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Chin. Phys. B, 2008, Vol. 17(12): 4396-4400    DOI: 10.1088/1674-1056/17/12/011
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A combined statistical model for multiple motifs search

Gao Li-Fenga, Liu Xinb, Guan Shanc
a Chinese Academy of Agriculture Science, Beijing 100081, China; b Institute of Theoretical Physics, Beijing 100080, China; c Physics science and technology Department, Yangzhou University, Yangzhou 225009, China
Abstract  Transcription factor binding sites (TFBS) play key roles in gene's 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.
Keywords:  transcription factor binding sites      motif      position weight matrix     
Received:  03 January 2008      Published:  20 December 2008
PACS:  87.16.Yc (Regulatory genetic and chemical networks)  
  87.14.E- (Proteins)  
  87.15.A- (Theory, modeling, and computer simulation)  
  87.15.B- (Structure of biomolecules)  
  87.15.Cc (Folding: thermodynamics, statistical mechanics, models, and pathways)  
Fund: Project supported by the National Science Foundation of China (Grant No 70671089), and the Key Important Project (No 10635040).

Cite this article: 

Gao Li-Feng, Liu Xin, Guan Shan A combined statistical model for multiple motifs search 2008 Chin. Phys. B 17 4396

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