和，2)约简变量总和，3)分数p值乘积，我们证明了方法3)在这两个方面都优于其他两种方法，并且结合Motif分数确实提供了更好的搜索精度。利用p值计分方法的乘积的主序列同源性搜索算法可在URL http：//www上交互使用和下载。Sdsc.edu/表情包。
Position-specific scoring matrices are useful for representing and searching for protein sequence moths, A sequence family can often be described by a group of one or more motifs, and an effective search must combine the scores for matching a sequence to each of the motifs in the group, We describe three methods for combining match scores and estimating the statistical significance of the combined scores and evaluate the search quality (classification accuracy) and the accuracy of the estimate of statistical significance of each. The three methods are: 1) sum of scores, 2) sum of reduced variates, 3) product of score p-values, We show that method 3) is superior to the other two methods in both regards, and that combining motif scores indeed gives better search accuracy. The MAST sequence homology search algorithm utilizing the product of p-values scoring method is available for interactive use and downloading at URL http: / /www. sdsc.edu/MEME.