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A combined statistical model for multiple motifs search

文献类型： 外文期刊

作者： Gao Li-Feng 2 ; Liu Xin 3 ; Guan Shan 1 ;

作者机构： 1.Yangzhou Univ, Phys Sci & Technol Dept, Yangzhou 225009, Peoples R China

2.Chinese Acad Agr Sci, Beijing 100081, Peoples R China

3.Inst Theoret Phys, Beijing 100080, Peoples R China

关键词： transcription factor binding sites;motif;position weight matrix

期刊名称：CHINESE PHYSICS B （ 影响因子：1.494； 五年影响因子：1.262 ）

ISSN： 1674-1056

年卷期： 2008 年 17 卷 12 期

页码：

收录情况： SCI

摘要： Transcription factor binding sites (TFBS) play key roles in genebior 6.8 wavelet expression and regulation. They are short sequence segments with definite structure and can be recognized by the corresponding transcription factors correctly. From the viewpoint of statistics, the candidates of TFBS should be quite different from the segments that are randomly combined together by nucleotide. This paper proposes a combined statistical model for finding over-represented short sequence segments in different kinds of data set. While the over-represented short sequence segment is described by position weight matrix, the nucleotide distribution at most sites of the segment should be far from the background nucleotide distribution. The central idea of this approach is to search for such kind of signals. This algorithm is tested on 3 data sets, including binding sites data set of cyclic AMP receptor protein in E. coli, PlantProm DB which is a non-redundant collection of proximal promoter sequences from different species, collection of the intergenic sequences of the whole genome of E. Coli. Even though the complexity of these three data sets is quite different, the results show that this model is rather general and sensible.

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