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Open Access Methodology article

A mutation degree model for the identification of transcriptional regulatory elements

Changqing Zhang124, Jin Wang123, Xu Hua1, Jinggui Fang5, Huaiqiu Zhu3* and Xiang Gao2*

Author Affiliations

1 State Key Laboratory of Pharmaceutical Biotechnology, School of Life Science, Nanjing University, Nanjing 210093, China

2 Model Animal Research Center, Nanjing University, Nanjing 210093, China

3 Department of Biomedical Engineering, and Center for Theoretical Biology, Peking University, Beijing 100871, China

4 College of Horticulture, Jinling Institute of Technology, Nanjing 210038, China

5 College of Horticulture, Nanjing agricultural university, Nanjing 210095, China

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BMC Bioinformatics 2011, 12:262  doi:10.1186/1471-2105-12-262

Published: 27 June 2011



Current approaches for identifying transcriptional regulatory elements are mainly via the combination of two properties, the evolutionary conservation and the overrepresentation of functional elements in the promoters of co-regulated genes. Despite the development of many motif detection algorithms, the discovery of conserved motifs in a wide range of phylogenetically related promoters is still a challenge, especially for the short motifs embedded in distantly related gene promoters or very closely related promoters, or in the situation that there are not enough orthologous genes available.


A mutation degree model is proposed and a new word counting method is developed for the identification of transcriptional regulatory elements from a set of co-expressed genes. The new method comprises two parts: 1) identifying overrepresented oligo-nucleotides in promoters of co-expressed genes, 2) estimating the conservation of the oligo-nucleotides in promoters of phylogenetically related genes by the mutation degree model. Compared with the performance of other algorithms, our method shows the advantages of low false positive rate and higher specificity, especially the robustness to noisy data. Applying the method to co-expressed gene sets from Arabidopsis, most of known cis-elements were successfully detected. The tool and example are available at webcite.


The mutation degree model proposed in this paper is adapted to phylogenetic data of different qualities, and to a wide range of evolutionary distances. The new word-counting method based on this model has the advantage of better performance in detecting short sequence of cis-elements from co-expressed genes of eukaryotes and is robust to less complete phylogenetic data.