Identification of degenerate motifs using position restricted selection and hybrid ranking combination.

The identification of regulatory elements recognized by transcription factors and chromatin remodeling factors is essential to studying the regulation of gene expression. When no auxiliary data, such as orthologous sequences or expression profiles, are used, the accuracy of most tools for motif discovery is strongly influenced by the motif degeneracy and the lengths of sequence. Since suitable auxiliary data may not always be available, more work must be conducted to enhance tool performance to identify transcription elements in the metazoan. A non-alignment-based algorithm, MotifSeeker, is proposed to enhance the accuracy of discovering degenerate motifs. MotifSeeker utilizes the property that variable sites of transcription elements are usually position-specific to reduce exposure to noise. Consequently, the efficiency and accuracy of motif identification are improved. Using data fusion, the ranking process integrates two measures of motif significance, resulting in a more robust significance measure. Testing results for the synthetic data reveal that the accuracy of MotifSeeker is less sensitive to the motif degeneracy and the length of input sequences. Furthermore, MotifSeeker has been tested on a well-known benchmark [M. Tompa, N. Li, T.L. Bailey, G.M. Church, B. De Moor, E. Eskin, A.V. Favorov, M.C. Frith, Y. Fu, W.J. Kent, et al. (2005) Nat. Biotechnol., 23, 137-144], yielding a correlation coefficient of 0.262, which compares favorably with those of other tools. The high applicability of MotifSeeker to biological data is further demonstrated experimentally on regulons of Saccharomyces cerevisiae and liver-specific genes with experimentally verified regulatory elements.