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.
Peng
Chien-Hua
CH
Department of Computer Science, Institute of Molecular and Cellular Biology, National Tsing Hua University, Taiwan 300, Republic of China.
Hsu
Jeh-Ting
JT
Chung
Yun-Sheng
YS
Lin
Yen-Jen
YJ
Chow
Wei-Yuan
WY
Hsu
D Frank
DF
Tang
Chuan Yi
CY
D000465:Algorithms; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods; D006801:Humans; D008099:Liver / Q000378:metabolism; D011401:Promoter Regions, Genetic; D018085:Regulon; D012441:Saccharomyces cerevisiae / Q000235:genetics; D017422:Sequence Analysis, DNA / Q000379:methods*; D014157:Transcription Factors / Q000378:metabolism
D014157:Transcription Factors